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Computer models in bedside physiology

Zhang, Y.

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COMPUTER MODELS IN BEDSIDE PHYSIOLOGY

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

op gezag van de Rector Magnificus

prof. dr. D.C. van den Boom

ten overstaan van een door het college voor promoties ingestelde

commissie, in het openbaar te verdedigen in de Agnietenkapel

op vrijdag 20 september 2013, te 12:00 uur

door

Yanru Zhang

geboren te Nei Mongol, China

Promotor: Prof.dr. J.H.R. Ravesloot

Copromotor: Dr. J.M. Karemaker

Overige leden: Prof.dr. E.T. van Bavel

Prof.dr.ir. C. Ince

Dr. R.W. Koster

Prof.dr. J.J. van Lieshout

Prof.dr. R.J.G. Peters

Faculteit der Geneeskunde

This thesis has been prepared in the department of Anatomy, Embryology and Physiology

of the Academic Medical Center, Amsterdam, The Netherlands

3

Table of contents

Chapter 1 General Introduction; aims of the thesis ................................................ 5

Chapter 2 Optimal Cardiopulmonary Resuscitation as Identified by Computer

Modeling ............................................................................................ 11

Chapter 3 Abdominal counter pressure in CPR: What about the lungs? An in

silico study .......................................................................................... 33

Chapter 4 Search for HRV‐parameters that detect a sympathetic shift in heart

failure patients on ß‐blocker treatment .............................................. 51

Chapter 5 A subgroup of Brugada patients shows low orthostatic blood pressures

as a sign of decreased sympathetic outflow ........................................ 75

Chapter 6 Dynamics of Pulse Pressure Variability and the Difficulty of Predicting

Fluid Responsiveness. ......................................................................... 91

Chapter 7 Summary and general conclusions ..................................................... 121

Nederlandse samenvatting ...............................................................125

Curriculum Vitae .................................................................................................. 127

Acknowledgements and colophon ........................................................................ 128

Chapter1 GeneralIntroduction;aimsofthethesis

I have been trained in Biomedical Engineering (BME), a field that is, broadly speaking, geared

to putting engineering methods to the assistance of clinical diagnosis and therapy. Biomedical

engineering methods cover a wide range – from chemical engineering (tissue engineering and

biomaterials) to electrical and mechanical engineering, as in the development of medical

devices (12). In this thesis I apply bioelectrical (BME‐) methods to solve several medical

problems.

The thesis consists of two parts: the first part is about cardiovascular modeling and

simulation; the second about analysis of beat‐to‐beat heart rate and blood pressure

variability.

1. Modelingandsimulationofthehumancardiovascularsystem

Models of the circulation are constructed to help understand physiological problems or to

simulate interventions which are difficult or impossible to perform in real life. In this thesis

we have reduced a number of aspects of the human cardiovascular model to a computer

program. A good model can accurately describe (some of) the behavior of a particular system;

use of the computer model may reduce experiment time and cost, avoid unnecessary injuries

and ethical controversies.

Since the first two‐element Windkessel (arteries) model was developed in 1899 by Otto Frank

(11), until the overall circulation regulation model developed in 1972 by A. C. Guyton(5), the

cardiovascular models went from very simple to very complex. Today circulation models are

developed simple or complex according to the research requirements. In the following two

sections the medical background of our specific cardiovascular model is introduced. Modeling

or simulation can never replace real experiments, but it can always make real experiments

better, or smarter.

a. CardiopulmonaryResuscitationsimulation

Cardiopulmonary arrest (CPA, also called cardiac arrest, CA) is a condition in which the heart

suddenly and unexpectedly stops beating. Data from the Sudden Cardiac Arrest Association

(www.suddencardiacarrest.org) show that in the U.S.A more than 300,000 people suffer a

CPA each year, only 8% of whom survive. When CPA occurs, cardiopulmonary resuscitation

(CPR) is an emergency procedure to preserve brain function (and that of other vital organs)

until further measures are taken to restore spontaneous circulation and breathing. No matter

Chapter 1

6

where CPA occurs: on the street or in a hospital, in‐time and proper CPR and advanced life

support can dramatically improve the survival rate to 50%! The International Liaison

Committee on Resuscitation (www.ilcor.org ) holds consensus meetings and publishes

updated CPR guidelines every five years since the first in 2000. There are also free training

courses (in hospitals, on websites, and in TV programs).

In Chapter 2 the question to be answered is: which CPR technique is the best one to improve

cardiac output and organ perfusion? We compared five different CPR techniques, from

conventional to innovative methods, by way of a cardiovascular circulation model.

In Chapter 3 the question to be answered is: if we apply all those efforts described in chapter

2 to improve cardiac output and organ perfusion, is that really the best we can do for the

patient? When we pushed this CPR optimization we found that with high pressures of thorax

and abdomen compression and large venous returns, we had to consider the lungs as well.

With improved mechanical techniques, ‘faster and harder’ compressions are not difficult to

obtain (in‐hospital that is, with bare hands it is still difficult); the question is: can the lungs

take it?

b. PulsePressureVariationsimulation

Cardio‐pulmonary interaction is in the spotlight again. Over the last 20 years pulse pressure

variation (PPV) has proven itself as an accurate predictor of volume responsiveness (1, 2, 4, 7,

9, 10): lower PPV implies that the volume status has pushed cardiac filling to the plateau of

the Frank‐Starling curve to a saturation state(9). PPV is in clinical use to steer volume infusion

for instance during surgery. However PPV does not work in situations like low tidal volume or

spontaneous respiration(9). The question when PPV may reliably be used as indicator of

volume responsive is still in discussion.

In Chapter 6 we use a cardiovascular circulation model, with respiration and ANS control to

simulate how PPV changes with changes in circulating volume, whether PPV can predict

volume responsiveness in different situations. We compare these results to recordings in

healthy test subjects who received a large intravenous saline infusion.

2. Analysisofbeat‐to‐beatheartrateandbloodpressurevariability

Before going into heart rate variability (HRV) and blood pressure variability (BPV), we have to

talk about the autonomic nervous system (ANS) first. The autonomic nervous system is

predominantly an efferent system transmitting impulses from the Central Nervous System

(CNS) to peripheral organ systems. Its effects include control of heart rate and force of

Introduction; aims of the thesis

7

contraction, constriction and dilatation of blood vessels, contraction and relaxation of

smooth muscle in various organs, visual accommodation, pupillary size and secretions from

exocrine and endocrine glands.

The ANS consists of two separate divisions: the parasympathetic and sympathetic systems,

distinguished on the basis of anatomical and functional differences. The sympathetic nervous

system aids in the control of most of the body's internal organs. Stress—as in the

flight‐or‐fight response—is thought to counteract the parasympathetic system, which

generally works to promote maintenance of the body at rest.

Disturbances of the autonomic nervous system can be the cause of serious health problems.

Autonomic nervous system disorders can occur alone or as the result of another disease,

such as Parkinson's disease, alcoholism and diabetes. Therefore evaluation of the condition

of the autonomic nervous system can be of diagnostic or predictive value.

a. Heartratevariability(HRV)inCHFpatients

Heart rate variability (HRV) has emerged as a simple, noninvasive method to evaluate ANS

activity. Reduced heart rate variability (HRV) is a powerful and independent predictor of an

adverse prognosis in patients with heart disease and in the general population.

In Chapter 4 we test which analysis method for HRV discriminates Chronic Heart Failure (CHF)

patients on beta‐blocker treatment from healthy control subjects; next we test which of

these parameter(s) detects the situation where such a patient would ‘slip into’ a more

sympathetic condition.

b. Challenge:ActivestandingupinBrugadapatients

When we stand up blood tends to shift towards the lower part of the body, sympathetic

activity will raise heart rate, peripheral resistance, cardiac performance etc. to maintain

blood pressure, at the same time parasympathetic activity will withdraw.

Active standing‐up from supine or sitting is clinically used to test ANS function in control of

blood pressure and heart rate (3, 6, 8, 13).

In Chapter 5 we compare the change of beat‐to‐beat parameters like heart rate, blood

pressure from supine to upright position in healthy control subjects and Brugada patients.

The Brugada syndrome (BrS) is a genetic disease that is characterized by abnormal

ECG‐findings and an increased risk of sudden cardiac death. The stand test is used to test the

Chapter 1

8

function of the ANS in Brugada patients and, ultimately, to find predictors for the risk of

sudden cardiac death.

3. Chapteroverview

In all there are seven chapters, as follows:

1. General introduction and aims of the thesis;

2. Optimal cardiopulmonary resuscitation as tested by computer modeling;

3. Abdominal counter pressure in CPR: What about the lungs? An in silico study;

4. Search for HRV‐parameters that detect a sympathetic shift in heart failure patients on

β‐blocker treatment

5. A subgroup of Brugada patients shows low orthostatic blood pressures as a sign of

decreased sympathetic outflow

6. Dynamics of pulse pressure variability and the difficulty of predicting fluid

responsiveness;

7. Summary and general conclusions

See the flowchart in Figure 1.

References

1. Bendjelid K, Suter PM, and Romand JA. The respiratory change in preejection period: a

new method to predict fluid responsiveness. J Appl Physiol 96: 337‐342, 2004.

2. Coyle J, Teplick R, Long M, and Davison J. Respiratory variations in systemic arterial

pressure as an indicator of volume status. Anesthesiology 59: A53, 1983.

Introduction; aims of the thesis

9

3. Den Heijer JC, Bollen WL, Reulen JP, van Dijk JG, Kramer CG, Roos RA, and Buruma OJ.

Autonomic nervous function in Huntington's disease. Arch Neurol 45: 309, 1988.

4. Freitas F, Bafi A, Nascente A, Assunção M, Mazza B, Azevedo L, and Machado F. Predictive

value of pulse pressure variation for fluid responsiveness in septic patients using

lung‐protective ventilation strategies. Br J Anaesth 110: 402‐408, 2013.

5. Guyton AC, Coleman TG, and Granger HJ. Circulation: overall regulation. Annu Rev Physiol

34: 13‐46, 1972.

6. Hägglund H, Uusitalo A, Peltonen JE, Koponen AS, Aho J, Tiinanen S, Seppänen T, Tulppo

M, and Tikkanen HO. Cardiovascular autonomic nervous system function and aerobic

capacity in type 1 diabetes. Front Physiol 3, 2012.

7. Kramer A, Zygun D, Hawes H, Easton P, and Ferland A. Pulse pressure variation predicts

fluid responsiveness following coronary artery bypass surgery. Chest 126: 1563‐1568,

2004.

8. Martinmäki K, Rusko H, Saalasti S, and Kettunen J. Ability of short‐time Fourier transform

method to detect transient changes in vagal effects on hearts: a pharmacological

blocking study. Am J Physiol‐Heart C 290: H2582‐H2589, 2006.

9. Michard F. Changes in arterial pressure during mechanical ventilation. Anesthesiology

103: 419‐428, 2005.

10. Michard F, Boussat S, Chemlad D, Anguel N, Mercat A, Lecarpentier Y, Richard C, Pinsky

MR, and Teboul J‐L. Relation between respiratory changes in arterial pulse pressure and

fluid responsiveness in septic patients with acute circulatory failure. Am J Resp Crit Care

162: 134‐138, 2000.

11. Sagawa K, Lie RK, and Schaefer J. Translation of Otto Frank's paper "Die Grundform des

Arteriellen Pulses" Zeitschrift fur Biologie 37: 483‐526 (1899). J Mol cell cardiol 22:

253‐254, 1990.

12. Wikipedia. Biomedical engineering. Last updated :2013 31 May, [cited 2013 15 June];

Available from: http://en.wikipedia.org/wiki/Biomedical_engineering.

13. Ziemssen T and Reichmann H. Cardiovascular autonomic dysfunction in Parkinson's

disease. J Neurol Sci 289: 74‐80, 2010.

11

Chapter2 OptimalCardiopulmonaryResuscitationasIdentifiedbyComputerModeling

Yanru Zhang, Xiaoming Wu, Hengxin Yuan, Lin Xu and John M.Karemaker

Most of the work in this chapter has been the subject of 2 congress papers (2009 and 2010) on

one of which Ms. Zhang was co‐author; moreover a full paper was published:

Xu, L, Wu, X, Zhang, Y and Yuan, H.: The Optimization Study on Time Sequence of Enhanced

External Counter‐Pulsation in AEI‐CPR. J. Computers 4; 1243‐1248 (2009)

In view of this publication history and the fact that chapter 3 originated from the study reported

here (as a warning not to ‘overdo’ the combination of abdominal and chest compression) an

extensive discussion of the findings has been deferred to chapter 3.

The appendix of chapter 2 (modeling details) applies to both the chapters 2 and 3.

Chapter 2

12

Abstract

Objective and Design: We aimed to find the optimal mode of cardiopulmonary resuscitation

(CPR) in a computer model of the circulation. At least four variants of the simple compression

of the heart between sternum and spine exist. For several reasons the efficacy of these

modes is difficult to compare in clinical trials. In order to identify the optimal mode we

conducted in silico experiments.

Setting and Interventions: A Matlab® computer model of the circulation was developed. At

simulated cardiac arrest the model was subjected to the various modeled CPR‐variants. The

effects on cardiac output and perfusion of critical organs were compared.

Measurements and Main Results: We compared Chest Only CPR (CO‐CPR), Active

Compression‐Decompression CPR (ACD‐CPR), Interposed Abdominal Compression CPR

(IAC‐CPR), thoracic‐abdominal compression‐decompression CPR (Lifestick‐CPR) and chest

compression assisted with Enhanced External Counterpulsation on the legs and abdomen

(EECP‐CPR). They were combined with the action of an Impedance Threshold Valve (ITV) to

support venous return in the chest decompression phase. The three techniques that involve

abdominal compression were at least twice as effective as the two that only apply force to

the chest, independent of which parameter was used to quantify the result. Compression of

the abdominal veins played a more important role than arterial compression in generating

cardiac output and organ perfusion.

Conclusions: In this in silico study Lifestick‐CPR was the most effective of the CPR techniques

tested.

Optimal cardiopulmonary resuscitation

13

Introduction

Sudden cardiac arrest is the most critical manifestation of cardiac disease, requiring

immediate action to save the patient’s life. Outside a hospital this will mostly be done by

cardiopulmonary resuscitation (CPR). This aims to keep the perfusion of heart, brain and

lungs going while the heart is in arrest. Since the introduction of closed‐chest CPR several

improvements to the classical, or Chest Only CPR (CO‐CPR) have been proposed (5, 11, 12,

19). These have in common that they either require the availability of equipment, or the

presence of a second person assisting the one that delivers CPR. Therefore, the application of

improved CPR is normally limited to a medical setting such as an ambulance or emergency

room.

The first variant is the combination of chest compression with Interposed Abdominal

Compression (IAC‐CPR) (11). This involves the application of abdominal pressure during the

relaxation phase of chest compression in otherwise classical CO‐CPR. This requires the

presence of a second rescuer or a dedicated machine. In a second variant, chest compression

is combined with active thorax expansion by the Active Compression‐Decompression CPR

(ACD‐CPR) (5). This variant uses a suction device to actively compress and decompress the

chest. A third variant involves machine‐driven, phased thoracic‐abdominal

compression–decompression CPR (Lifestick‐CPR) (17). Lifestick‐CPR combines IAC and

ACD‐CPR by alternately compressing and decompressing the chest and the abdomen through

adhesive pads. And finally, a G‐suit like device has been designed to ‘milk’ the venous blood

by inflatable cuffs wrapped around legs and buttocks. This variant is knows as Enhanced

External Counterpulsation (EECP‐CPR) (8, 21).

An inspiratory impedance threshold valve (ITV) has been proven as a beneficial adjunct (15).

The valve closes the airways while the chest is being decompressed, with the objective to

improve venous return due to the enhanced subatmospheric intrathoracic pressure during

the recoil phase of chest compression. In our modeling ITV is combined with the

aforementioned five CPR variants (Fig. 1) mentioned.

At present there is no decisive evidence which one of the alternative CPR‐techniques is

superior to Chest Only CPR (10, 20). In 2010 International Consensus on Cardiopulmonary

Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations

emphasizes CO‐CPR as the only accepted resuscitation method (7). Clinical evidence is lacking

Chapter 2

14

since for obvious reasons, it is not feasible to compare the various CPR techniques under the

same condition. We therefore turned to computer models of the circulation.

Otto Frank’s computational model of cardiac function in conjunction to the Windkessel

concept conceived in the late 19th century (13), paved the way for the current era. In our

time, with ever increasing calculation speed, computer modeling of circulation has shaped a

new avenue of research to investigate medical problems (6, 16). A computer model may

avoid the major disadvantages of animal and clinical experiments such as ethical issues, high

costs, complexity, influence of external factors and operator‐dependent effects. Furthermore,

it overcomes time and space limitations. We argue that to conquer the difficulties inherent to

finding ‘evidence‐based’ best practice in an emergency situation like CPR, computer modeling

is an obvious choice. This approach can quantify and evaluate the effects of the different

components of the various CPR‐techniques to help decide which ones are effective and which

are of little consequence.

In this paper a circulatory model has been used as an experimental platform to test the five

CPR techniques. In silico experiments do not put a critically ill patient at risk, and allow

comparison of the various CPR techniques under highly‐defined conditions. We extended the

model developed by Charles F. Babbs (1‐3) to simulate CO‐CPR, ACD‐CPR, IAC‐CPR, Lifestick

CPR and EECP‐CPR. In all experiments we assumed that an ITV was in place. We focused on

their hemodynamic effects to identify the optimal CPR technique. Here we report that

Lifestick‐CPR is the most effective of the five CPR techniques.


15

Figure 1 Schematics of the five CPR techniques. See text for details.

Chapter 2

16

Methods

Modeldescription

The model we adopted for this study is largely based on the work of Babbs et al. (1‐3). The

model is an electrical equivalent circuit and has sufficient details to allow simulation of the

five CPR techniques except for the initial blood volumes immediately after the cardiac arrest

(CA). These blood volumes were estimated by us (Table 1).

The model has 14 compartments, including four heart chambers, the pulmonary circulation,

the thoracic aorta feeding an upper body and an abdominal compartment (Fig. 2). The

structure of each lumped artery and vein compartment is the same, including a resistance

followed by a capacitance (compliance). The peripheral circulation for each compartment is a

simple resistance. Each ventricle has two valves. Each vein outside the trunk has one valve to

ensure unidirectional blood flow. In view of the low blood velocity in CPR, inertial effects

were neglected. The initial pressure of all systemic compartments in simulated CA is set to

5 mmHg, and to 8mmHg in the pulmonary part. Finally, it was assumed that coronary

perfusion stopped during the compression phase.

Figure 2 Top: structure of the circulatory model. Details of chest pump: next page


17

Figure 2. Bottom (detail): model of the chest pump including heart and pulmonary circulation.

Relevant parameter values of the model are listed in the appendix. To help understand Fig. 2

a list of symbols and subscripts is given in Table 1

Table 1. Symbols and subscripts used in Fig. 2

Symbol Electrical Mechanical

C capacitor Compliance (i.e. compliant compartment)

i current (blood) flow

R resistance resistance to flow

Subscript Definition Subscript Definition

aa abdominal aorta la left atrium and central pulmonary

ao thoracic aorta lv left ventricle

c / car carotid arteries mv mitral valve

cppa central to peripheral pulm aa. pa pulmonary arteries

cppv peripheral to central pulm vv. pc pulmonary capillary

fa femoral arteries ppa peripheral pulmonary arteries

fv femoral veins ppv peripheral pulmonary veins

h head vasculature pv pulmonary valve

ht heart (coronary) ra right atrium

iv iliac veins rv right ventricle

ivc / v inferior vena cava s splanchnic

j / jug jugular (veins) tv tricuspid valve

Chapter 2

18

CPRdescription

CPR chest compression theoretically has two mechanically distinct effects on the central

circulation. In the thoracic pump mechanism compression of lungs and mediastinal organs,

including atria and great vessels (pulmonary trunk, ‐ arteries and aorta) causes blood to flow.

In the cardiac pump mechanism compression of the heart causes blood to flow. Which one of

the two mechanisms prevails remains controversial (18). In our model we left the controversy

alone. Like in Babb’s model (2) we defined a factor ftp (0 ≤ ftp ≤ 1) to allocate the relative

contribution of both mechanisms: ftp = 0 denotes pure cardiac pump mechanism, ftp = 1

denotes pure thoracic pump mechanism. Values between 0 and 1 denote a mixture of the

two mechanisms.

Every CPR technique involves chest compression. In our in silico experiments chest

compression is modeled as described earlier (2, 4). Briefly, the resistance of the chest to

external compression is represented by a simple spring and damper system defined by spring

constant k and damping µ. During chest compression the force applied, F(t), leads to

downward movement of both sternum and anterior chest wall over a distance x1 (cm). The

reactive force due to damping depends on the speed of movement (x´1). We can now

define F(t) as follows

'11)( xxktF (1)

During the decompression phase F(t) will, initially, expand the cardiac chambers by filling

with blood over a distance x2 (cm). The compression effects on the organs in the thorax can

be expressed by x1 and x2 directly. For example, when V is the blood volume in a particular

heart chamber and A its cross sectional area, then

AVx /2 (2)

The mechanical effects of chest compression are translated in equivalent electrical events

and applied to our model. The equations are presented in the appendix. For example, in

IAC‐CPR, Lifestick CPR when the abdomen is compressed, the pressure to the abdomen is

taken as a voltage source in series with the appropriate compliances (Civc and Caa in Fig.2). In

EECP‐CPR the lower body and abdomen are compressed ‘sequentially’.

ITV is combined with each CPR technique. When ambient pressure is higher than airway

pressure during the decompression phase, the ITV valve is closed or else it is open. The valve


19

allows positive pressure ventilation but prevents inspiration caused by subatmospheric

airway pressures.

Following the 2010 International Consensus on CPR and ECC Science with Treatment

Recommendations (14), the compression frequency was set to 100 times/min. As

compression/decompression waveforms we adopted sine waves or rather half‐sine waves as

suggested by Babbs (2005) and Noordergraaf et al. (2005) (2, 9); the pressure patterns for the

various CPR‐techniques are shown in Fig. 3A‐E.

The model was solved using MAT LAB/Simulink. The solver was ODE4. To make the model

reach steady state in a convenient time frame, step size was set at 0.0001s; simulation time

was 40 s. The model reached a steady state after 8 seconds.

Chapter 2

20

Figure 3 Pressure waveforms: A: CO‐CPR; chest compression only; B: ACD‐CPR; chest compression and

decompression combined; C: IAC‐CPR; chest compression and abdomen compression

combined (abdomen: red dashed line); D: Lifestick CPR; chest compression and decompression,

combined with abdomen compression and decompression. E: EECP‐CPR; chest compression

and decompression, combined with abdomen and lower body compression (green line). Peak

values for thorax (de‐)compression: +400 & ‐200 N; for abdomen (Pabd): +100 & ‐20 mmHg, for

lower body and legs (Plb): +200 mmHg.


21

Results

ComparisonofthefiveCPRtechniques

To compare the relative effects of CO‐CPR, ACD‐CPR, IAC‐CPR, Lifestick CPR and EECP‐CPR

(combined with ITV), we chose to present Cardiac Output (CO), carotid artery flow (Qcar) and

myocardial flow (Qheart). Qcar reflects global cerebral perfusion, while Qheart reflects

coronary perfusion. The results are summarized in Fig. 4.

Figure 4 Comparison of the five different CPR techniques: A: CO cardiac output (L/min). B: Qcar average

carotid blood flow and Qheart average coronary blood flow (ml/s). These three parameters

are considered the most important parameters for CPR. The effects of IAC‐CPR, LIFESTICK CPR

and EECP‐CPR give great improvement over CO‐CPR and ACD‐CPR

Compared to CO‐CPR and ACD‐CPR, the three techniques that involve abdominal and/or

lower body compression (IAC‐CPR, LIFESTICK CPR and EECP‐CPR) produce at least a two times

higher CO. The same holds for Qcar and Qheart which are at least doubled compared to

CO‐CPR and ACD‐CPR. Of the three superior variants Lifestick CPR has the best efficacy in

terms of cardiac output and cerebral perfusion. Finally, in our model, ACD‐CPR is only slightly

better than CO‐CPR.

The aforementioned experiments were conducted with ftp = 0.75. To test for the sensitivity

of our results for ftp we repeated the in silico experiments with ftp = 0, which represents the

Chapter 2

22

pure cardiac pump mechanism, and ftp = 1 which represents the thoracic pump mechanism.

The results are summarized in Fig. 5.

Figure 5 Comparison of CO between pure cardiac pump (Ft=0) and pure thoracic pump (Ft=1.

Compared to CO‐CPR, IAC‐CPR, LIFESTICK CPR and EECP‐CPR give a higher CO irrespective of

ftp. Of the three superior variants Lifestick CPR again has the best efficacy in terms of cardiac

output regardless of ftp. Furthermore, except for EECP‐CPR, CO is higher when a cardiac

pump mechanism is operative. Another striking result is the large difference between cardiac

pump mechanism CO and thoracic pump mechanism CO in CO‐CPR and ACD‐CPR but not the

other three.

Veins/Arteries‐onlycompressioninabdomenandlimbs

IAC‐CPR, LIFESTICK CPR and EECP‐CPR have abdominal compression during chest

decompression in common. Next, we aimed to analyze which aspect of this maneuver is more

important, compression of the abdominal veins or compression of the abdominal arteries.

Such an analysis, of course, is only possible in computer models of the circulation. Venous

compression pushes blood towards the heart, thus favoring a higher cardiac output.

Arterial compression raises the resistance of the vessels and tends to trap blood in the

proximal arteries. This mechanism resembles the action of a balloon pump. Arterial

compression potentially limits the arterial pressure fall upon chest decompression in the

phase of diastolic runoff.

To address this question we first determined CO, Qcar and Qheart when both the arteries

and veins were compressed (V + A)compr. Next we determined the same three parameters if

only the veins are compressed (Vonly) or if only the arteries are compressed (Aonly). Finally

we took the ratio of Vonly/(V + A)compr and that of Aonly/(V + A)compr. This procedure was

repeated for the three CPR variants with interposed abdominal and/or legs compression. The

results of these in silico experiments are summarized in Table 2.


23

Table 2. Ratios of veins‐ or arteries‐only compression to compression of both

IAC‐CPR LIFESTICK CPR EECP‐CPR

Veins‐ Arteries‐ Veins‐ Arteries‐ Veins‐ Arteries‐

CO (%) 94 52 93 43 89 46

Qcar (%) 91 45 94 41 92 41

Qheart (%) 71 86 76 67 57 93

Regardless of the CPR technique used, CO benefits most from venous compression. 89‐94%

of the CO is preserved when only the veins are compressed. In contrast, CO halves (43 –

52%) when only the arteries are compressed. Similar observations were made for Qcar.

Qheart, on the other hand, benefits most from arterial compression in IAC‐CPR and EECP‐CPR

but not so much in Lifestick CPR. This follows directly from the assumption of stopped

coronary flow during chest compression.

These results explain to why IAC‐CPR, Lifestick CPR and EECP‐CPR are superior to CO‐CPR with

regard to the resulting CO. The inferior vena cava (as simple model for the abdominal venous

pool) is the largest vein in the body and a large amount of blood can be stored here.

Compressing this vein restores a considerable venous return, thus favoring CO.

Discussion

In this paper CO‐CPR, ACD‐CPR, IAC‐CPR, Lifestick CPR and EECP‐CPR have been assessed

using a computer model of the circulation. We reasoned that this model offers a uniform and

unique platform to evaluate the different CPR techniques. We observed that abdominal and

lower body compression in counter phase with thorax compression lead to substantially

improved hemodynamic results and is therefore recommended. We concluded that Lifestick

CPR gave the best effects in cardiac output, blood flow to brain and heart, EECP‐CPR

increased mainly CO and Qcar. EECP‐CPR and Lifestick CPR produced better effects, but they

are mechanical CPR techniques, which require the appliance at hand and trained

professionals, which limits their use. IAC‐CPR can be performed just by hand, and the effects

are tightly following EECP‐CPR and Lifestick CPR. If cardiac arrest happens in a public space,

IAC‐CPR presumably is the best choice.

Our model is restricted to the hemodynamic effects of CPR. Ventilation effects and oxygen

saturation of the blood, important for survival, are not included. Another limitation is that the

Chapter 2

24

compression pressure is set to fixed values, according to the recommendations of the

Guidelines or clinical empirical setting. However, it is very likely that during actual

resuscitation the compression pressure vary. Finally, our model is based on a ‘standard’ 70‐kg

‘textbook’ human subject, this potentially limits generalizability of our results.

References

1. Babbs CF. CPR techniques that combine chest and abdominal compression and

decompression: hemodynamic insights from a spreadsheet model. Circulation 100:

2146‐2152, 1999.

2. Babbs CF. Effects of an impedance threshold valve upon hemodynamics in Standard CPR:

studies in a refined computational model. Resuscitation 66: 335‐345, 2005.

3. Babbs CF. Efficacy of interposed abdominal compression‐cardiopulmonary resuscitation

(CPR), active compression and decompression‐CPR, and Lifestick CPR: Basic physiology in

a spreadsheet model. Crit Care Med 28, 2000.

4. Babbs CF, Weaver JC, Ralston SH, and Geddes LA. Cardiac, thoracic, and abdominal pump

mechanisms in cardiopulmonary resuscitation: studies in an electrical model of the

circulation. Am J Emerg Med 2: 299‐308, 1984.

5. Cohen TJ, Tucker KJ, Lurie KG, Redberg RF, Dutton JP, Dwyer KA, Schwab TM, Chin MC,

Gelb AM, Scheinman MM, and . Active compression‐decompression. A new method of

cardiopulmonary resuscitation. Cardiopulmonary Resuscitation Working Group. JAMA

267: 2916‐2923, 1992.

6. Guyton AC, Coleman TG, and Granger HJ. Circulation: overall regulation. Annu Rev Physiol

34:13‐46.: 13‐46, 1972.

7. Hazinski MF, Nolan JP, Billi JE, Bottiger BW, Bossaert L, de Caen AR, Deakin CD, Drajer S,

Eigel B, Hickey RW, Jacobs I, Kleinman ME, Kloeck W, Koster RW, Lim SH, Mancini ME,

Montgomery WH, Morley PT, Morrison LJ, Nadkarni VM, O'Connor RE, Okada K, Perlman

JM, Sayre MR, Shuster M, Soar J, Sunde K, Travers AH, Wyllie J, and Zideman D. Part 1:

Executive summary: 2010 International Consensus on Cardiopulmonary Resuscitation

and Emergency Cardiovascular Care Science With Treatment Recommendations.

Circulation 122: S250‐275, 2010.

8. Yuan HX, Zheng ZS, Zhong HF, Liao XX, Du ZM and Huang X. “Double Pump”

Cardiopulmonary Resuscitation: The Hemodynamic Study of the Inflational VEST with

External Counterpulsation (II). J of Jinan Univ (Nature Science & Medicine Edition): S1,

1997

9. Noordergraaf JG, Dijkema JT, Kortsmit WJPM., Schilders WHA, Scheffer GJ, and

Noordergraaf A. Modeling in Cardiopulmonary Resuscitation: Pumping the Heart.

Cardiovasc Eng 5: 105‐118, 2005.

10. Panzer W, Bretthauer M, Klingler H, Bahr J, Rathgeber J, and Kettler D. ACD versus

standard CPR in a prehospital setting. Resuscitation 33: 117‐124, 1996.


25

11. Ralston SH, Babbs CF, and Niebauer MJ. Cardiopulmonary Resuscitation with Interposed

Abdominal Compression in Dogs. Anesth Analg 61: 645‐651, 1982.

12. Safar P. Closed chest cardiac massage. Anesth Analg 40: 609‐613, 1961.

13. Sagawa K, Lie RK, and Schaefer J. Translation of Otto Frank's paper "Die Grundform des

Arteriellen Pulses" Zeitschrift fur Biologie 37: 483‐526 (1899). J Mol Cell Cardiol 22:

253‐277, 1990.

14. Sayre MR, Koster RW, Botha M, Cave DM, Cudnik MT, Handley AJ, Hatanaka T, Hazinski

MF, Jacobs I, Monsieurs K, Morley PT, Nolan JP, Travers AH, and Adult Basic Life Support

Chapter C. Part 5: Adult basic life support: 2010 International Consensus on

Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With

Treatment Recommendations. Circulation 122: S298‐324, 2010.

15. Shuster M, Lim SH, Deakin CD, Kleinman ME, Koster RW, Morrison LJ, Nolan JP, Sayre MR,

Techniques CPR, and Devices C. Part 7: CPR techniques and devices: 2010 International

Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care

Science With Treatment Recommendations. Circulation 122: S338‐344, 2010.

16. Snyder MF, Rideout VC, and Hillestad RJ. Computer modeling of the human systemic

arterial tree. J Biomech 1: 341‐353, 1968.

17. Tang W, Weil MH, Schock RB, Sato Y, Lucas J, Sun S, and Bisera J. Phased chest and

abdominal compression‐decompression. A new option for cardiopulmonary resuscitation.

Circulation 95: 1335‐1340, 1997.

18. von Planta M and Trilló G. Closed chest compression: a review of mechanisms and

alternatives. Resuscitation 27: 107‐115, 1994.

19. W.B.Kouwenhoven, R.Jude J, and Knickerbocker GG. Closed‐chest cardiac massage. JAMA

173: 1064‐1067, 1960.

20. Ward KR. Possible reasons for the variability of human responses to IAC‐CPR. Acad Emerg

Med 1: 482‐489, 1994.

21. Yuan H, Zheng Z, and Jiang S. The Hemodynamic Study of The Chest Compression Assisted

with Enhanced External Counterpulsation. Chinese J Emerg Med 4: 140‐144, 1995.

Chapter 2

26

Appendix‐modelingdetails

Table 1A Definitions and values of resistances

Definition

R i t f S b l H /L/carotid arteries Rc 60

head vasculature Rh 5520

jugular veins Rj 30

tricuspid valve Rtv 5

pulmonic valve Rpv 10

path from central to peripheral pulmonary arteries Rcppa 10

path from peripheral to central pulmonary veins Rcppv 5

mitral valve Rmv 5

aortic valve Rav 10

pulmonary capillary bed Rpc 105

coronary vessels (heart) Rht 15780

aorta Ra 25

inferior vena cava Rv 25

splanchnic vasculature Rs 1800

iliac arteries Ria 360

iliac veins Riv 180

leg vasculature Rl 8520

airways to ventilation Rairway 1.2


27

Table 1B Definitions and values of compliances

Definition

Compliance of the ….. Symbol L/mmHg

Initial

volume (L)

Unstressed

volume (L)

arrested right ventricle Crv 0.01600 0.150 0.070

large pulmonary arteries Cpa 0.00420 0.109 0.076

peripheral pulmonary arteries Cppa 0.00042 0.160 0.157

peripheral pulmonary veins Cppv 0.00128 0.300 0.290

central pulmonary veins and left atrium Cla 0.01280 0.250 0.148

arrested left ventricle Clv 0.00800 0.150 0.086

carotid arteries Ccar 0.00020 0.132 0.131

jugular veins Cjug 0.01200 0.216 0.156

thoracic aorta Cao 0.00080 0.078 0.074

right atrium and intrathoracic great veins Crh 0.00950 0.367 0.320

abdominal aorta Caa 0.00040 0.114 0.112

inferior vena cava Civc 0.02340 1.930 1.813

femoral arteries Cfa 0.00020 0.078 0.078

femoral veins Cfv 0.00470 0.267 0.244

combined lung‐chest wall Clung 0.15800 ‐ ‐

Initial conditions: 5 mmHg filling pressure in the systemic, 8 mmHg in the pulmonary

circulation. Valves are supposed to be ideal, driven by pressure differences with the

resistances as indicated, immediately closing when reverse flow would occur. Compliances

are supposed to behave as follows: For positive pressures, volumes are increasing linearly

above the unstressed volume as indicated by the index of compliance; for negative pressures

volumes are supposed to be zero and the unstressed volume is emptied. For more details the

reader is referred to: Koeken, Y, Aelen P, Noordergraaf GJ et al. The influence of nonlinear

intra‐thoracic vascular behaviour and compression characteristics on cardiac output during

CPR. Resuscitation 2011; 82: 538‐544.

Chapter 2

28

Table 2 Miscellaneous: Abbreviations and definitions

Abbreviation Definition Value

Al Cross section area of lung squeezed by sternal compression 100 cm2

Ara Ala Arv Alv Cross section area of cardiac chambers in front to back 20 cm2

Frequency Number of cycle per minute for chest and abdominal pressure 100/min

Duty cycle Fraction of cycle time for chest compression 0.5

ftp Thoracic pump factor

(0.75=adult, 0.25=child, 1.0=emphysema, 0=open chest)

0 ‐ 1.0

Pinit Initial equilibrium pressure of arrested circulation 5 mmHg

Fmax_chest Maximum external force on sternum 400 N

X0 Effective compression threshold 2 cm

Table 3 Blood flow Abbreviations and definitions

Abbreviation Definition

ic Blood flow in both carotid arteries

ih Blood flow in the head vasculature

ij Blood flow in both jugular veins

ii The input blood flow to thoracic

io The output blood flow from thoracic

iht Blood flow in coronary vessels(heart)

ia Blood flow in the aorta

is Blood flow in residential systemic vasculature

iv Blood flow in the inferior vena cava

ifa Blood flow in both femoral arteries

ifv Blood flow in both femoral veins

i2 Blood flow in the pulmonary veins

i3 Blood flow between central and peripheral pulmonary arteries

i4 Blood flow in the pulmonary capillaries

i5 Blood flow in between central and peripheral pulmonary veins

i6 Blood flow in the left atrium


29

Table 4 Volumes and pressures: Abbreviations and definitions

Abbreviation Definition

Blood volume

Vaa Blood volume in the abdominal aorta

Vivc Blood volume in the inferior vena cava

Via Blood volume in both iliac arteries

Vfa Blood volume in both femoral arteries

Vfv Blood volume in both femoral veins

Blood pressure

Pao Blood pressure of thoracic aorta

Paa Blood pressure of the abdominal aorta

Pivc Blood pressure of the inferior vena cava

Pfa Blood pressure of both femoral arteries

Pra Blood pressure of the right atrium

Pfv Blood pressure of the femoral veins

Peecp Compression pressure to lower body

Pabd compression pressure to the abdomen

CPP Coronary perfusion pressure

Chapter 2

30

There are 14 compartments in the model; there are 14 pairs of formulas to describe the

relationship between pressure and volume. ‘P’ stands for pressure, ‘V’ stands for volume, and

Subscripts indicate which compartment the formulas describe.

( )

[ ]

car c h

car jugao car

c h

carcar

car

V i i t

P PP Pt

R R

VP

C

(1)

( )

[ max(0, )]

jug h j

car jug jug ra

h j

jugjug

jug

V i i t

P P P Pt

R R

VP

C

(2)

When the chest is compressed, the organs in the chest pump undergo different pressure

weights. ‘F(t)’ is the chest compression force, ‘PM’ is the pressure to the mediastinum, ‘Plung’ is

the pressure to the lungs. ‘ftp’ is thoracic pump factor; more information in references 2, 3.

1 1( ) 0F t kx x (3)

1 2

0

( )M

E x xP

d

(4)

1[ ]lung monthlung L

lung airway

P PdtdP x A

C R

(5)

3 4( )

[ ]

ppa

pa ppa ta tv

cppa pa

ppappa lung

ppa

V i i t

P P P Pt

R R

VP P

C

(6)


31

4 5( )

[ ]

ppv

ppa ppv ppv la

pc cppv

ppvppv lung

ppv

V i i t

P P P Pt

R R

VP P

C

(7)

10

( )

[max(0, ) ]

ao o c a ht

lv ao ao car ao car ao ra

av c a ht

paao lung tp

pa

V i i i i t

P P P P P P P Pt

R R R R

V EP P f x t

C d

(8)

2 3

10

( )

[max(0, ) ]

pa

rv pa pa ppa

pv cppa

papa lung tp

pa

V i i t

P P P Pt

R R

V EP P f x t

C d

(9)

10

( )

[max(0, ) max(0, )]

( )

ra j v ht i

jug ra ivc ra ao ra ra rv

j v ht tv

ra rara lung tp

ra ra

V i i i i t

P P P P P P P Pt

R R R R

V VEP P f x t

C d A

(10)

2

10

( )

[max(0, ) max(0, )]

( )

rv i

rv para rv

tv pv

rv rvrv lung

rv rv

V i i t

P PP Pt

R R

V VEP P x t

C d A

(11)

5 6

10

( )

[ max(0, )]

( )

la

ppv la la lv

cppv mv

la lala lung tp

la la

V i i t

P P P Pt

R R

V VEP P f x t

C d A

(12)

Chapter 2

32

6

10

( )

[max(0, ) max(0, )]

( )

lv o

la lv lv ao

mv av

lv lvlv lung

lv lv

V i i t

P P P Pt

R R

V VEP P x t

C d A

(13)

When the abdomen is compressed in IAC-CPR, the abdominal aorta and inferior vena

cava are directly exposed to the compression pressure.

( )

[ ]

aa a s ia

aa faao aa aa ivc

a s ia

aaaa abd

aa

V i i i t

P PP P P Pt

R R R

VP P

C

(14)

( )

[ ) max(0, )]

ivc s v fv

fv ivcaa ivc ivc ra

s v iv

ivcivc abd

ivc

V i i i t

P PP P P Pt

R R R

VP P

C

(15)

When the lower body is compressed in EECP-CPR, it undergoes the compression

pressure directly.

)],0max([)(1

][)(1

iv

ivcfv

l

fvfa

fvlfvl

fvlfv

l

fvfa

ia

faaa

fallia

falfa

R

PP

R

PP

C

tPtii

CPP

R

PP

R

PP

C

tPtii

CPP

(16)

33

Chapter3

AbdominalcounterpressureinCPR:Whataboutthelungs?

Aninsilicostudy

Yanru Zhang and John M. Karemaker

This chapter had been published as:

Zhang Y and Karemaker JM. Abdominal counter pressure in CPR: What about the lungs? An in

silico study. Resuscitation 83: 1271‐1276, 2012.

The appendix of chapter 2 equally applies to the model used in this chapter.

Chapter 3

34

Abstract

The external pumping action in CPR should generate sufficient flow and pressure, but the

pump must also be ‘primed’ by ongoing venous return. Different additions to standard CPR

are in use just for this purpose. Active decompression of the thorax (ACD‐CPR) to ‘suck in’

venous blood has proven successful, but, theoretically, compression of venous reservoirs in

the abdomen should be even more effective. We compared different techniques for

improved CPR with specific attention to the pulmonary circulation. We did our comparisons

‘in silico’ rather than ‘in vivo’ in a well‐evaluated computer model.

Methods: We used an adapted version of Babb’s computer model for CPR, reprogrammed in

Matlab®. 1) We compared standard chest compression‐only CPR (CO‐CPR) and ACD‐CPR to

CPR with interposed abdominal compression (IAC‐CPR). 2) Since the thorax/heart

configuration differs between patients, and consequently the way blood is propelled by the

chest compressions, we checked the influence of the ratio thoracic/cardiac pump

effectiveness.

Results: 1) Only IAC‐CPR leads to physiological values for mean aortic pressure and cardiac

output. 2) However, since the whole heart is in the pressure chamber of the compressed

thorax, pulmonary artery pressure rises to about the same level as aortic pressure. In practice,

this might lead to pulmonary edema during and after CPR, unless 3) Intra‐abdominal

compression pressure is strictly limited; simulations indicate that intra‐abdominal pressure

should not exceed 30‐40 mmHg.

Conclusions: IAC‐CPR outperforms the other techniques in achieving good aortic pressure

and cardiac output. However, abdominal pressure should be limited.

CPR-optimisation and the lungs

35

Introduction

Mechanical adjunct devices which do more than just compressing the thorax to improve CPR

have been proposed and tried in various studies (1‐6).Despite the initial high hopes of the

inventors, to date these CPR techniques fail to give consistently better outcomes than

standard CPR (S‐CPR) (7‐9). In 2010 the AHA published its updated guidelines (10) for how

and when to perform Cardiopulmonary Resuscitation (CPR). The new guidelines devote only

1.5 out of 330 pages to the option of more complex, possibly machine‐supported, modalities

of CPR in view of the lack of supporting clinical evidence (10,11).One wonders why praxis is

lagging behind, since it makes perfect sense, theoretically, to improve venous return by

intermittent abdominal in counter phase with thoracic compressions (IAC‐CPR).

In early 2011 the Lancet published a large randomized, multicenter trial that shows improved

outcome of CPR when the effect of chest compressions is supported by mechanical devices

(12), these are: A hand‐held suction cup with handle placed on the thorax to support active

thoracic recoil in the relaxation phase (ACD‐CPR) and an impedance‐threshold valve (ITV) to

connect to a facemask or advanced airway access that would not open until intrathoracic

pressure would fall below – 16 cm H2O pressure. The latter was in place to promote venous

return during the supported chest recoil phase. The Lancet study thus encourages adjuncts to

S‐CPR. It showed that ACD‐CPR with ITV gave the same survival rate, but better neurological

function than S‐CPR (12). The only significant adverse effect where intervention‐ and S‐CPR

groups differed was in the prevalence of pulmonary edema: 11% in the intervention group

(94/840) compared to 8% (62/813) in the SCPR group. This inspired us further to elaborate on

our earlier modeling work (13), looking more specifically into the pulmonary effects of

increased venous return during CPR.

For the present study, we used a computer model to simulate compression‐only (CO‐CPR)

following the new (2010) guidelines, i.e. 100 compressions per minute, no breaks for chest

inflation; next ACD‐CPR with and without ITV and, additionally, CPR with Interposed

abdominal compression (IAC‐CPR). The aim was to explore favorable and potentially

unfavorable hemodynamic changes produced by augmented CPR techniques compared to

standard manual CPR, specifically looking at the effects of improved venous return. For the

purpose of this theoretical study, we used a well‐known mathematical model of the

circulation and the application of CPR, which has been developed and extensively published

by Babbs (14‐16). A computer model allows analyzing the effects of alternative CPR

techniques on many aspects of the cardiovascular and respiratory systems at the same time.

Chapter 3

36

In the experimental laboratory, this would require sacrificing many experimental animals and

in the clinic, it would be next to impossible. In the model we checked the effects on systemic

and pulmonary pressures, ventricular pressures, flow to vital organs and so on. Our working

hypothesis was that the CPR techniques with supported venous return may have side effects

which prevent them from reaching their full beneficial effect.


37

Methods

Modeldescription

The computer model used is essentially Babbs’ circulatory model, programmed in Matlab® as

we used it in earlier studies; details are in (13,15). In short, the model simulates a 70‐kg adult,

it includes four heart chambers, the pulmonary circulation, the thoracic aorta feeding an

upper body compartment and an abdominal compartment, the latter feeds the lower body

(legs, buttocks) compartment. CPR is modeled by the application of external forces to the

various compartments. Figure1A gives a mechanical representation of the model, Figure1B its

electrical analogue as used in the calculations; Table 1 in the Appendix to chapter 2

summarizes the most important model parameters; Table 1A gives the resistances in the

model, Table 1B the compliances, initial volumes and unstressed volumes in all

compartments. The model supposes a constant blood volume of 4.36 liters, the subject starts

in the condition of cardiac arrest, where blood flow has stopped and the blood volume is

distributed over the various compartments in relation to their volume compliance. The mean

filling pressure in the systemic circulation was chosen to be 5 mmHg (17) that of the

pulmonary circulation 8 mmHg while supine.

Figure 1 A: Schematic of the circulation under CPR. ACD: Alternating Compression‐Decompression, IAC: Interposed Abdominal Compression, ITV: Impedance threshold valve, impeding inflow of air at negative intrathoracic pressures. NV: Niemann’s valve, preventing reverse venous flow from the thorax to head and neck. VV: venous valves in the legs, preventing reverse venous flow from the abdominal compartment. The (upper) arterial side is connected by lumped Starling resistors to the (lower) venous side.

Chapter 3

38

Figure 1 B: Electrical analogue of the circulation as used in the calculations. Top: the whole circulation; Bottom: the details inside the chest pump. Symbols as explained in Appendix: Table 1.

When CPR is started, chest compression simultaneously increases intrathoracic and

mediastinal pressure, the first one working on all compartments in the chest, the second one

leading to compression of the heart between sternum and spine. Both contribute as


39

‘circulatory pumps’ to the generation of flow and blood pressure. We followed Babbs’ model

in attributing 75% of the CPR effect to the ‘thoracic pump’ and ‘25% to the ‘cardiac pump’

effects (15). As this choice influences the result, we also checked various other values of this

factor.

Abdominal compression is supposed to lead to an immediate pressure increase within the

abdomen, compressing in particular the capacity veins feeding the right heart, but also the

abdominal aorta, giving the effect of an aortic balloon pump, and changing diastolic runoff.

Babbs’ original model does not include abdominal wall mechanics, which in general is much

less of a hindrance to externally applied pressures. Therefore, we assumed a homogeneous

pressure in the abdominal cavity, following the set time pattern not bothering about how

much pressure was applied to the outside to generate this inside pressure.

The model does not take displacement of the diaphragm into account, neither during

thoracic nor abdominal compression. Computed pressures in the aorta (minus right atrial

pressure) lead to organ flow, depending on the estimated resistances of the various organs

(brain in particular). To get a realistic estimate of coronary flow, we suppose that no flow

passes while the heart is being compressed.

CPRtechniques

We simulated three different CPR techniques: first chest‐compression only CPR (CO‐CPR) as

applied by one rescuer and Active Compression‐Decompression CPR (ACD‐CPR) with and

without an impedance–threshold valve (ITV) as in the Lancet study (12). Furthermore, we

implemented one technique that combines thorax ‐ with abdomen compression in the

relaxation phase to support venous return, i.e. Interposed Abdominal Compression CPR

(IAC‐CPR). Two rescuers together, one administering thorax compression and the other one

compressing the abdomen in counter phase, can administer this (3,18).

In keeping with the new guidelines, a compression frequency of 100/min is used for all

techniques; no time is devoted to ventilation. The chest is compressed by a force of 400 N, or

around 40 kilo’s, which clinically relates to a depth of 5.1 cm; non‐overlapping half‐sinusoids

with 50% duty cycle are used (15,19) as external pressure waveforms. A force of 150 N

supports active decompression of the thorax in ACD‐CPR; abdominal compression is

supposed to result in (up to) 100 mmHg intra‐abdominal pressure in IAC‐CPR. The latter two

are also shaped as half‐sinusoids, with 50% duty cycle in exact counter phase to the thorax

compressions.

Chapter 3

40

Results

EffectsofvariousCPRtechniques

The successive columns of Table 1 show that with increasing complexity of the applied

technique the numbers get better: higher aortic pressures, higher CO. Indeed, by using

IAC‐CPR almost physiological levels for mean aortic pressure and cardiac output can be

reached. Figure 2 demonstrates how this is obtained: in the phase of abdominal compression,

thoracic aortic pressures rise again due to increased systemic vascular resistance during

diastolic runoff. In line with the definitions for the working heart, we have defined diastolic

pressures as those pressures at the start of thorax compression.

Figure 2: Aortic (Pao fat –red‐ line) and peripheral pulmonary venous pressures (Pppv thin –blue‐ line) during Interposed Abdominal Compression CPR (IAC‐CPR) at a compression rate of 100/min, abdominal compression pressure: 40mmHg, thoracic pump factor of 0.75.

Table 1 also shows that ACD‐CPR, indeed, gives better results than CO‐CPR, only slightly

improved by the addition of an ITV. However, none of these 3 techniques gives very

satisfactory pressures or cardiac output, while the 4th technique IAC‐CPR, can easily overdo it:

at increasing abdominal pressures the forced venous return leads to overly increased values


41

for ventricular end‐diastolic and pulmonary artery pressures. All numbers indicate that

pulmonary capillary pressures will be above normal plasma colloid osmotic pressures (around

25‐30 mmHg (20)), which may cause acute pulmonary edema. However, one may well ask to

what extent these results are due to choices made in the modeling process, in particular the

division between cardiac pump and thoracic pump. In the computations for Table 1 a thoracic

pump factor of 0.75 was used.

Table 1. Blood pressures and flows for the tested CPR‐techniques. Thoracic pump factor is 0.75

Ventilation/ITV CO‐CPR‐ ACD‐CPR

No ITV

ACD‐CPR

With ITV

IAC‐CPR

No ITV

IAC‐CPR

No ITV

IAC‐CPR

No ITV

AC‐CPR

No ITV

Chest comp/decomp

(force in N) 400/‐ 400/150 400/150 400/‐ 400/‐ 400/‐ 400/‐

Abdominal

compression ‐ ‐ ‐ 40 80 100 Continuo

us 40

Pao (sys/dia; mean) 47/22; 30 52/27; 35 55/28; 37 61/34; 46 75/45; 62 83/52; 71 66/41; 49

Plv (sys/end‐dia) 48/7 53/8 56/10 62/26 76/44 84/53 67/27

CO (l/min) 1.3 1.5 1.7 2.0 2.8 3.2 1.4

Ppa (sys/dia; mean) 41/7; 18 41/7; 17 42/6; 18 62/27; 39 82/47; 58 92/57; 68 62/27; 39

Prv (sys/end‐dia) 42/4 41/4 43/1 63/25 83/47 93/57 62/22

Pppv (sys/dia; mean) 33/7; 16 31/7; 14 34/5; 15 53/26; 35 72/44; 53 81/53; 62 53/27; 36

Qheart (ml/s) 0.8 0.9 1.0 1.0 1.2 1.4 0.8

Qhead (ml/s) 5.3 6.0 6.6 8.5 11.8 13.6 6.1

All pressures are in mmHg. Pao: aortic pressure; Plv: left ventricular pressure; Ppa: pulmonary arterial pressure; Prv: right ventricular pressure; Pppv: peripheral pulmonary veins; sys: systolic pressure; dia: diastolic pressure; end‐dia: end‐diastolic pressure. CO: cardiac output; Qheart: coronary blood flow; Qhead: blood flow to neck and head; AC‐CPR: abdominal compression CPR (the pressure is no longer interposed, but continuous).

Chapter 3

42

Cardiacvsthoracicpump

The ‘cardiac pump’ theory supposes that forward blood flow is caused by direct compression

of the heart under the sternum (with blood flow similar to an intact circulation); while the

‘thoracic pump’ theory supposes that blood flow is secondary to changes in intrathoracic

pressure (21). The exact contribution of either pump mechanism in a specific case is unknown

(21‐24),and may depend on thorax‐heart configuration: a deep thorax translating

CPR‐pressure more into a thoracic pump effect, a flat (or child’s‐) thorax more into a cardiac

pump effect. Therefore, we tested how the alternative attribution of ‘thoracic pump’ or

‘cardiac pump’ might influence the results. In the model a thoracic pump factor 0 implies a

pure cardiac pump, 1 a pure thoracic pump.

Figure 3 Top: Effects of thoracic pump factor in CO‐CPR (cardiac pump factor + thoracic pump factor = 1); a higher thoracic pump fraction leads to lower aortic pressures and higher pulmonary pressures. A thoracic pump factor = 0 is comparable to direct cardiac massage. Chest compression force: 400 N.

Bottom: Effects of thoracic pump factor in IAC‐CPR; a higher thoracic pump factor has little effect on aortic pressures and leads to higher pulmonary pressures. Chest compression Force: 400 N; abdominal compression pressure: 100 mmHg.


43

Figure 3 top shows the hemodynamic effects on CO‐CPR when the thoracic pump factor

changes from 0 to 1: diastolic and mean aortic pressures (left panel) decrease and all

pulmonary pressures (right panel) increase. Figure 3 bottom shows the same for IAC‐CPR, (in

the computations a peak abdominal pressure of 100 mmHg was assumed). Here the effects

on aortic pressures are less outspoken, much more those on peripheral pulmonary venous

pressures. Even at a pure cardiac pump effect (thoracic pump factor = 0) the pressure in the

pulmonary capillaries will be too high at this abdominal pressure. Therefore we chose to look

for an optimal abdominal pressure that would prevent acute pulmonary edema.

OptimizationofIAC‐CPR

If mean pulmonary capillary pressure exceeds plasma colloid osmotic pressure (around

25‐30 mmHg), pulmonary edema may be expected to occur. To prevent this, we tried a range

of abdominal pressures, from 0 to 100 mmHg, at a thoracic pump factor of 0.75. Figure 4

shows that Pppv (the model’s approximation of pulmonary capillary pressure) exceeds a

value of 30 mmHg when abdominal compression pressure is higher than 30 mmHg; at this

point CO is 1.8 L/min and mean aortic pressure around 43 mmHg.

Figure 4 Effects of abdominal compression pressure in IAC‐CPR. Thoracic pump factor is set to 0.75; chest compression force: 400 N; abdominal compression pressure: 0‐100 mmHg. The point of 30 mmHg (mean) peripheral pulmonary venous pressure is shown as upper limit above which pulmonary edema due to hydrostatic pressure may occur. At that level mean aortic pressure is ca. 43 mmHg and cardiac output 1.8 L/min.

Chapter 3

44

Discussion

This study shows that CO‐CPR may be improved upon: better systemic pressures and cardiac

output can be reached by improving venous return and increasing lower body vascular

resistance as in IAC‐CPR. However, these improvements come at a price: the lungs are at risk.

In the early papers where IAC‐CPR was applied, the authors looked specifically for damage to

abdominal organs (4, 25). They took great care in how and how much pressure was applied,

and thereby they were able to prevent abdominal trauma. We have shown that peripheral

pulmonary venous pressures may run very high during IAC‐CPR (Figure 3) with the inherent

risk of pulmonary edema, which may prevent successful resuscitation or lead to protracted

problems after return of spontaneous circulation. In an experimental study in dogs Kern et al

(26) showed that IAC‐CPR led to 3/10 cases of pulmonary edema vs. 1/10 in 2 alternative

–chest compression only ‐ CPR techniques. In a critical review of the clinical use of IAC‐CPR

Ward (9) ascribed the variability in its outcome to its inconsistency to improve coronary

perfusion pressure (CPP) compared to S‐CPR. Although he did not mention pulmonary edema,

he did notice that at various ways to exert abdominal compression the induced abdominal

pressures rise above 100 mmHg, even up to 150 mmHg. The resulting increase in right atrial

pressure was supposed to be the cause of decreased CPP. In our study we did not find a

diminution of CPP under IAC‐CPR compared to CO‐CPR, even while accounting for the aortic

to right atrial pressure drop and setting cardiac perfusion to zero in the phase of thorax

compression.

As to the damage of the lungs that is hypothesized in our study: what is lacking to make our

case, are systematic data from thorax X‐ray after successful CPR and/or autopsy data on

lungs after unsuccessful attempts. In the early years of CPR a few reports have been devoted

to the problem of pulmonary edema (27, 28). Probably more data are available in many

clinics where the more intense machine‐supported CPR is in use for unshockable cardiac

arrest, but except for a few publications (29, 30)it seems that detailed analysis and reporting

of pulmonary pathology is not part of standard CPR follow‐up.

In this study, we have shown that increased venous return by active decompression of the

thorax, but even more by abdominal compression leads to better organ flow in the absence

of a spontaneous rhythm. However, there seem to be limits to ‘better pressures and better

venous return’: the wall of the right ventricle might be endangered when venous return is

increased too much with ensuing increased diastolic pressures in the ventricle. Moreover, the

high pressures in the pulmonary circulation may lead to acute pulmonary edema, shifting the


45

cause of death from cardiac arrest to suffocation. Of course, this prospect may not

discourage rescuers from starting CPR in the best possible way; pulmonary edema can be

resolved over time, after return of spontaneous circulation. This complication that might be

present immediately after successful CPR should be recognized and properly treated.

In view of the two competing mechanisms that explain the effects of CPR: the thoracic pump

and the cardiac pump, one might argue that active abdominal compression in fact adds a

third pump, in series with the thoracic and cardiac pump (31). As is the case with two

locomotives that combine forces to pull a heavy train, one should take care that not one

locomotive is doing all the pulling or pushing, including the other one. In the case of the two

CPR‐pumps together, our modeling points to the lungs as being ‘caught in the middle’ This

might call for a new look at CPR, for instance applying the compression/suction cup to the

abdomen and reversing the ITV, so as to block outflow of air up to a certain pressure. That

might prevent many of the known dangers of present‐day CPR, like broken ribs, cardiac

contusion and pulmonary edema. Alternatively a G‐suit‐like device may be applied to the

abdomen and legs. In many ambulances these are available as inflatable supports to stabilize

broken legs or to support the circulation after extensive loss of blood. Rubal et al. (32)

studied the effects of constant G‐suit inflation in healthy subjects by left and right heart

catheterization. They showed that inflation pressures >40 mmHg can significantly increase

both cardiac and pulmonary pressures. In a swine model of CPR Lottes et al. ( 33) tested the

effect of a constantly inflated G‐suit. They applied G‐suit pressures up to 200 mmHg which

resulted in improvements like those obtained by vasopressor drugs. In our model a

continuous intra‐abdominal pressure of around 40 mmHg (Table 1, last column) gives about

the same pressures, systemic and pulmonary, as IAC‐CPR at the same abdominal value.

However, flows are considerably less: these are comparable to CO‐CPR.

A short survey of available human and animal experimental data shows a consistent lack of

measurements on the low‐pressure side of the circulation. We consider our study to be

successful when in follow‐up experimental CPR‐studies more often a Swan‐Ganz catheter is

used.

Studylimitations:This computer model is based on anatomy and physiology, combined with experimental data

when these were available. Elaborate studies, implementing other options to improve CPR,

have been published by Babbs (16,34,35). In animal experiments higher aortic pressures than

in our simulations have sometimes been observed (36). However, those were acute

Chapter 3

46

experiments in animals with healthy hearts (and lungs), probably not comparable to the

‘average’ human heart suddenly going into cardiac arrest due to a build‐up of underlying

cardiac pathology. What is not modeled is the effect of abdominal compression on

displacement of the diaphragm and thereby compression of the thoracic contents (lungs and

heart).

No attempts have been made here to incorporate the effects of oxygenation by

mouth‐to‐mouth breathing or otherwise. However limited, a model is just as good as the

assumptions that were input to it. In particular the supposed linearity of many anatomical

and physiological systems is a simplification to keep models practical. Since this particular

model covers a large range of pressure‐ and volume changes, differences between model and

outcome in praxis must exist.

Conclusions:This study shows that there is room for improvement of CPR by proper choice of parameters

for thorax and/or abdominal compression. However, this has its limits: more is not always

better. Too much venous return, combined with effective thorax and heart compression may

unduly increase pulmonary pressure, thereby exposing the lungs to the risk of acute edema.

Post‐CPR checks of pulmonary damage by X‐ray or autopsy should be a standard follow‐up on

CPR, be it successful or unsuccessful.

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of cardiopulmonary resuscitation. Cardiopulmonary Resuscitation Working Group. JAMA

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2. Lurie KG, Shultz JJ, Callaham ML, et al. Evaluation of active compression‐decompression

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3. Ralston SH, Babbs CF, Niebauer MJ. Cardiopulmonary resuscitation with interposed

abdominal compression in dogs. Anesth Analg 1982; 61: 645‐51.

4. Sack JB, Kesselbrenner MB, Bregman D. Survival from in‐hospital cardiac arrest with

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5. Tang W, Weil MH, Schock RB, et al. Phased chest and abdominal

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6. Yuan H, Jiang L, Xu W, Yuan S, He G. Hemodynamics of Active

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47

Inspiratory Impedance Threshold Valve. Lingnan Journal of Emergency Medicine 2007;

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7. Mateer JR, Stueven HA, Thompson BM, Aprahamian C, Darin JC. Pre‐hospital IAC‐CPR

versus standard CPR: Paramedic resuscitation of cardiac arrests. Am J Emerg Med 1985;

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8. Stiell IG, Hebert PC, Wells GA, et al. The Ontario trial of active

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9. Ward KR. Possible reasons for the variability of human responses to IAC‐CPR. Acad Emerg

Med 1994; 1: 482‐9.

10. Hazinski MF, Nolan JP, Billi JE, et al. Part 1: executive summary: 2010 International

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11. Cave DM, Gazmuri RJ, Otto CW, et al. Part 7: CPR techniques and devices: 2010 American

Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency

Cardiovascular Care. Circulation 2010; 122: S720‐S728.

12. Aufderheide TP, Frascone RJ, Wayne MA, et al. Standard cardiopulmonary resuscitation

versus active compression‐decompression cardiopulmonary resuscitation with

augmentation of negative intrathoracic pressure for out‐of‐hospital cardiac arrest: a

randomised trial. The Lancet 1922; 377: 301‐11.

13. Zhang Y, Xu L, Wu X, Liu Q, Yuan H. The Hemodynamic Effects Analysis of the New CPR

Technique‐Electro Ventilation Double Pump CPR: Studies in the Computer Model.

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14. Babbs CF, Weaver JC, Ralston SH, Geddes LA. Cardiac, thoracic, and abdominal pump

mechanisms in cardiopulmonary resuscitation: studies in an electrical model of the

circulation. Am J Emerg Med 1984; 2: 299‐308.

15. Babbs CF. Effects of an impedance threshold valve upon hemodynamics in Standard CPR:

studies in a refined computational model. Resuscitation 2005; 66: 335‐45.

16. Babbs CF. Design of near‐optimal waveforms for chest and abdominal compression and

decompression in CPR using computer‐simulated evolution. Resuscitation 2006; 68:

277‐93.

17. AC, Polozo D, Armstrong GG. Mean circulatory filling pressure measured immediately

after cessation of heart pumping. Am J Physiol 1954; 179: 261‐7.

18. Berryman CR, Phillips GM. Interposed abdominal compression‐CPR in human subjects.

Annals of Emergency Medicine 1984; 13: 226‐9.

19. Noordergraaf GJ, Dijkema TJ, Kortsmit WJPM, Schilders WHA, Scheffer GJ, Noordergraaf

A. Modeling in Cardiopulmonary Resuscitation: Pumping the Heart. Cardiovascular

Engineering 2005; 5: 105‐18.

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20. Guyton AC, Lindsey AW. Effect of Elevated Left Atrial Pressure and Decreased Plasma

Protein Concentration on the Development of Pulmonary Edema. Circ Res 1959; 7:

649‐57.

21. Houri PK, Frank LR, Menegazzi JJ, Taylor R. A randomized, controlled trial of two‐thumb

vs two‐finger chest compression in a swine infant model of cardiac arrest. Prehosp

Emerg Care 1997; 1: 65‐7.

22. Knhn C, Juchems R, Frese W. Evidence for the `Cardiac Pump Theory' in cardiopulmonary

resuscitation in man by transesophageal echocardiography. Resuscitation 1991; 22:

275‐82.

23. Haas T, Voelckel WG, Wenzel V, Antretter H, Dessl A, Lindner KH. Revisiting the cardiac

versus thoracic pump mechanism during cardiopulmonary resuscitation. Resuscitation

2003; 58: 113‐6.

24. Paradis NA, Martin GB, Goetting MG, at al. Simultaneous aortic, jugular bulb, and right

atrial pressures during cardiopulmonary resuscitation in humans. Insights into

mechanisms. Circulation 1989; 80: 361‐8.

25. Babbs CF. Interposed abdominal compression CPR: a comprehensive evidence based

review. Resuscitation 2003; 59: 71‐82.

26. Kern KB, Carter AB, Showen RL, Voorhees III WD, Babbs CF, Tacker WA, Ewy GA.

CPR‐induced trauma: Comparison of three manual methods in an experimental model.

Ann Emerg Med 1986; 15: 674‐9.

27. Dohi S. Postcardiopulmonary resuscitation pulmonary edema. Crit Care Med 1983; 11:

434‐7.

28. Ornato JP, Ryschon TW, Gonzalez ER, Bredthauer JL. Rapid change in pulmonary vascular

hemodynamics with pulmonary edema during cardiopulmonary resuscitation. Am J

Emerg Med 1985; 3: 137‐42.

29. Hoeper MM, Galie N, Murali S, et al. Outcome after cardiopulmonary resuscitation in

patients with pulmonary arterial hypertension. Am J Respir Crit Care Med 2002; 165:

341‐4.

30. Menzebach A, Bergt S, von WP, Dinu C, Noldge‐Schomburg G, Vollmar B. A

comprehensive study of survival, tissue damage, and neurological dysfunction in a

murine model of cardiopulmonary resuscitation after potassium‐induced cardiac arrest.

Shock 2010; 33: 189‐96.

31. Aliverti A, Bovio D, Fullin I, et al. The abdominal circulatory pump. PLoS One 2009; 4:

e5550.

32. Rubal BJ, Geer MR, Bickell WH. Effects of pneumatic antishock garment inflation in

normovolemic subjects. J Appl Physiol 1989; 67: 339‐45.

33. Lottes AE, Rundell AE, Geddes LA, Kemeny AE, Otlewski MP, Babbs CF. Sustained

abdominal compression during CPR raises coronary perfusion pressures as much as

vasopressor drugs. Resuscitation 2007; 75: 515‐24.


49

34. Babbs CF. Biophysics of cardiopulmonary resuscitation with periodic z‐axis acceleration or

abdominal compression at aortic resonant frequencies. Resuscitation 2006; 69: 455‐69.

35. Jung E, Lenhart S, Protopopescu V, Babbs C. Optimal control applied to a

thoraco‐abdominal CPR model. Math Med Biol 2008; 25: 157‐70.

36. Adams CP, Martin GB, Rivers EP, Ward KR, Smithline HA, Rady MY. Hemodynamics of

interposed abdominal compression during human cardiopulmonary resuscitation. Acad

Emerg Med 1994; 1: 498‐502.

51

Chapter4

SearchforHRV‐parametersthatdetectasympatheticshiftinheart

failurepatientsonß‐blockertreatment

Yanru Zhang, Olav R. de Peuter, Pieter W. Kamphuisen, John M. Karemaker

This chapter has been published as:

Zhang Y, de Peuter OR, Kamphuisen PW, and Karemaker JM. Search for HRV‐parameters that

detect a sympathetic shift in heart failure patients on beta‐blocker treatment. Frontiers in

Physiol 4: 81, 2013.

Chapter 4

52

Abstract

Background: A sympathetic shift in heart rate variability (HRV) from high, beat‐to‐beat, to

lower frequencies may be an early signal of deterioration in a monitored patient. Most

chronic heart failure (CHF) patients receive ß‐blockers. This tends to obscure HRV observation

by increasing the fast variations. We tested which HRV parameters would still detect the

change into a sympathetic state.

Methods and results: ß‐blocker (Carvedilol®) treated CHF patients underwent a protocol of

10 minutes supine rest, followed by 10 minutes active standing. CHF patients (NYHA Class

II‐IV) n=15, 10m/5f, mean age 58.4 years (47‐72); healthy controls n=29, 18m/11f, mean age

62.9 years (49‐78). Interbeat intervals (IBI) were extracted from the finger blood pressure

wave (Nexfin®). Both linear and nonlinear HRV analyses were applied that (1) might be able

to differentiate patients from healthy controls under resting conditions and (2) detect the

change into a sympathetic state in the present short recordings. Linear: mean‐IBI, SD‐IBI,

rMSSD (root mean square of successive differences), pIBI‐50 (the proportion of intervals that

differs by more than 50 ms from the previous), LF, HF and LF/HF ratio. Nonlinear: SampEn

(sample entropy), MSE (Multiscale entropy) and derived: MSV (Multiscale variance) and MSD

(Multiscale rMSSD).

In the supine resting situation patients differed from controls by having higher HF and,

consequently, lower LF/HF. In addition their longer range (τ=6‐10) MSE was lower as well.

The sympathetic shift was, in controls, detected by mean‐IBI, rMSSD, pIBI‐50 and LF/HF, all

going down; in CHF by mean‐IBI, rMSSD, pIBI‐50 and MSD (τ=6‐10) going down. MSD6‐10

introduced here works as a band‐pass filter favoring frequencies from 0.02‐0.1Conclusions: In

ß‐blocker treated CHF patients, traditional time domain analysis (mean‐IBI, rMSSD, pIBI‐50)

and MSD6‐10 provide the most useful information to detect a condition change.

HRV parameters that detect a sympathetic shift

53

Introduction

Heart rate variability (HRV) analysis has seen an increasing interest since the early work of

B.McA. Sayers in the 1970’s (21), picking up speed since the 1980’s (1, 3, 16). However, these

analysis techniques still have not made it to the bedside, probably due to the fact that

equipment manufacturers do not offer standard solutions in ECG‐monitors to provide

intricate HRV‐data. Only recently, some HRV‐analysis methods have sneaked into the clinic by

a ‘stealth’ method, hidden in algorithms that give a generalized ‘alert value’ to a patient

condition, based on observation of a series of vital parameters (11, 25, 29).

In the present study we tried a clinical approach to a problem that has received little

attention in biomedical literature. It has been fairly well established that chronic heart failure

patients (CHF) have different heart rate variability patterns compared to matched healthy

controls (10, 35). However, many of those patients will be on ß‐blocker therapy, which has a

strong influence on both HR and HRV (14, 23, 30, 32). Under these circumstances HRV has

been shown to improve towards normal (27, 30), a fact that might mask an underlying

developing disease status. We therefore tested which HRV‐analysis technique might be able

to give early warning when there is a ‘slipping of’ in the sympathetic direction of the

autonomic balance. To test this we used recordings in CHF patients who went from supine to

upright, as a simple model to reduce vagal outflow to the heart and induce generalized

sympathetic activation. For this study we had a set of 21 recordings in CHF‐patients on

Carvedilol® treatment that has been fully described earlier (39). In view of the practical

applicability in situations where a diagnosis should be available after a short period of

recording only, we were wondering if the 10 minutes in supine posture followed by 10

minutes upright that we had were sufficient to answer two questions:

1) Is there still a distinction health/disease when comparing the supine recordings in ß‐blocker

treated CHF patients to those from matched, healthy controls? And

2) Can HRV‐analysis demonstrate an intra‐individual shift towards a more sympathetic state

when comparing the upright to the supine recording, even in this group of ß‐blocker treated

patients?

The first question is of importance for a quick triage of patients, the second to detect a

deterioration of a patient’s health before a ‘normal’ alarm would sound.

Chapter 4

54

We decided to test a number of obvious linear measures, from the time domain: mean‐IBI

(interbeat interval), SD‐IBI (standard deviation), rMSSD (root mean square of successive

differences of IBI’s), pIBI‐50 (proportion of pairs of successive IBI’s that differ by more than

50 ms) and from the frequency domain: LF, HF and LF/HF (Low Frequency around 0.1 Hz,

High Frequency, i.e. respiratory frequency, mostly around 0.25 Hz, and their quotient). In

view of earlier studies where the use of short recordings for non‐linear analysis has been

analyzed (4, 24, 26, 40), we decided to use SampEn (sample entropy (34) and MSE or

Multi‐Scale Entropy (13)). The latter method computes entropy over progressively coarser

grained versions of the original series. As a by‐product we considered the variances

(Multiscale variance or MSV) and multiscale root mean square of successive differences

(MSD) of those newly constructed series as well.

A successful analysis method or combination of methods should be able to do the triage

(question 1) as well as detect the sympathetic shift (question 2) within the limits of the 20

minute recordings that were available.


55

Methods

Studypopulation

Patients

The patient recordings had been made in the study that has extensively been described in

(39). In short: 21 CHF patients (NY Heart Assoc. classification II‐IV) participated in a study that

was directed to discrimination of ß‐blocker sensitivity depending on the specific ß2‐receptor

subtype that was present in the patient. In a double‐blind cross‐over design they received the

non‐selective ß‐blocker Carvedilol® (Eucardic, Roche, Mijdrecht, Netherlands) or the selective

metoprolol succinate (Selokeen ZOC, AstraZeneca, Zoetermeer, Netherlands) as ß‐blocker for

6 weeks. Both drugs were titrated to equipotent dosages, additionally checked by resting

heart rate. Since recordings made under Carvedilol showed fewer extrasystoles and other

rhythm disturbances, we have restricted our study to the recordings made after 6 weeks on

this drug. It should be mentioned here, that Carvedilol is known to also have α1‐blocking

properties, without intrinsic sympathetic activity (17).

Patients had given their written informed consent after study approval by the local Ethics

committee. Due to problems with too frequent premature ventricular contractions and

erroneous blood pressure tracings, 6 out of the original 21 patient recordings (39) under

Carvedilol had to be rejected. This left data of 15 CHF patients for the present study, 10/5

(male/female), age 58.4 ±6.5 (mean±SD), BMI 27.4±6.0.

Healthycontrolsubjects

Subjects had been recruited by advertisement and selected to match the patient group by

gender, age and ß2‐receptor subtype. They were in good health, free of cardiovascular

disease, non‐smokers. After written, informed consent 34 subjects participated. Due to

technical problems in the recordings and 2 cases of near‐syncope in the stand‐test, 5 out of

the original 34 control recordings had to be rejected. This left 29 (18/11, m/f), age 62.9 ± 7.3

BMI 26.1 ± 4.2 for analysis. There are no significant differences in age and BMI distribution

between healthy controls and CHF patients.

Measurementsanddatapreprocessing

Continuous non‐invasive blood pressure was measured from a finger by the volume‐clamp

technique. A Nexfin® (BMEYE, Amsterdam, Netherlands) hemodynamic monitor was used

Chapter 4

56

with instantaneous display of reconstructed upper arm blood pressure, heart rate, pulse

contour derived cardiac output and systemic vascular resistance. This enabled proper

monitoring during the stand test. To prevent hydrostatic errors the hand was held at heart

level in both positions by a sling around the neck.

Patients and controls underwent a test protocol which included blood draws for clotting

factors in the supine position as described earlier (39). Then they rested for at least 20

minutes before actively standing up. They remained standing for another 10 minutes. From

the Nexfin computed data we only analysed IBI values for the present study, measured to an

accuracy of 5 ms (200 Hz sample rate of A/D conversion).

In view of dysrhythmias like PVC’s, other rhythm disturbances and, occasionally, movement

artefacts that were present in the IBI‐recordings, t hese had to be pre‐processed before

analyses could be performed. We used a two‐step spike‐removal procedure. First we

established the global mean value IBImean‐glb of the whole set (supine or upright), and

substituted any IBIi outside the range 80‐120% of IBImean‐glb by that value. Next, a 10‐beat

window would slide over the recording, replacing any newly added IBIj outside the 80‐120%

range around the IBImean‐local by the value of the local mean. The first step deletes sharp

spikes globally, making it easier for the second step which is required to preserve continuity

of the time series.

CalculationofHRVparameters

Linearmethods

Since we derived heart periods from blood pressure recordings rather than from an ECG, we

cannot call them NN‐intervals (normal to normal) since, strictly spoken, we have no

information on the origin of the heartbeat, whether it originates from the sinus node or from

some other pacemaking site in the heart. Although all patients underwent a test‐ECG just

prior to the present recording, where normal sinus rhythm had been established, we will use

the more general term ‘IBI’ (interbeat interval) instead.

After data pre‐processing as described above we calculated mean‐IBI, SD‐IBI, rMSSD (root

mean square of successive differences), pIBI‐50 (the proportion of intervals that differs by

more than 50 ms from the previous) following the usual methods (37). We chose a period of

at least 5 minutes stable recording for both the supine and upright periods. Of the upright

recording a period of 2 minutes after the standing up maneuver was skipped, to allow for the


57

first transient in blood pressure and heart rate to disappear. This left a period of maximally 8

minutes upright to be included in the computations.

For the frequency analysis we used the IBI data set without interpolation, putting the average

interbeat interval as spacing between heart beats (15). After removal of a linear trend and

Hanning‐windowing we applied a digital Fourier transform (Matlab®) rather than FFT. This

method can be applied to an arbitrary number of data points without the need of

zero‐padding until a power of two has been reached. LF, HF and LF/HF ratio were computed

after integration of the spectral curve from 0.04‐0.15 Hz for LF and from 0.15‐0.4 Hz for HF.

The values were reduced to normalized units by division by the total variance (37).

Non‐linearmethods:SampEnandMSE

The calculation of Multiscale Entropy (MSE) has been fully described in (12). It is the sample

entropy (SampEn) (34) of consecutively coarser grained time series Y constructed from the

original time series X:{x1,…, xi,…,xN} by a scale factor of τ.

The coarse‐graining procedure is the first step to compute MSE, as well as multiscale variance

(MSV) and multiscale successive differences (MSD). By taking τ consecutive values together,

the original signal is progressively ‘smoothed’ and more and more beat‐to‐beat ‘noise’ is

averaged out. This process is visualized in Figure 1 and formalized in formula 1:

./1,1

1)1(

)(

Njxyj

jiij

(1)

This describes a set of consecutively more coarse‐grained time series, {y(τ)} from the series X,

where τ is the scale factor. Next, the SampEn (34) of each time series{y(τ)} is computed,

resulting in MSE. SampEn is a measure of the probability that a sequence of m consecutive

data points will not remain similar (within a given tolerance r) at the next point in the data set.

A high SampEn value implies low regularity, i.e. few repetitions of the same pattern. Details

on how to calculate SampEn can be found in references (13, 34, 42).

In short, MSE aims to measure the complexity of the system. In a totally random sequence,

SampEn will decrease to zero with increasing τ; in a sequence that has been generated by a

system with some degree of complexity, like heart rate over time, it tends to find a stable

non‐zero value.

Chapter 4

58

Linearextension:MSV&MSD

We also computed a side‐product of the coarse‐graining process, i.e. the (multiscale) variance

(MSV) and multiscale rMSSD (MSD) of the newly constructed series Y(τ). We reasoned that

these might show in a simple way the variability of heart rate at medium‐scale time‐intervals.

Statistics

All computations were done by use of SPSS®. After testing for normality, comparisons

between groups were done by pairwise testing using Student’s t‐test or Mann‐Whitney u‐test

where appropriate. For the change to a sympathetic state within one subject, we used the

computed supine value to normalize the upright value. The resulting quotient upright/supine

was then linearized by a log‐transformation before statistical testing.

To compensate for the multiple comparisons we adapted the test magnitude alpha by

applying the Bonferroni‐Holm correction. This will lead to a value of alpha smaller than the

0.05 that we considered significant. The corrected alpha is mentioned in the tables along with

the computed exact p‐value. This allows the reader to judge the significance of observed

changes, taking into account the type I/type II error as well as the biological significance of

the change (9, 31).


59

Figure 1 Schematic diagram of the process of coarse‐graining

This is a representative ~ 10 minutes heart rate recording from a healthy volunteer in supine position. From top to bottom tau =1, 2, 5, 10. X‐scale: item number in the series; Y‐scale: (averaged) duration of heart periods in seconds.

Chapter 4

60

Results

ComparisonCHFpatientsvs.healthycontrols

Table 1 gives an overview of the chosen 11 HRV parameters in patients and control subjects

in the supine posture. It is remarkable that the mean supine heart rates, pIBI‐50 and rMSSD

in the two groups are equal despite the ß‐blockade in the patients. Total variability as

expressed by SD‐IBI is lower in CHF, although not statistically significant. HF as short term

variability index is higher in CHF, but this may well be due to the ß‐blockade (19, 41). As a

consequence LF/HF is significantly lower in CHF as well.

To further analyze the internal structure of the variability we computed, first, the MSE‐curves

for τ=1 to 10, results shown in Figure2.A. At τ=1 SampEn in CHF and controls are equal (Table

1). For values of τ above 3 the curve of the CHF‐patients falls below that of the healthy

controls. However, a large overlap exists. To emphasize the longer range interactions rather

than the short‐term variability (20) we integrated the values for τ from 6 to 10, resulting in

the MSE6‐10 number in Table 1. The variances of the coarse grained distributions for

increasing τ are depicted by the MSV as shown in Figure2.B. Average MSV in CHF is lower

than that in controls for all values of τ; we averaged the value for τ = 6 to 10 as MSV6‐10 in

Table 1. Except for τ = 1 the same holds true for the rMSSD of the coarse grained distributions,

expressed as MSD in Figure2.C and averaged to one value from τ = 6 to 10 in Table 1. Both

MSV6‐10 and MSD6‐10 are lower in CHF than in controls, however, in view of the wide

distribution of the numbers, taking the Bonferroni‐Holm corrected alpha and the magnitude

of the difference into account, we do not consider these differences biologically significant.

The same cannot be said for MSE6‐10: although the number fails to meet the

Bonferroni‐Holm corrected alpha (p=0.05/9=0.006), yet in view of the narrow distribution

and the exact p‐value of 0.015 we do consider this difference significant.


61

Table 1 Parameter comparison between control subjects and CHF patients in 10‐min supine

posture

Parameter (units) CONTROL CHF p value

Bonferroni-

Holm

corrected

alpha

deemed

significant

meanIBI (ms) 1002 ± 158 951 ± 120 0.281 0.02 no

SD-IBI (ms) 38.0 [21.8-87.2] 32.4 ± 15.7 0.090 0.01 no

rMSSD (ms) 25.7 [12.1-125.4] 27.8 ± 14.5 0.795 0.05 no

pIBI-50

(proportion) 0.04 [0.00-0.84] 0.03 [0.00 0.26] 0.586 0.025 no

LF (n.u.) 0.19 ± 0.07 0.13 ± 0.08 0.025 * 0.007 no

HF (n.u.) 0.25 [0.09-0.72] 0.52 ± 0.23 0.000

# 0.005 yes

LF/HF 1.06 ± 0.73 0.36 ± 0.36 0.000

# 0.005 yes

SampEn 1.51 ± 0.37 1.69 ± 0.32 0.152 0.0125 no

MSE6-10 1.57 ± 0.21 1.35 ± 0.39 0.015 * 0.006 yes

MSV6-10 (ms2) 870 [303.4-4210.4] 554 [3.0-1880.8] 0.046 * 0.008 no

MSD6-10 (ms) 24.5 [13.6-75.2] 19.6 ± 11.0 0.016 * 0.006 no

If data are normal distributed, the value is as mean ± std and the Student t‐test is applied; if non‐normal distributed, the value is as median [minimum‐maximum] and the Mann‐Whitney U test is applied. Before Bonferroni‐Holm correction, *p<0.05; #p<0.01; n.u. = normalized units (power in the respective bands is normalized by division by total variance). The values of MSE, MSV, and MSD are computed by averaging over the 5 highest tau values: sum(MSE(tau=6:10))/5, sum(MSV(tau=6:10))/5, sum(MSD(tau=6:10))/5. The column ‘deemed significant’ gives the interpretation of the authors, taking the p‐value, corrected alpha and the biological significance into account; cf. text.

Chapter 4

62

Figure 2 MSE, MSV and MSD curves: comparison between control subjects and CHF patients in supine

posture

A: MSE; B: MSV; C: MSD. Fat (blue) line: control healthy subjects; dotted (red) line: CHF patients. Tau from 1 to 10. The curves represent mean values with +/‐ 1 x standard deviation. CHF patients have lower MSE, MSV and MSD than healthy subjects for tau above 2.


63

Sympatheticshiftincontrolsubjects:uprightvs.supineposture.

We computed the 11 parameters again for the upright condition, and expressed the upright

value as fraction of the individual supine one. For statistical testing the values have been

log‐transformed, to obtain values which follow a normal distribution with mean=0 if the

supine and upright values are equal. The averages and p‐values are shown in Table 2.

As to be expected, mean‐IBI has significantly decreased, i.e. heart rate goes up on standing.

All short‐term variability measures go down as well: rMSSD, pIBI‐50 and HF. The increase in

LF did not reach statistical significance; LF/HF increased in line with the decreased HF.

SampEn and the coarse grained measures, MSE6‐10, MSV6‐10 and MSD6‐10 did not change.

SympatheticshiftinCHFpatients:uprightvs.supineposture.

Generally speaking, the sympathetic change in CHF‐patients followed the same pattern as in

healthy control subjects, without statistically significant differences between the two groups.

However, there were a few notable within‐group exceptions, as shown in Table 2.

In the patients mean‐IBI decreased significantly, so did rMSSD, pIBI‐50, but none of the

frequency analysis measures. Of the non‐linear and coarse‐grained parameters only MSD6‐10

decreased significantly, the other ones did not change appreciably.

AbsolutepowerresultsfromFourieranalysis

Fourier analysis of HRV may be analyzed in many ways; we chose to look at the individual

normalized powers of LF and HF and the LF/HF quotients. However, if the underlying

(absolute) data are very much changed by the intervention (standing up in this case) these

numbers may be misleading. Therefore we checked the absolute powers and in addition

those in the VLF band (very low frequency band: from the lowest observed frequency in the

5‐10 minutes recording to 0.04Hz). The results are given in Table 3. No significant changes in

total power or power in the various bands with standing were detectable. These might have

invalidated the above analyses.

Chapter 4

64

Table 2. Control subjects & CHF patients: Normalized parameters (=upright/supine)

Normalized Parameter

=(upright/supine) CONTROL SUBJECTS p value CHF PATIENTS p value

Mean IBI 0.84 ±0.06 0.000 # 0.90 ±0.06 0.00003 #

SD-IBI 0.98 ±0.36 0.253 0.92 ±0.44 0.130

rMSSD 0.72 ±0.28 0.00001 # 0.69 ±0.24 0.0003 #

pIBI-50 0.36 [0.00-1.75] 0.000 # 0.15 [0.00-2.55] 0.009 #

LF 1.60 ±1.65 0.123 1.19 ±0.92 0.635

HF 0.89 ±0.55 0.019 * 1.00 ±0.71 0.279

LF/HF 2.87 ±3.36 0.008 # 2.01 ±2.91 0.723

SampEn 0.96 ±0.30 0.066 0.94 ±0.29 0.199

MSE6-10 1.02 ±0.18 0.256 0.98 [0.31, 1.63] 0.609

MSV6-10 1.39 ±1.17 0.989 1.55 ±2.42 0.540

MSD6-10 1.01 ±0.48 0.314 0.76 ±0.22 0.001 #

If data are normal distributed, the value is as mean ± sd and a one‐sample t‐test is applied; if non‐normal distributed, the value is as median [minimum‐maximum] and a Wilcoxon signed‐rank test is applied. Normalized parameters are calculated as: (value of upright)/ (value of supine) for every individual. After log‐transformation a one‐sample t‐test has been used to test the deviation from zero

(i.e. upright value = supine value); *p<0.05; #p<0.01(within‐group differences for upright to supine) ; the values of MSE, MSV, and MSD are as in table 1: sum(MSE(tau=6:10))/5, sum(MSV(tau=6:10))/5, sum(MSD(tau=6:10))/5.

Table 3. Total power and power in the various bands: VLF, LF, HF; supine values compared to upright.

Parameter

(ms2)

Control subjects (median [min-max]) CHF patients

Supine Upright Supine Upright

Total 1445 [474-7602] 1437 [226-9627] 1162 [44-2974] 677 [59-2823]

VLF 770 [271-4477] 934 [133-5147] 265 [1-2106] 322 [0-2086]

LF 243 [34-1804] 235 [43-3185] 116 [6-646] 75 [2-537]

HF 344 [44-4902] 189 [49-1295] 464 [27-1561] 238 [57-1036]

In view of the non‐normal distributions of absolute powers the medians and ranges are given. No within‐group significant changes from supine to upright can be demonstrated due to the large variability.


65

Discussion

The present computer‐based post‐hoc study tried to establish the numbers that could aid in

fast diagnosis of a ß‐blocker treated patient’s ‘slipping off’ into a more sympathetic state. We

chose a stand‐test as model for this condition; no one can stand very well for 10 minutes

without sympathetic system involvement in view of the induced drop in blood pressure at the

level of the carotid sinuses and the relative hypovolemia that is observed by pressure

sensitive receptors in the low‐pressure area (atria, lungs) (6, 38).

We reasoned that patients on a ß‐blocker, when remotely monitored or admitted to an

intensive care unit for acute exacerbation of symptoms, might pose additional challenges to a

monitoring system that would incorporate HRV‐measures in an intelligent alarm.

Beta‐blockers have a tendency to increase short term HRV as well as total background

variability as it may be observed in the low to very low frequency ranges (2, 8, 19). Moreover,

the additional α1‐blocking properties of Carvedilol may lead to less apparent blood pressure

waves when sympathetic arousal takes place. This property has been pointed out as

instrumental in not lowering HR as much as do other ß‐blockers (36), as compensation for the

decreased systemic resistance that it provokes (18).

In line with our initial suppositions we found that in the supine resting state the CHF‐patients

differed from the healthy control subjects by showing equal HR with almost equal SD‐IBI, but

significantly higher HF variability, therefore lower LF/HF ratio. Furthermore the patients had

slightly lower values for MSE6‐10, MSV6‐10 and MSD6‐10. In our view this is mirroring the

increased beat to beat variability due to the ß‐blocker together with a slightly increased

sympathetic activation. When going to the upright posture the control subjects displayed

most of the expected changes: increased HR, decreased rMSSD, pIBI‐50, HF, increased LF/HF,

but not an increased LF, probably due to the large variance in this measure. MSE6‐10,

MSD6‐10 or MSV6‐10 did not record a change. When it came to the CHF‐patients in upright

posture the parameters that did show up as useful were HR (increased), rMSSD, pIBI‐50 and

MSD6‐10 (decreased). None of the other parameters would indicate a shift into a

sympathetic state. Our results in the healthy control group tally well with those of

Turianikova et al. (40) who recently published a comparable orthostasis study. Exception is

our lack of results for MSE6‐10; in comparison we would have expected a definite increase.

However, we studied subjects around 63 years of age, the earlier study had subjects around

20 years of age.

Chapter 4

66

A few notes should be made; the most important one being that almost all HRV is vagally

mediated: both the fast beat‐to‐beat changes and the slower waves that may be riding on

underlying blood pressure variations. As long as heart rate is in the vagal range, for humans

below (120 – 0.6 * age) (22), most variations in HR will, as first guess, mainly come from

changes in vagal activity. That is not to say that the sympathetics play no role in HRV, their

contribution can be found both in the underlying blood pressure variability and the

longer‐range variations in heart rate. These slower variations, to be observed over the course

of minutes to hours, may influence both HR and BP at the same time, via central and

peripheral mechanisms. In analysis techniques that aim at this ‘system complexity’ it has

become established that stable estimates may only be found when thousands of heart beats

are incorporated, an order of magnitude requiring at least some 4 hours observation. This is a

requirement that is impractical for straightforward clinical monitoring. Although 4 hours of

data may become available in any patient on the monitor, a deterioration of condition should

be signaled earlier than after 4 hours.

In recent years the literature on HRV analysis methods has been reviewed for various areas of

application. Rajendra et al. (33) gave a more or less complete overview of methodologies that

are applied, from time domain to frequency domain t to non‐linear analysis methods.

Generally speaking, the main disadvantages of the latter are the large number of data

required and the sensitivity to baseline shift and noise of some of the methods. In 2007

Maestri et al. (28) reviewed the use of non‐linear indices of HRV for CHF patients. Many were

highly correlated to classical linear indices; only two (families of) analysis methods gave

independent prognostic information, i.e. empirical mode decomposition and symbolic

dynamics. We considered these not practical for our purpose. Again in 2009 Buccelletti et al.

(7) noted that techniques like power law (fractal) analysis or detrended fluctuation analysis

were less practical in the prognosis of myocardial infarction patients than entropy directed

methods in view of the number of required heart beats. In the present study we have,

therefore, restricted our analysis to SampEn and MSE, being the most promising ones for our

application. We looked at scale factors 6‐10 for MSE, supported by a recent study by Ho et al.

(20) who had noticed that in CHF patients on ß‐blockade values of τ=6 and up were

insensitive to this therapy when used as predictors of mortality.

In short, the most reliable HRV parameters to early detect a patient’s ‘slipping off’ into a

sympathetic state are those that indicate so‐called vagal withdrawal, i.e. the disappearance

of short‐term variability and the loss of longer term ‘jumpiness’ as shown by the decreased


67

MSD6‐10. These changes will occur even before heart rate goes up into a definite

sympathetic region, where all vagal efferent traffic is silenced. The newer parameters like

SampEn or MSE have no use here, at least not in the present group of patients who use

ß‐blockers. A study by Batchinsky et al. (4) has shown that SampEn can make a difference for

triage in emergency care, even when only short recordings are available. Interestingly, the

newly introduced parameter MSD6‐10 seems to do a good job as well, detecting both the

sympathetic shift and the difference between healthy controls and CHF‐patients. This

computes the ‘jumpiness’ or rMSSD of coarse grained averages over 6 to 10 adjacent beats.

This is not a non‐linear parameter like MSE or SampEn, but one that is derived from the

intermediate coarse grained series constructed for the computation of MSE. In that same

vein we computed the variances of these series, which showed some promise in the

controls‐CHF comparison of Table 1, but failed to show a sympathetic shift (in Table 2).

rMSSD has peculiar properties, acting as a high‐pass filter to the original heart rate signal. It

has been proven (5) that ‘classical’ rMSSD captures the same frequency range as the HF band

in frequency analysis does, roughly between 0.2 and 0.45 Hz. However, it is biased by the

prevailing heart rate and is sensitive to lower frequencies as well. By extending the algorithm

to progressively more coarse grained series of heart periods we have, with MSD6‐10,

constructed a combination of a low‐pass filter (the coarse‐graining process, cf. Figure1)

followed by a high‐pass filter. Building on the earlier study into the rMSSD filter properties

one may extrapolate that the number represented by MSD6‐10 will favour frequencies

between 0.02 and 0.07 Hz (i.e. 0.2/10 and 0.45/6 as 3dB points), thus mainly spanning the

LF‐band and slightly lower, as illustrated in Figure3, the result of a simulation like in the

Berntson study. It should be noted that these filter characteristics are dependent on the

prevailing heart rate. In the present simulation, as in our study, we assumed an average heart

rate of 60/min, 1 second intervals. This problem of scaling by heart rate is one that is

omnipresent in MSE‐studies, although very seldom mentioned.

Inconclusion:

For patients on ß‐blockers only the gradual disappearance of short‐term variability, as

measured by traditional methods rMSSD and pIBI‐50, proved a reliable indicator of a shift to

a sympathetic state. The newly introduced MSD6‐10 – jumpiness in coarse grained beat

series – shows some promise here as well. HRV analysis cannot work in clinical monitoring

without taking HR‐active medication into account; without ß‐blockade more parameters

might be useful, notably SampEn and frequency analysis may carry useful information for the

Chapter 4

68

clinician. The best application for these measures is probably their use in intelligent

monitoring, where the clinician is not bothered with the numbers and their intricacies, but

just with the condition changes that are shown by analysis of HRV along with other vital

parameters.

Figure 3 Band‐pass filter characteristics of MSD6‐10.

The algorithm has been applied to model‐generated beat‐series with additional noise. A simplified

model of baroreflex control has been used as in DeBoer et al., 1987 to generate the intervals. Average

heart period around 1000 ms. A modulating ‘respiratory’ frequency was forced with periods from 3 to

120 seconds. The values for MDS6‐10 have been normalized to the peak‐peak amplitudes of the forced

oscillations (Berntson et al., 2005).


69

Limitations

This study was conducted in a small number of relatively healthy CHF‐patients: they had been

stable on their medication before entering the study. The circumstances for patients

admitted to the ICU for an acute cardiac condition may be quite different. Therefore this

study should be extended to real‐life ICU‐recordings and to larger groups of patients before

definite conclusions about the usefulness of the present HRV‐measures may be reached.

In a standard ICU‐setting one would turn to the ECG‐monitor for more accurate heart period

detection than was possible here. The use of a 200 Hz sampled BP‐recording limits the

accuracy to ~ 5 ms, whereas normally at least 1 ms should be obtainable. Therefore some

measures that came out as rather insensitive now, like SampEn or MSE for low values of τ

might perform better then. For ‘classical’ MSE the present recordings were too short anyway,

reason why we limited the τ (coarse graining parameter) to 10 rather than going to 20. At

τ=10 we have in a 10 minutes recording about 60 points for the coarse grained series. Since

our τ‐MSE curves reached stable levels for the data sets that we used, we considered this

choice appropriate.

Disclosures

None of the authors has any relevant disclosures to make. Ms. Zhang, PhD is the recipient of

a post‐doc training grant from the Dept. of Physiology.

Chapter 4

70

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Appendix

List of abbreviations

BMI: body mass index;

CHF: chronic heart failure;

HF: high frequency;

HRV: heart rate variability;

IBI: interbeat interval;

ICU: Intensive Care Unit;

LF: low frequency;

mean‐IBI: mean interbeat interval;

MSD: multiscale root mean square of successive differences;

MSE: Multi‐Scale Entropy;

MSV: multiscale variance;

PVC: premature ventricular contraction;

pIBI‐50: proportion of pairs of successive IBI’s that differed by more than 50 ms;

rMSSD: root mean square of successive differences of IBI’s;

SampEn: sample entropy;

SD‐IBI: standard deviation of interbeat intervals;

VLF: very low frequency

75

Chapter5 AsubgroupofBrugadapatientsshowsloworthostaticbloodpressuresasasignofdecreasedsympatheticoutflow


(to be submitted)

The results in this chapter have been obtained by Y. Zhang’s analysis of recordings

made by DA van Hoeijen, M.D. and MT Blom, M.D. in patients of HL Tan, M.D, PhD. Dr.

Tan is cardiologist and Principal Investigator at the Heart Failure Research Center of

the AMC.

A full, final report of this study will follow.

Chapter 5

76

Abstract

Background: Patients with Brugada syndrome have an increased risk to develop

life‐threatening ventricular fibrillation (VF). Various studies have shown involvement of the

autonomic nervous system in eliciting these attacks. We tested autonomic control of the

circulation in a 10 minutes supine, 10 minutes upright standing test in 28 established Brugada

patients and compared those to 25 healthy, matched control subjects.

Methods: Measurement of continuous non‐invasive blood pressure. Analysis of heart rate

variability, changes in blood pressure, baroreflex sensitivity and pulse‐contour derived

cardiac output and systemic vascular resistance in the upright posture.

Results: In addition to low values of LF/HF in supine posture, indicating relatively increased

vagal outflow, we found a subgroup of Brugada patients who showed low values of

upright/supine diastolic blood pressure, caused by low increase or even decrease in

estimated systemic vascular resistance in the upright posture.

Conclusions: A subgroup of Brugada patients exhibits low sympathetic outflow in the upright

posture. This may lead, on longer duration standing, to an increased risk of vasovagal

syncope. An association with increased risk of VF remains to be established.

Autonomic nervous system in Brugada

77

Introduction

After its first description in 1992 (3), the Brugada syndrome (BrS) has been recognized as a

major risk for sudden death in young adults, particularly in some south‐east Asian countries

(1). BrS patients may develop a variety of cardiac arrhythmias; the worst of those being

ventricular fibrillation (VF). In some BrS patients the ECG shows the characteristic

‘saddleback’ ST elevation (23); however in many patients this may turn up only after

pharmacological provocation (12). In recent years a relationship between symptomatic BrS

and autonomic nervous system (ANS) imbalance/‐dysfunction has been suggested, with

increased parasympathetic activity and withdrawal of sympathetic activity (14, 15, 22) or

even presynaptic sympathetic dysfunction (14, 15, 22) as risk factors in established BrS. Some

of patients have syncopal events which are due to neurogenic (vasovagal) syncope; this is

considered a benign condition (20). If, however, the syncope cannot (retrospectively) be

characterized as such, due to lack of prodromal events, the association with self‐terminating

VF becomes more likely, necessitating therapeutic intervention like the implantation of an

ICD(18).

In the present study we included 28 Brugada patients who came to the outpatient clinic of

the AMC between March 2011 and August 2012 for a full evaluation of their disease status,

including assessment of their ‘autonomic balance’ as judged from the heart rate variability in

LF/HF and other parameters in a simple test designed to mimic every‐day life conditions.

Patients were 10 minutes in a supine resting condition followed by 10 minutes active

standing while continuous finger blood pressure was measured. The patients’ recordings

were compared to those from 25 matched healthy control subjects. The ultimate goal of this

pilot study was to decide which of the parameters to evaluate ANS activity may be useful to

point out those BrS patients who might be more at risk due to abnormal autonomic control of

the circulation (14‐16).

Methodsandmaterials

Patients,controlsubjectsandrecordingsWe included 28 Brugada patients who were consecutively admitted between March 2011

and August 2012 to the outpatient clinic of the Academic Medical Center for assessment of

their disease status. In addition to a full cardiologic evaluation, including a pharmacological

provocation test to establish the existence of a Brugada(‐like) condition, they underwent an

autonomic function test by 10 minutes supine resting followed by 10 minutes active standing.

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Continuous non‐invasive finger blood pressure was measured by a Nexfin® cardiovascular

monitor (BMEye, Amsterdam, The Netherlands). The hand was held at heart level, when

necessary supported by a sling around the neck while in the standing position. Nexfin shows

continuous reconstituted upper arm blood pressure, heart rate (HR) and online computed

stroke volume (SV) by pulse contour analysis; from HR*SV cardiac output (CO) and, finally, it

computes systemic vascular resistance (SVR) from mean arterial pressure (MAP) divided by

CO. These online presented parameters helped in patient monitoring during the stand test

and they were used for offline evaluation.

Since this supine/stand test has been in use in the Academic Medical Center for a couple of

years now, an anonymized set of recordings in healthy control subjects is available who

originally had been recruited for various (other) studies. All controls are non‐smokers in good

health, free of cardiovascular disease. We matched 25 subjects from this set for age and

gender to the Brugada patients.

All subjects participated after written informed consent; the study protocol had been

approved by the local review board. This also holds true for the subjects whose recordings

were chosen from the anonymized database.

AnalysesSince we measured the heart period as the interval between blood pressure waves, we use

the term interbeat interval or IBI (in seconds); instantaneous HR is calculated as 60/IBI. From

steady state periods in the supine and upright recordings we derived the classical measures,

i.e. mean‐IBI, SD‐IBI (standard deviation), rMSSD (root mean square of successive differences),

pIBI‐50 (fraction of pairs of successive IBI’s that differ by more than 50 ms). Finally we

performed frequency analysis of HR variability over those periods, resulting in a contribution

of low frequency LF (0.04‐0.15 Hz) and of high frequency HF (0.15‐0.4 Hz) and the quotient

LF/HF as measure of vasovagal balance (21). From the Nexfin cardiovascular monitor data we

derived for the same periods the averaged blood pressure data (per beat: systolic, diastolic

and mean pressure) and CO, SV, and SVR. From the coherent power in LF for systolic

pressures and IBI we computed baroreflex sensitivity (BRS) in ms/mmHg (4).

To allow for age‐related effects, subjects were divided into two groups as follows:

20 ≤ age < 40 and 40 ≤ age < 60. On the basis of their clinical background, BrS patients were

classified in one of three groups: asymptomatic, syncopal (including vasovagal syncope), i.e.

having experienced at least one syncopal event, and VF, if one or more documented VF’s had

occurred. An overview of basic data is presented in Table 1.


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Table 1 Demographics

[20,40) yrs [40,60) yrs

Control Asympt Syncopal Control Asympt Syncopal VF

Group size 12 4 6 13 7 8 3

Age (years) 31.1±5.9 27.5±5.2 30.8±7.1 51.1±6.9 52±2.1 48.5±5.3 53.7±6.8

Gender (m/f) 10/2 4/0 2/4 10/3 3/4 7/1 3/0

BMI 24.4±3.3 22.6±3.7 22.3±2.2 25.7±3.8 28.1±6.3 25.3±2.2 24.1±3.0

Ratiobetweensupineanduprightstate

To quantify the change in autonomic condition with standing, the ratios between standing

and supine parameters were calculated. The distributions of the resulting quotients

upright/supine and LF/HF were normalized by a log‐transformation before statistical testing.

Statistics

For the various parameters averages ± SD are given, unless the underlying distribution is

not‐normal, then the median and range [min‐max] are presented. After testing for normality,

groups were compared using a one way ANOVA or the Kruskal‐Wallis H test where

appropriate. In case of significant differences (p<0.05) a post‐hoc test was used to determine

which two groups are significantly different.

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Results

ComparisonbetweenBrSpatientsandhealthycontrolsinsteadystatesupineanduprightposture

Table 2 summarizes all parameters in patients and control subjects in the supine and upright

posture. The only significant differences between patients and healthy controls were found in

the younger age group in the supine position [20, 40). Post‐hoc analysis showed that the

symptomatic BrS patients had significantly lower LF, higher HF and, accordingly, a lower

LF/HF than healthy controls. Moreover, supine SVR was significantly lower in healthy controls

and standing blood pressure was, albeit marginally, lower in the younger patient group as

well. The supine values of asymptomatic young patients were in‐between those of healthy

controls and symptomatic patients; therefore they did not differ significantly from either

group.

Comparisonoftheuprighttosupineratios

The upright/supine ratio defines the reaction of a person’s cardiovascular control system to

this daily challenge. If the supine value is maintained, the ratio will be 1, decreases in the

upright posture result in ratios lower than 1. Table 3 summarizes all upright/supine ratios.

Striking is the (slight, but significant) lowering of upright blood pressure in the group of BrS

patients rather than the normal response, i.e. the increase that is observed in the control

group. When BrS‐patients stand up, their HR is increased and SV is decreased, as in healthy

controls; however in the younger age group [20, 40) CO is significantly increased in contrast

to healthy controls and at the same time their SVR is not increased, but even decreased. In

the older age groups these effects are tending in the same direction, but do not reach

statistical significance to the same degree. Of note, only one patient had presyncopal signs

after 8 minutes of standing, and had to return to the sitting posture. This patient belonged to

the older group and was known to have experienced both VF’s and vasovagal syncope’s.

This combination of effects points to a changed cardiovascular control in (some of) the BrS

patients. Figure 1 shows how these statistical effects on LF/HF and diastolic pressure (Pdia)

come into being: there is subgroup of patients who have a combination of low Pdia‐ratio (i.e.

the value upright/supine) and low supine LF/HF. The histogram of LF/HF values for patients is

only shifted to lower values compared to controls; the histogram of Pdia‐ratios in patients

shows a bimodal distribution. Figures 2A and 2B demonstrate these effects. Having a

Pdia‐ratio < 1.0 is not restricted to the younger age group of BrS patients: as Figure 3

demonstrates, neither is it restricted to any patient subgroup, as shown in Table 3.


81

Figure 1 LF/HF values as a function of the ratio upright/supine diastolic blood pressure. Red triangles: BrS patients; grey squares: healthy controls. Dividing line at Pdia‐ratio = 1.0 separates the low responders to the left from the normal responders to the right. In the lower left corner of the figure a conspicuous group of patients is observed, with both low LF/HF and low Pdia‐ratio.

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Table 2 Hemodynamics and autonomics

[20,40) yrs [40,60) yrs

Supine control asympt syncopal p-value control asympt Syncopal VF p-value

Sys 119±12 121±17 127±6 0.429 125±17 146±15 127±22 126±12 0.094

Dia 70±8 75±10 76±5 0.356 70±7 79±8 76±10 68±4 0.092

MAP 88±9 91±13 94±5 0.383 92±10 103±8 96±14 89±6 0.118

SV 110±15 102±11 99±15 0.311 95±13 94±22 91±15 105±10 0.618

CO 7.5±1.2 6.7±1.5 6.3±1.2 0.154 5.3 [4.0-9.0] 5.3±1.3 5.7±1.4 6.1±1.4 0.830

SVR 964±179 1116±213 1238±250 0.044 1332±358 1482 [1126-2941]

1404±294 1199±206 0.486

dP/dt 836±182 1010±210 924±147 0.237 877±263 1000±212 882±357 903±140 0.794

IBI 0.90±0.17 0.94±0.12 0.97±0.20 0.718 1.01±0.21 1.07±0.07 0.94±0.19 1.06±0.16 0.578

SD-IBI 0.05±0.02 0.04±0.01 0.05±0.02 0.813 0.05±0.02 0.05±0.03 0.03±0.004 0.06±0.01 0.128

rMSSD 0.03±0.02 0.04±0.02 0.05±0.02 0.523 0.04±0.03 0.04±0.02 0.03±0.01 0.04±0.02 0.540

pIBI-50 0.17±0.17 0.22±0.23 0.32±0.24 0.347 0.21±0.23 0.17±0.18 0.09±0.12 0.18±0.14 0.745

BRS 13 [9-34] 10±2 12±4 0.262 8±5 8±6 8±4 12±4 0.675

LF 0.33±0.13 0.25±0.06 0.15 [0.14-0.32]

0.023 0.21 [0.07-0.69]

0.13 [0.09-0.46]

0.19±0.12 0.2±0.10 0.982

HF 0.22 ±0.07 0.33 [0.30-0.55]

0.46±0.14 0.002 0.31±0.20 0.31±0.16 0.26 [0.12 0.78]

0.10±0.04 0.156

LF/HF 1.66±0.88 0.74±0.29 0.34 [0.20-1.19]

0.006 0.77 [0.21-4.86]

0.86±0.78 0.79±0.60 1.98±0.16 0.221

Upright

Sys 124±10 114±15 112±6 0.046 128±16 130±13 123±21 129±14 0.842

Dia 80±6 73±6 73±5 0.055 78±5 76±8 78±9 78±8 0.917

MAP 96±8 88±8 87±5 0.050 97±9 95±8 95±14 102 [84-102]

0.833

SV 84±15 85±8 69 [61-103] 0.083 74±11 80±22 74±14 76±8 0.813

CO 7.0±1.2 6.9 [6.2-7.8]

6.5±1.2 0.630 5.6±1.2 5.5±1.2 5.2±1.2 5.7±1.2 0.900

SVR 1119±202 1012±48 1100±176 0.596 1455±341 1458±369 1497±306 1368±172 0.952

dP/dt 960±224 987±273 845±171 0.514 928±282 934±215 902±323 1031±215 0.922

IBI 0.73±0.12 0.74±0.08 0.69±0.17 0.793 0.81±0.14 0.87±0.08 0.87±0.18 0.81±0.09 0.717

SD-IBI 0.04±0.01 0.04±0.01 0.04±0.02 0.759 0.05±0.02 0.04±0.01 0.04±0.01 0.05±0.02 0.624

rMSSD 0.02±0.01 0.02±0.01 0.02±0.01 0.795 0.03±0.02 0.02±0.005 0.02±0.004 0.02±0.01 0.881

pIBI-50 0.03 [0.005-0.18]

0.03±0.02 0.02 [0.003-0.22]

0.681 0.04 [0.002-0.42]

0.03±0.03 0.03±0.02 0.03±0.04 0.826

BRS 7±1 8±2 6±3 0.478 5±3 4±2 5±2 5±3 0.845

LF 0.40±0.11 0.47±0.06 0.25 [0.22-0.48]

0.175 0.34±0.19 0.33±0.17 0.36±0.18 0.31±0.17 0.986

HF 0.09 [0.04-0.34]

0.13±0.04 0.19±0.08 0.395 0.15±0.07 0.20±0.14 0.16 [0.07-0.34]

0.05±0.01 0.074

LF/HF 4.5±3.4 3.96±1.77 2.01±1.04 0.214 2.43 [0.98-7.07]

3.35±3.41 3.35±3.23 6.66±4.59 0.469

Sys: systolic pressure [mmHg]; Dia: diastolic pressure [mmHg]; MAP: mean arterial pressure [mmHg]; SV: stroke volume [mL]; CO: cardiac output [L/min]; SVR: systemic vascular resistance [dyn·s/cm5]; dP/dt: maximal value of the time-derivative of pressure [mmHg/s]; IBI: interbeat interval [s]; BRS: baroreflex sensitivity [ms/mmHg]; LF: Low Frequency power in HRV [nu] ; HF: High Frequency HRV [nu] ; LF/HF: quotient.


83

Table 3 Hemodynamics and autonomics: upright / supine ratios

[20,40) yrs [40,60) yrs

control asympt syncopal p-value control asympt syncopal VF p-value

Sys 1.05±0.06 0.94±0.03 0.88±0.05 0.000# 1.03±0.08 0.89±0.05 0.97±0.12 1.02±0.04 0.019 *

Dia 1.14±0.097 0.99±0.06 0.97±0.06 0.000 # 1.12±0.09 0.97±0.09 1.03±0.10 1.15±0.06 0.004 #

MAP 1.09±0.06 0.97±0.05 0.93±0.04 0.000 # 1.06±0.07 0.92±0.07 1.00±0.11 1.08±0.05 0.005 #

SV 0.77±0.10 0.83±0.10 0.74±0.09 0.320 0.78 ±0.08 0.85 ±0.10 0.82±0.08 0.72 ±0.04 0.145

CO 0.94±0.07 1.06±0.15 1.04±0.11 0.047 * 0.96 ±0.09 1.04 ±0.09 0.93 ±0.08 0.94 ±0.02 0.088

SVR 1.16±0.09 0.93±0.15 0.90±0.13 0.000 # 1.11 [0.97-1.52]

0.90 ±0.12 1.08 ±0.15 1.15 ±0.09 0.026 *

dP/dt 1.15±0.13 0.97±0.09 0.92±0.12 0.002 # 1.07±0.16 0.94±0.15 1.07±0.25 1.14±0.06 0.337

IBI 0.82±0.09 0.79±0.07 0.71±0.08 0.084 0.81 ±0.07 0.82 ±0.08 0.88 ±0.07 0.77 ±0.04 0.076

SD-IBI 0.96±0.27 0.99±0.32 0.86±0.26 0.718 0.94 ±0.31 0.85 ±0.34 1.11 ±0.23 0.83 ±0.16 0.493

rMSSD 0.73±0.23 0.62±0.22 0.45±0.19 0.067 0.70 ±0.32 0.62 ±0.19 0.78±0.23 0.52 ±0.09 0.481

pIBI-50 0.55±0.44 0.27±0.25 0.27±0.33 0.278 0.30 [0.01-2.22] 0.17 [0.04-1.11]

0.58 ±0.50 0.16±0.12 0.423

BRS 0.50±0.16 0.85±0.33 0.50±0.14 0.076 0.67±0.16 0.45 [0.35-2.34]

0.74±0.19 0.39±0.14 0.042 *

LF 1.00 [0.61-3.67]

1.94 ±0.59 2.01 ±1.16 0.476 1.58[ 0.82-8.63] 1.93 ±0.93 2.25±1.10 1.55 ±0.43 0.544

HF 0.57 [0.20-2.22]

0.37 ±0.15 0.47 ±0.33 0.554 0.63 [0.12 2.82] 0.41 [0.38-1.63]

0.56 [0.11-2.43]

0.45 [0.45-0.93]

0.949

LF/HF 2.44 [0.34-15.03]

6.13 ±3.14 6.44 ±4.55 0.316 4.10 ±3.57 3.91 ±2.77 4.47 [0.64-31.5]

3.33 ±2.34 0.990

Abbreviations as in Table 2 In age group [20, 40), by one-way ANOVA test there is significance in Sys (p=0.000), Dia (p=0.000), MAP (p=0.000), CO (p=0.047), SVR (p=0.000), and dP/dt (p=0.002).

# Sys after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.009), between control and syncopal group (p=0.000).

# Dia after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.002), between control and syncopal group (p=0.000).

# MAP after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.003), between control and syncopal group (p=0.000).

# SVR after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.004), between control and syncopal group (p=0.000).

# dP/dt after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.043), between control and syncopal group (p=0.003).

* CO after post-hoc test (Gabriel test) no significant difference between two groups. In age group [40, 60), by one-way ANOVA test (or one-way Kruskal-Wallis test) there is significance in Sys (p=0.019), Dia(p=0.004), MAP(p=0.005), SVR (p=0.000), and BRS (p=0.042).

# Dia after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.006), between asympt and fibr group (p=0.035).

# MAP after post-hoc test (Gabriel test) significant difference between control and asympt group (p=0.005). * SVR after post-hoc test ((t-test/Mann-Whitney test)) significant difference between control and asympt group (p=0.005),

between asympt and Vfib group (p=0.012). (t--test/Mann-Whitney test as post-hoc test p<0.008 as significance).

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Figure 2 A: Histograms of Pdia‐ratios for BrS patients (red bars) and healthy controls (grey bars). To linearize the distribution we computed the 2log of the values. The patients show a bimodal distribution, with a subgroup definitely below 0, indicating a ratio < 1.0, the histogram of healthy controls is unimodal and above zero. B: Histograms of LF/HF for BrS patients and healthy controls. Values have been linearized by 2log‐transformation. The two distrbutions show no obvious differences other than that the patients have lower values. Colors as in Figure2A.


85

Figure 3. Pdia‐ratios as a function of age for BrS patients and healthy controls. Colors as in Figure1. BrS patients with ratios below 1.0 are observed at all ages. Only 2 healthy controls have ratios around the divisor line at Pdia‐ratio = 1.0.

Discussion

This study was set up to test which parameters that evaluate the autonomic nervous system

can be used to single out those BrS patients who might be more at risk due to abnormality in

their autonomic control of the circulation. The, as yet unproven, idea being that the patients

whose numbers deviate most from healthy controls might be those that are most at risk for

the fatal complications of the disease. Patients describe the odds against them as ‘one strike

you’re out’ and, although not every tachycardia attack ends in unstoppable VF, there is truth

in that saying.

In literature earlier studies have pointed to over‐activity of the parasympathetic system,

particularly during sleep, as involved in eliciting a fatal attack (2, 7, 10, 13). Our study was

limited to 2x10 minutes recording during daytime as part of a diagnostic workup. Even in this

short period a subgroup of patients in the younger group stood out as having a low supine

LF/HF ratio, as expression of increased parasympathetic activity (Table 2). But in addition the

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same subjects exhibited another peculiarity in cardiovascular control: their upright diastolic

pressure did not rise, even decreased in the upright posture (Table 3, Figures 1‐3). The same

pattern in upright diastolic pressure regulation was observed in the older group (Table 3,

Figure 3). The underlying abnormality is an insufficient increase in systemic peripheral

resistance, as shown in Table 3. Even though the resistance effects were stronger than the

pressure effects, we chose to emphasize the diastolic pressure effects, as being better suited

for general praxis: even with a simple sphygmomanometer this can be measured, as long as

supine and upright pressure are measured taking caution to keep the arm cuff at heart level

both supine and upright.

This study confirms earlier observations that showed a decreased sympathetic (cardiac)

activity in BrS patients (15, 17, 20) and extends it to decreased vasomotor activity. In the

standing posture sympathetic activity to the resistance vessels must, of necessity, increase to

maintain sufficient blood pressure at the level of the brain (9, 21). Alternatively, orthostatic

cardiac output should increase, or at least not decrease as much as it mostly does (6, 19).

Upregulation of blood pressure is both by way of the systemic baroreflex and the

‘low‐pressure’‐reflex. The carotid sinus pressure receptors, being above heart level, signal a

lowering of standing blood pressure and consequently vagal outflow to the heart is

diminished (HR rises) and inhibition of sympathetic outflow is lessened. The low‐pressure

receptors in the pulmonary vessels and atria are also less stimulated by the decreased venous

return, which results in sympathetic activation as well. The present study does not allow a

further interpretation of the lack of sympathetic outflow in BrS patients. The baroreflex

sensitivity (BRS) is not changed as shown in Table 2. However, that number just relates to the

vagal effect on heart rate and is only remotely related to the sympathetic effects on the

vasculature. Alternatively we should consider an overall decrease in sympathetic drive in BrS

patients, which might tally with the observed increase in vagal outflow and low LF/HF in the

supine position (Table 2, Figures 1 and 2B)

In earlier studies an increased propensity to orthostatic syncope in BrS patients has been

documented (8, 24). These authors used a classical tilt table test, till actual syncope was

induced; maximum 45 minutes of standing, followed by sublingual nitroglycerine spray.

Makita et al.(11) showed a novel SCN5A mutation that gave rise to a Brugada‐type ECG

combined with frequent neurocardiogenic syncopal attacks. In the broad spectrum of genetic

diseases that go under the name of Brugada syndrome this underpins the relationship

between the ECG‐abnormalities and other changes in cardiovascular control.


87

In a subgroup of BrS‐patients orthostatic blood pressure is maintained at a lower level than in

control subjects. Question is, if this sign is indicative of them being more at risk than other

bearers of the syndrome. It might predispose this subgroup to orthostatic syncope; however,

as yet we do not know if this implies a predisposition to VF’s as well.

Limitations

This study includes only 28 patients, the results should be confirmed in a larger group before

measurement of the upright/supine Pdia‐ratio may become a standard part of BrS‐workup.

Moreover, follow‐up studies should show the significance of the present findings: it might

very well be that the observed alteration in orthostatic blood pressure control is unrelated to

the risk of VF in BrS‐patients. Finally, when judging orthostatic BP the reference level is of

paramount importance. Any error in the measurement, as it might be induced by keeping the

measured finger or arm above or below level of the aortic valves, may result in spurious

outcomes. Since all investigators involved, both for the control group and the BrS‐patients,

have been trained by the same instructor (JMK) on how to perform the supine‐stand test, we

consider this unlikely.

Conclusions

This study was designed to find indices of ANS activity that may be of use in the risk

assessment of Brugada patients. We found a subgroup of Brugada patients who have lower

than normal upright/supine diastolic blood pressure ratios, due to a deficient increase in

systemic vascular resistance. This points to an alteration in cardiovascular control that may

add to the well‐known problem of increased vagal outflow, all increasing the risk of a fatal VF.

This outcome requires follow‐up studies to determine the value of this newly found trait for

general risk assessment. We used a setting that can easily be copied: 10 minutes supine

followed by 10 minutes active standing, measure at heart level a number of blood pressure

values in each posture to compute reliable averages. We consider a Pdia‐ratio lower than 1.0

indicative of poor cardiovascular control.

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Chapter6 DynamicsofPulsePressureVariabilityandtheDifficultyofPredictingFluidResponsiveness.


(to be submitted)

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92

Abstract

Background: In the intensive care setting intravenous fluid is often administered to prevent

or correct hypotension. However, not all patients will respond favourably to a fluid load.

Measurement of respiration induced pulse pressure variability (PPV) is currently under study

as method to predict ‘fluid responsiveness’ (FR). In this study we tested PPV as predictor for

FR in healthy (awake) test subjects and compared their responses to a computer model of the

circulation. We expected that PPV in the test subjects would poorly correlate to the obtained

increase in cardiac output (CO) due to spontaneous variability in PPV and circulatory control,

and in the model the correlation would be almost perfect.

Methods and results: We re‐analyzed recordings in 6 healthy test subjects from an earlier

study where they had received ~ 1.5 l of saline infusion in 20 minutes as part of a space

mission simulation (HDT’88). We performed spectral analysis of pulse pressure and of PPV.

Both showed a large contribution of VLF and LF variability to total variance. Due to the

deviant results in one test subject, average PPV measured over 5 minutes correlated poorly

to the obtained increase in CO. The computer model behaved well in this respect, even better

when autonomic outflow was diminished as simulation of sedation or anesthesia.

Conclusions: PPV is a complex measure when used in awake subjects. It shows large

moment‐to‐moment variability due to low‐frequency oscillations in the cardiovascular and

respiratory control systems. Its capacity to predict volume responsiveness, or use in

automated systems for fluid loading, is restricted to stable, heavily sedated or anesthetized

and ventilated patients. In those cases the circulation reduces to the simple textbook

situation.

Pulse pressure variability and fluid responsiveness

93

Introduction

The teachings of many great physiologists have shaped our understanding of the circulation.

Notably the work of Arthur Guyton (1919 – 2003), by his experimental work and computer

modeling (6, 14, 15, 17, 18), and in particular by his influential textbook since 1956 (16), is

still the basis for many of today’s clinical interventions. He stressed the importance of venous

return, central venous pressure and mean circulatory filling pressure to understand what

made the circulation go round (19, 20). In that light, it came as a surprise that static data like

right atrial pressure do not help much to guide volume infusion in the case of a failing

circulation (22, 25). Intravenous volume, if correctly given to critically ill patients will improve

ventricular function and survival rate after surgery (13, 28). However, overfilling may put the

lungs, heart and other organs at risk (3, 34).

Over the last 20 years, attention has shifted to dynamic parameters derived from continuous

arterial blood pressure like pulse pressure and stroke volume variation (PPV and SVV) as

better predictors of volume responsiveness. These variations are measured over the course

of one or more respiratory cycles, working on the assumption that PPV and SVV bring the

mechanical effect of respiration on ventricular filling to light, thereby answering the question

whether there is room for increased diastolic filling to improve cardiac function. However, in

view of the multitude of supervening regulatory mechanisms and real world ‘noise’, these

measures have shown to be applicable only in sedated patients on mechanical ventilation (25,

26). Under those circumstances, many of the normal influences are no longer at work, or at

least are much less prominent.

We undertook the present study to first, test the dynamic properties of PPV in spontaneously

breathing, awake, healthy test subjects who received a massive, quick infusion of saline. Next,

we used a computer model of the (regulated) circulatory system, to simulate these same

events in the ‘awake’ condition of the model. Then we purposely reduced the effectiveness

of the models’ autonomic nervous outflow, to simulate the condition of a sedated or

anesthetized patient. The computer model, by design, has just as much ‘noise’ as the scientist

allows it to have. Our hypothesis was that the model (like the patient under anesthesia)

allows using PPV as an accurate probe of cardiac function, while in the real world, in awake

subjects, normal variability virtually precludes this.

For these purposes we re‐analyzed continuous blood pressure recordings from 6 healthy

(male) test subjects who had received a large infusion of saline in a short period of time in the

Chapter 6

94

setting of a space‐mission simulation study (HDT’88, (2)). Next, we adapted our earlier

developed computer model of the circulation(39) to simulate the effects of this same volume

infusion, with and without preceding volume loss, both in the ‘awake’ and ‘sedated’

condition.

Methodsandtestsubjects

Experimentsinhealthytestsubjects.

We used recordings from a space‐mission simulation study that has been conducted in 1988

in the DLR‐Institute of Cologne, Germany (HDT’88) and has extensively been published

elsewhere (2). In short, six young (21‐34 yrs) male, healthy subjects went through a 16 days

baseline period, 10 days 6º head‐down tilted space simulation and 7 days recovery. During

those periods a number of interventions were repeatedly applied to test the condition of the

cardiovascular and fluid regulatory systems, of which the most important for the present

paper is an infusion of saline (22 ml/kg over 20 minutes) (11, 21). The DLR Institutional

Research Review Committee had approved the study and all subjects participated after

written, informed consent. The senior author of the present paper (JMK) participated as one

of the PI’s in that study; together with his co‐worker at the time, D.J. ten Harkel, they

recorded non‐invasive Finapres® continuous finger blood pressure during all experimental

interventions (36, 37). The digital records of beat‐by‐beat systolic, diastolic and mean blood

pressure and heart rate have been kept on file and were re‐analyzed here. Cardiac output

measurements were made by way of the foreign gas rebreathing method, carried out by Drs.

H. Schulz and A. Hillebrecht (11, 21) who had made the results of their measurements

available to us.

Analysis of blood pressure tracings: after visual inspection of the records for outliers and

artifacts, we chose suitable periods of some 5 minutes baseline recording before the main

intervention and 5 minutes at the peak of the intervention for spectral analysis of PPV. PPV

was measured as:

100*(maximal pulse pressure – minimal pulse pressure)/(average pulse pressure)

in a sliding window, covering a period of 10 heartbeats (cf. appendix ).


95

Power spectral density curves were computed using the spectral techniques as described in

(7, 43) and sampled in the usual frequency bands as proposed by the Task Force (10): very

low frequency‐VLF (0.003‐0.04 Hz), low frequency LF (0.04‐0.15 Hz) and high frequency or HF

(0.15‐0.4 Hz). The latter is broadly ascribed to respiratory variability.

Modeldescription

The computer model is an extension of the circulation models to simulate the effects of

gravity by van Heusden et al (39), and the model to simulate cardiopulmonary resuscitation

by Babbs (1) and adapted in Zhang and Karemaker (44). In short, the circulation model

includes four heart chambers, the systemic and pulmonary circulations. In the systemic

circulation, the thoracic aorta feeds the arteries to the upper body, kidneys, intestines, liver,

and lower body. The upper body returns its blood to the superior vena cava (SVC), the organs

below heart level return it to the abdominal vena cava (AVC), the intestinal venous outflow

via the liver. Both venous flows join in the right atrium, where the systemic circulation ends

and the pulmonary circulation starts. The whole circulation model includes 15 compartments;

the basic unit is a compliance in series with a resistance. Blood pressure in the model has

(baroreflex driven) ANS control to influence heart rate, cardiac contractility, vascular

compliances and unstressed volumes and systemic vascular resistance. The respiration effect

that we added here is simulated as a varying pleural pressure that influences intrathoracic

pressure, as in Figure 1. All organs in the thorax undergo this pleural pressure, with negligible

effect on the ones with relatively stiff walls, i.e. the left ventricle, thoracic aorta, right

ventricle during systole and pulmonary artery during systole. The model can be fitted to

individual data by inputting a specific subjects’ information, including height, weight, (resting,

supine) HR, blood pressure etc.

Figure 1. Pleural pressure is added as voltage source into unit of intrathoracic organs that is elastic: right atrium, right ventricle during diastole, pulmonary veins, pulmonary arteries during diastole, left atrium.

Chapter 6

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PPVaffectedbyanesthesia,volumeloss&infusion

During surgery patients will often be under general anesthesia or at least sedated.

Consequently, both sympathetic and parasympathetic activity are lowered or even have

completely ceased. The model simulates this condition by an extra gain factor in the (mainly

baroreflex driven) output of the autonomic nervous system (ANS) between zero and one. A

gain of one implies normal ANS‐output of the baroreflex, the patient is conscious; at a gain of

zero, ANS activity has totally stopped; at gains between zero and one different levels of

sedation or anesthesia are present.

The model simulates volume loss by connecting a ‘bleeding’ branch in the arterial

compartment as in Figure 2A, which is uncoupled when the lost volume reaches its preset

value. Volume infusion is simulated by connecting a current source to the right atrium as in

Figure2B until the preset infusion value is reached.

Figure 2 A: Volume loss is simulated as a low resistance branch, which connects the arterial

compartment to ground (0 V); the value of the resistance sets the amount of current that will flow

when the switch is closed; B: Volume infusion is simulated as a low resistance branch, which connects in

the same fashion a high pressure (voltage) source to the right atrium.


97

Modeling&simulation

We used the model to simulate the following situations:

First simulation series:

1. subject supine, in awake, stable condition, then a volume of 500 ml of ‘whole blood’ is

infused. The model does not have an interstitial fluid compartment, only an effective

circulating volume, therefore this infusion of ~ 10% of circulating volume is comparable

to the 1.6 l saline that was infused in the test subjects. Saline will distribute over the

entire extracellular fluid volume.

2. same as the first simulation, but a volume loss of 500ml is induced, followed by the

volume infusion of 500ml

Second simulation series:

1. subject under anesthesia, volume infusion of 500 ml, subject wakes up.

2. Subject under anesthesia, loss of 500ml, variable infusion volumes of 500 ml and more,

subject wakes up.

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Results

Infusionexperimentsintestsubjects.We restricted our analysis to the second infusion test in the pre‐head down tilt period. The

subjects had already undergone one such test, so they knew what was coming. One

exception here: the rebreathing data for the second control period infusion of test subject

P24 have been lost, for him we had to take the first test. To prevent the additional challenge

of acute head down tilting, subjects were in the horizontal, supine position, which is more

comparable to the clinical situation. Figure 3 shows an example from the resting control

period before the second infusion test in subject P24. The recording shows beat‐by‐beat

values of systolic and diastolic blood pressure, heart rate and, in panel B, pulse pressure and

pulse pressure variability. Pulse pressure is showing conspicuous very‐low‐frequency

oscillations one minute apart that appear exaggerated in pulse pressure variability. Moreover,

small variations in amplitude of the respiratory oscillations in pulse pressure appear as

low‐frequency oscillations at around 5/min in PPV. This property of the PPV‐algorithm is

shown in Figure 4 for all subjects: the bars denote the relative amounts of VLF, LF and HF,

both for pulse pressure (Figure4A) and PPV (Figure4B). Obviously there is still some power

left in the respiratory band of PPV, however, this should be considered ‘noise’ from looking at

the spectral curves – no clear peaks remain visible in PPV‐spectra, although respiratory peaks

are, of course, very prominent in the spectra of pulse pressure. About 50% of PPV‐variability

in all subjects is accounted for by VLF.

Figure 3 A stretch of recording from the

resting period before infusion (#2) in P24. A:

systolic and diastolic pressures (red bar and

lines), blue line: HR. B: Pulse pressure (red

line), Pulse pressure variability (blue line).


99

Figure 4 Relative amounts of VLF, LF and HF in % of total variability in the control period before infusion. A: Pulse pressure, B: Pulse pressure variability.

Table 1 summarizes the results of the infusion experiments and Figure 5 shows cardiac

output as function of PPV in the control period. It should be noted that the infusions were on

average 1.6 litres in 20 minutes (22 ml/kg) as opposed to the usual clinical fluid challenge of

500 ml (9, 27, 32, 35, 38). This PPV has been measured over a longer control period than that

shown in Figure 3, which resulted in a more stable estimate of average PPV. In spite of the

large infusion volume, the CO increase in 4 test subjects is less than what is usually required

to call a subject ‘volume responsive’ i.e. less than 15% (24). These 4 have a supine PPV of less

than 9%. One exception, though: subject P29 with low PPV (8.6%) reacted with a CO‐increase

of 46%. Subject P25 with a PPV of 11.8% scored 29% increase. Remarkably, PPV –on average‐

only diminished from 8.5% pre‐infusion to 7.7% post. Pulse pressure, on average, did not

change.

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Table 1. Results of infusion experiments; test subjects & model simulation

control period end of infusion

Subject PPV CO MAP SVR PPV CO MAP SVR

(%) (l/min) (mmHg) (m.u.) (%) (l/min) (mmHg) (m.u.)

P24 8.3 7.72 100.6 0.78 7.7 8.43 107.0 0.76

P25 11.8 6.82 68.2 0.60 11.0 8.82 70.7 0.48

P26 6.7 7.99 72.7 0.55 8.4 8.96 68.2 0.46

P27 6.9 7.45 80.5 0.65 5.0 7.81 80.9 0.62

P28 8.6 7.50 74.4 0.60 7.3 8.43 73.7 0.52

P29 8.6 4.65 73.4 0.95 6.9 6.79 76.1 0.67

Average 8.5 7.02 78.3 0.69 7.7 8.20 79.4 0.59

Model

Awake 11.1 6.80 101.3 0.89 6.3 7.37 91.1 0.74

Sedated 16.5 6.75 86.3 0.77 8.6 7.95 87.3 0.66

Values after infusion as % of control:

CO MAP SVR

P24 109 106 97

P25 129 104 80

P26 112 94 84

P27 105 100 96

P28 112 99 88

P29 146 104 71

Average 119 101 86

Model

Awake 108 90 83

Sedated 118 101 86

MAP = mean arterial pressure, measured from Finapres finger pressure. m.u. = medical units,

i.e. mmHg/(ml/sec).


101

Figure 5 CO after infusion as % of control value, as function of the pulse pressure variability in the

control period. Squares denote test subjects in the study, round symbols results from simulation

experiments as described in the text.

The infusions did not raise blood pressure much: between + and ‐6% as shown in Table 1. The

remainder of the CO‐increase was, consequently, accounted for by a fall in systemic vascular

resistance (SVR). This was particularly large in subject P29 with the 46% increase in CO: he

had a 39% drop in SVR (and only 4% rise in mean BP). This test subject has low resting CO and

high SVR compared to the other subjects anyway, as shown in the table. This pattern was the

same in the other saline infusion tests, in the earlier one in the control phase, as well as the

HDT‐ and the recovery phase.

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Modelingexperiments

Firstsimulationseries:modelinthe‘awake’condition

1. volumeinfusion:500ml(Figure6)The model had been fitted, first, to the supine resting data of a healthy, male subject. After

initial stabilization a volume expansion was started at t=300 s, completing the set 500 ml in

about 50 s. The model shows a drop in HR and MAP but an increase in pulse pressure, PPV is

almost halved. The model data have been entered in Table 1 and Figure 5 to be compared

with the healthy subjects. It shows that the model response is slightly different from the

subjects: it has higher resting PPV at a low increase in CO due to infusion.


103

Figure 6 Simulation of volume expansion in a healthy subject. At t = 300 s an infusion is started which ends after ~ 50 s at a volume of 500ml as indicated in panel B. From above down: Beat to beat aortic blood pressure (A), heart rate (B), pulse pressure (C) and pulse pressure variability (D).

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2. volumelossof500ml,followedbyinfusionof500ml(Figure7)At t=200 s the model loses 500 ml in 20 s. At t=300 s this volume is repleted as in the previous

case. The figure shows that PPV roughly doubles at a decreased pulse pressure after volume

loss. After re‐infusion the baseline is restored.


105

Figure 7 Simulation of the same subject as in Figure 6. At t = 200 s a volume of 500ml is lost within 20 s,

at t = 300 s this is repleted as indicated in panel B. From above down: Beat to beat aortic blood

pressure (A), heart rate (B), pulse pressure (C) and pulse pressure variability (D).

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Secondsimulationseries:modelinthe‘anesthetized’condition

1. volumeinfusion:500ml,subjectwakesup(Figure8)For the simulation of autonomic effects by anesthesia we assumed vagal and sympathetic

activity to drop to ¼ of the normal, awake condition. In Figure 8 anesthesia is started at

t=100 s, (models may have a new condition within seconds or less): BP drops and PPV is

immediately increased compared to baseline. At t=300 s an infusion is started, totalling a

volume of 500 ml in about 50 s. Awakening is at t=500 s. When the autonomic outflow is

altered by the anesthesia, the model behaves differently: now the hypervolemia does no

longer lead to a decrease in MAP, systolic pressure is not defended by the vagal baroreflex as

it was in the awake state. Obviously, after anesthesia the final condition is equal to that of

Figure 6. We have entered the values for the anesthetized model in table 1 and Figure 5 as

well. Comparison with the test subject values now shows a more ‘normal’ reaction to volume

infusion: MAP does not decrease, systolic pressure is not as heavily defended by the vagal

baroreflex as it was before, in the ‘awake’ model. Still, the values for the model are shifted to

higher values of PPV than those for the test subjects.


107

Figure 8 Simulation of the same subject as in Figure 6. At t = 100 s anesthesia is instituted by

diminishing autonomic outflow to 0.25 of the normal value. At t = 300 s an infusion of 500 ml is given

as in Figure 6. Anesthesia is stopped at t = 500 s. The interventions are indicated in panel B.

From above down: Beat to beat aortic blood pressure (A), heart rate (B), pulse pressure (C) and pulse

pressure variability (D).

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2. volumelossof500ml,infusion:500ml,subjectwakesup(Figure9)At t=200 s the volume loss starts, stopping after 10 s, when 500 ml has been lost. After an

initial jump PPV follows the slow compensation that is set in by the (crippled) sympathetic

nervous system, however, the earlier level is never reached. After the 500 ml (whole blood)

volume expansion that started at t=300 s and completes within ~ 50 s, BP, HR and PPV get

back to their levels before volume loss (the first 100‐200 s). At t=500 s the subject is back to

consciousness; first BP shows an overshoot, then pressure settles back to baseline (0‐100 s),

all values return to normal.

In this simulation PPV seems a good reflection of cardiac preload, or volume status.


109

Figure 9 Simulation of same subject as in Figure 6. Anesthesia at t = 100 s as in Figure 8; at t = 200 s a

volume of 500 ml is lost and repleted at t = 300 s as in Figure 7. The interventions are indicated in panel

B. From above down: Beat to beat aortic blood pressure (A), heart rate (B), pulse pressure (C) and pulse

pressure variability (D).

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3. volumelossof500ml;infusion:1500ml,subjectwakesup(Figure10)In this simulation, obviously, the circulation became overfilled. When we followed PPV as

probe for volume status, infusion could have been stopped at the moment that the

pre‐bleeding value of PPV had been reached. Stressing the outcome shown in Figure 10,

where PPV reaches the earlier value at the time when the volume loss had been

compensated by the infusion (at the arrow in panel D of Fig.10). When the infusion continues

beyond that level, PPV drops further, BP starts to rise above pre‐bleeding values and right

atrial pressures and pulmonary pressures rise as well, sometimes to unwanted high pressures.

After return from anesthesia in an overfilled state, of course, BP overshoots to higher than

normal values. This pattern is dramatically shown here, where the infusion only was stopped

when a volume of 1500 ml had been reached. Since the infusions in the model were by

‘whole blood’ rather than by saline, the simulation had a tendency to run out of normal

boundaries at the high infusion values.


111

Figure 10 Simulation of same subject as in Figure 6. Anesthesia at t = 100 s as in Figure 8; at t = 200 s a

volume of 500 ml is lost and at t = 300 s an infusion is started, stopping at 1500 ml after 150 s.

Anesthesia is stopped at t = 500 s. The interventions are indicated in panel B. The arrow in panel D

points to the moment where the pre‐bleeding PPV is reached again. From above down: Beat to beat

aortic blood pressure (A), heart rate (B), pulse pressure (C) and pulse pressure variability (D).

Chapter 6

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Discussion

“Don’t fuss, just fill” or paraphrases of this corollary have been taught to generations of ICU

clinicians. However, the side effects of filling more than necessary became also well‐known

by recent studies inspired by Evidence Based Medicine: slower wound healing, danger of

pulmonary edema, longer duration of hospital admission (4, 23, 29, 34). These newer insights

made a strategy for optimal volume control necessary. In that vein, measurement of pulse

pressure variation has been cheered as the new wonder of ingenuity and has been the source

of disappointment (5). The present study was undertaken to test if PPV predicted volume

response in awake subjects receiving a large amount of intravenous saline within a short

period. Furthermore, our computer model of the circulation was put into action to mimic the

use of PPV in sedated, ventilated patients, where its use in automated infusion has been

proposed (31). The volumes of the respective infusions (test subjects/computer model) may

not be directly compared: saline has a much larger distribution volume than whole blood: in

the HDT’88 study haematocrit by the 1.6 l infusion was lowered by about 10% at the

completion of infusion, to return back to normal values in about 2 hours post‐infusion (33).

‘Infusion’ of whole blood into the model of 1500 ml brought it to its boundaries of normal

operation.

Optimal cardiac performance has often been judged by the cardiac output value, since that

should deliver the required oxygen at an appropriate pressure to the periphery. From the

Frank‐Starling curve we know that increased diastolic filling only works up to a maximum,

beyond which no more gain can be expected. Logically speaking that maximum is reached

when respiratory variations in filling no longer lead to changes in stroke volume and thus to

changes in pulse pressure. However, in the awake subject the relation between total

circulating volume and cardiac performance is subject to many influences of which filling of

the ventricle is only one. The autonomic nervous system influences cardiac inotropy, thereby

ejection fraction and speed of ejection, to name but a few of the factors that co‐determine

pulse pressure. The fact that about 50% of variability in PPV in the test subjects is found in

VLF (cf. Figure4B) underpins this and points to an important involvement of the sympathetic

nervous system and possibly slower, hormonal regulations. Earlier, Virtanen and co‐workers

(41) have studied beat‐to‐beat oscillations in pulse pressure and concluded that several

mechanisms contribute to its LF variability, among which serum insulin levels.

Even though the subjects in this study received roughly the triple amount of a normal fluid

challenge (around 1.6 l instead of 0.5 l) they did not react with more CO‐increase than what


113

could be expected if we look at their pre‐infusion PPV (Table 1, Figure 5); 4 out of 5 had PPV’s

below 9% and reacted with less than 15% increase in CO. However, the numbers that have

been established for patients under anesthesia have not been validated in awake subjects.

Apart from the one subject who had 46% increase in CO, the others showed an almost linear

relationship between PPV and CO‐increase (Figure 5).

A reason why the values for PPV that we calculated here might deviate from those in the

literature is the fact that we used ‘raw’ finger blood pressure values from an early research

version of the Finapres (TNO‐BMI’s model 5, a forerunner of later space‐qualified versions

and Ohmeda’s 2300 blood pressure monitor). In the Finapres® technique continuous blood

pressure is measured in the finger arteries. Since those are very small, peripheral arteries,

one may expect sharp, peaking systolic pressures, compared to more central pressure

recordings. In young subjects one may observe a finger pulse amplification by a factor of 1.5

compared to more central pressures (upper arm, aorta) (42). Elaborations of the technique of

continuously measured non‐invasive finger pressure make use of later research on pulse

wave transmission along the lower arm and hand (12) applying that knowledge to estimate

upper arm pressure, rather than giving the difficult‐to‐interpret finger pressure (this

algorithm is used in the newer Nexfin® device, presently marketed by Edwards Lifesciences

Corp.). A higher systolic and lower –peripheral‐ diastolic value leads to higher pulse pressures.

Since PPV is measured as percentage of pulse pressure, one may well suppose that the

numbers from the present study are on the low side. If one would apply this knowledge to

the data points in Figure 5, the differences between measurements in the test subjects and

those in the model would become less or might even disappear.

As shown in Figure 4 the contribution of very low‐ and low‐frequency variability in PPV is

considerable. This necessitates a sufficiently long stable period of blood pressure to find a

reliable value of PPV in awake subjects. It is insufficient to just measure a few respiratory

cycles and calculate the average, as it might be done in sedated or anesthetized patients (27,

30). Figs. 3B and 4 underline this dependency of PPV on oscillations slower than respiration.

This finding has consequences for the treatment of patients in the pre‐operative stage:

judging the – later to be expected – volume responsiveness before induction of anesthesia

should not rely on a short‐lasting measurement of supine PPV. When in doubt, an actual

test‐fluid challenge should be given, as advocated by (40).

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Our modeling also shows that PPV under conditions of low autonomic activity might be useful

in an automated infusion control system (31). Since the model is very ‘Guytonian’ in nature

this makes sense: Here PPV is mainly the consequence of variations in cardiac filling.

Conclusions

We have shown in this study that PPV is a complex measure when used in awake subjects. It

shows large moment‐to‐moment variability due to low‐frequency oscillations in the

cardiovascular and respiratory control systems. Its capacity to predict volume responsiveness,

or use in automated systems for fluid loading, is restricted to stable, heavily sedated or

anesthetized and ventilated patients. In those cases the circulation reduces to the simple

textbook situation, where cardiac output and its variations are determined by only the basic

factors: cardiac filling, cardiac function and systemic vascular resistance.


115

Appendix:howtocomputePPV

The optimal way to compute respiratory‐related PPV is, of course, to record the variations

from begin‐ to end‐expiration. This requires either a respiration signal to synchronize the

detection or intricate algorithms to trace variations in the pulse pressure signal that might be

attributed to respiration. To circumvent those problems often an approximation formula is

used as follows:

(PPmax – PPmin)/[(PPmax + PPmin)/2]

or, alternatively:

(PPmax – PPmin)/(PPavg)

Where

PPmax = maximal pulse pressure in the sampling period

PPmin = minimal pulse pressure in the sampling period

PPavg = average pulse pressure over the sampling period

All measured either over one or more defined respiratory periods or over a fixed number of

heart beats or period of time.

In view of the practical applicability we chose to use the second formula over a fixed number

of heart beats. To find the optimal number of beats, we ran a simulation of a simplified

beat‐to‐beat circulation model of cardiac control (8, 43) where we added various respiratory

frequencies to the pulse pressure. Due to the presence of sympathetic baroreflex driven

feedback the model would show low frequency variability as well as the respiratory

frequency.

The result of these simulations is shown in Figure 12. It shows the normalized, computed PPV

as percentage of what it should have been, as a function of sampling period and for various

respiration intervals, from 3 seconds up to 15 seconds (20/min to 4/min).

As to be expected, the shorter the sampling interval, the lower the amount of variability that

is caught by the algorithm: it does not span the whole respiratory cycle. However, when the

sampling period gets longer the algorithm will catch more of the low frequency variability and

thereby overestimate the actual PPV.

From Figure 11 we concluded that the optimal sampling period for the prevailing respiratory

rates would be around 10 seconds, or 10 heart beats if HR is not too high. Longer periods

result in overestimation, shorter in underestimation of actual PPV.

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Figure 11. Theoretical result of PPV‐measurement as function of sampling period and respiratory period.

Ordinate gives the % result of what should have been measured, given the sampling period (abscissa).

Curves have been computed for respiratory periods of 3, 4, 5, 6, 7, 8, 9, 10, 12 and 15 seconds, as

indicated by the arrow (3s → 15s). The optimal value is found when sampling period and respiratory

period are equal.


117

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Chapter7

Summaryandgeneralconclusions

Introduction

Computer modeling in bedside physiology may assist clinical research, in cases where:

1. It is impossible to perform the experiments in reality;

2. It is difficult to repeat the observation in the same condition;

3. It is expensive to do the experiment;

4. It offers a new angle to analyze the problems.

Modeling and simulation can be used to simulate clinical situations, and to offer a virtual

platform to solve clinical problems. Our circulation model for Cardiopulmonary Resuscitation

(CPR) is an example; to compare the effects of various CPR techniques on the same subject is

impossible; apart from the obvious ethical issues, to experiment on animals is expensive and

it is difficult to imitate the situation in humans. Comparing CPR techniques in a circulation

model can avoid these disadvantages and offer an unbiased platform.

In Chapters 2 and 3 we used a circulation model to compare the effects of different CPR

techniques, and to investigate the potential risks of intensive CPR performance. In Chapter 6

we used a circulation model with Autonomic Nervous System (ANS) adjustment to simulate

volume loss and infusion, to check whether pulse pressure variation (PPV) might reflect fluid

responsiveness.

The ANS is an important control system, which affects heart rate, blood pressure and

–indirectly‐ respiratory rate, to name but a few. Some diseases influence the function of the

ANS and vice versa, ANS dysfunction may also influence someone’s health. Analysis of

physiological signals like blood pressure variability (BPV) and heart rate variability (HRV) can

detect the ‘status’ of the ANS, to dig deeper into hidden pathology.

Chapter 4 is about Chronic Heart Failure (CHF) patients who are on beta‐blocker treatment; if

hospitalized, even with all monitors around, they might still slip into a sympathetic state

without any (early) alarm sounding. We analyzed heart rate variability (HRV) in several ways,

testing newly proposed techniques, to find which one would be able to give an early warning

and uncover hidden risks.

Chapter 7

122

Another example is the Brugada syndrome (BrS), which is a genetic disease which might lead

to sudden death, even with normal cardiac structure. The problem is that some patients may

have a high risk, some don’t. How to recognize those who are at risk is important but difficult.

In Chapter 5 we applied extensive analysis to evaluate the ANS function in recordings which

had been made by clinical colleagues who asked BrS patients to remain quietly supine for 10

minutes and then stand up for another 10 minutes, all while recording continuous BP and HR.

Summaryoffindings

Question 1: Which cardiopulmonary resuscitation technique will improve cardiac output

best?

In Chapter 2 we used a ‘stopped’ cardiac circulation model, to compare the effects of five CPR

techniques: chest‐only compression CPR (CO‐CPR), Active compression‐decompression CPR

(ACD‐CPR), Interposed abdomen compression CPR (IAC‐CPR), Lifestick CPR, and Enhanced

External Counterpulsation CPR (EECP‐CPR)). In the circulation model, Lifestick‐CPR gave the

best cardiac output, coronary perfusion, and cerebral perfusion. At the same time we found

that it is very important to improve CO by combining chest compression and abdomen

compression, so if cardiac arrest occurs out‐of‐hospital, IAC‐CPR is recommended because it

can be performed without equipment by two rescuers working together.

Question 2: If we can improve cardiac output by these alternative CPR techniques, why is

chest‐compression CPR still the only CPR mode that is recommended in the Guidelines?

In Chapter 3 we refined our look at cardio‐pulmonary resuscitation: we measured not only

CO, perfusion to all important organs, but also pulmonary pressure. We found that all

combined efforts to improve CO might put the lungs at risk to develop acute edema. That is

possibly why CO‐CPR still stands out as ‘treatment of choice’, chosen above alternative CPR

techniques. To create a large perfusion volume a big volume of blood is accumulated in the

chest. But then we have to use high pressure to push the blood to where it is needed. In this

process, if the lungs are connected both to the vascular pressures on one side and to outside

air on the other, the exerted high pressure can lead to pulmonary edema. A solution would

be, when performing CPR in this way, to apply positive pressure ventilation avoiding this lung

pressure difference, or, alternatively, compression pressure should be controlled to within

tolerance levels to protect the lungs.

Question 3: Can pulse pressure variation (PPV) predict fluid responsiveness?

General summary

123

In Chapter 6 we tested the relation between PPV and blood volume change in test subjects

and a computer model. We confirmed that under anesthesia PPV is a good reflection of

cardiac filling (in healthy hearts). In the conscious state PPV is more variable: we need a long

observation period to calculate PPV, to reflect fluid responsiveness. In the conscious state

PPV is under the influence of many disturbances other than changes in cardiac filling as is

reflected by Fourier analysis of its variability.

Question 4: Which HRV parameters can detect quickly when a CHF patient slips into a

sympathetic state, even under beta‐blocker treatment?

In Chapter 4 we used the standing condition to induce a more sympathetic situation

(compared to the supine state). Among all HRV parameters (meanIBI, SD‐IBI, rMSSD, LF, HF,

LF/HF, MSV, MSD) only meanIBI, rMSSD, pIBI50, and MSD were still able to detect the change

between supine and upright state. This ruled out a set of newly proposed analysis techniques

for monitoring in acutely ill patients.

Question 5: Can we discriminate the ANS in BrS patients from healthy subjects by applying

physiological parameter analysis? In what way they are different?

In Chapter 5 we compared a wide range of cardiovascular parameters between healthy

controls and BrS patients; in the supine state it is hard to tell any difference between the two

groups. When the sympathetic nervous system is challenged by a position change from

supine to standing, we found that there is probably less sympathetic outflow increase in BrS

subjects, which might lead to vasovagal syncope, or even carry a potential VF risk.

Limitations

To concentrate on the main problem: the model is always a simplification of the real situation.

In reality the patients’ situations are complex; in Chapters 2 and 3 we simulated the ‘stopped’

circulation; we have to ignore all kinds of possible effects like oxygenation, blood flow inertia,

the effects of CNS. In the model we set parameters as constant, in reality they may be

changing with pressure and with time; our model is limited to simulate a standard 70kg man,

in reality only few patients fit this pattern. In Chapter 6, although we got a more

individualized model, we neglected long‐term adjustments, like circulating hormones.

In the Chapters 2, 3 and 6 we do not have parallel clinical or animal experimental results to

compare to the results of our modeling.

Chapter 7

124

In Chapter 4 we only got short heart rate recordings (2 x 10 min); to get more stable values

for the ‘modern’ chaos parameters like MSE, MSV and MSD we should extend the test period

longer (15‐20min) and in future experiments a sympathetic state shift might be induced

slower and more continuous (like on a tilt table in slow mode). This could be more like a

realistic clinical situation. In Chapter 5 we need more healthy controls and BrS patients in

different age group, to verify the experimental results.

GeneralConclusion

Even with all the limitations, to solve clinical problems by way of physiological modeling and

biomedical engineering is not rare any more. It is a fast growing field, as it gives the

opportunity of ‘another angle’ to look at and analyze the problems at hand. However without

a full understanding of the medical problems to be obtained by cooperation with clinicians,

biomedical engineering is only ‘armchair strategist’ science. As biomedical engineers, we just

hope that clinicians, who are with so much knowledge of practical medical problems, can

open their arms, to welcome these kinds of ‘healthy’ cooperations.

General summary

125

Nederlandsesamenvatting

ComputermodellenbijFysiologieaanhetziekbed

Computermodellen kunnen worden toegepast in gevallen waarin dat nuttig en nodig is voor

klinisch onderzoek, bijvoorbeeld:

1. Wanneer het onmogelijk is om bepaalde experimenten ‘in het echt’ uit te voeren;

2. Wanneer de experimenten moeilijk herhaald kunnen worden onder steeds dezelfde

omstandigheden;

3. Wanneer de experimenten bijzonder duur zijn;

4. Wanneer een nieuwe kijk op het probleem kan worden gegeven.

Aan de hand van een aantal onderzoeksvragen kunnen de bevindingen van de 5

experimentele hoofdstukken van dit proefschrift worden samengevat:

Vraag 1: Welke techniek voor ‘hartmassage’ (CPR) geeft het beste hartminuutvolume (HMV)?

In hoofdstuk 2 wordt een computermodel van de stilstaande circulatie (na hartstilstand)

geïntroduceerd. Met dit model werden de effecten van 5 mogelijke technieken van CPR

vergeleken. De hier getoetste technieken zijn: alleen thoraxcompressie (CO‐CPR), actieve

thorax compressie‐decompressie (ACD‐CPR), alternerende borst‐buik compressie (IAC‐CPR),

CPR met gebruik van een speciale hefboom (“Lifestick”‐CPR) en CPR met toepassing van een

set computer‐opblaasbare balgen rond kuiten, bovenbenen en onderbuik (EECP‐CPR). In het

model gaf Lifestick‐CPR de beste resultaten, kijkend naar HMV en de doorstroming van hart

en hersenen. Hierbij stelden we ook vast, dat het zeer belangrijk is om compressie van borst

en buik te combineren, dus bij een hartstilstand buiten het ziekenhuis zou ACD‐CPR, toe te

passen door 2 reddingswerkers samen, de beste resultaten geven, omdat dit ook kan zonder

apparatuur.

Vraag 2: Als we het hartminuutvolume dan zo kunnen verbeteren door deze niet‐standaard

technieken, waarom beveelt de officiële richtlijn dan nog alleen maar CO‐CPR aan?

In hoofdstuk 3 zijn de effecten van CPR nauwkeuriger gemeten: niet alleen maar HMV en de

doorstroming van belangrijke organen (hart, hersenen), maar ook de druk in de longen.

Daarbij vonden we, dat opvoeren van het HMV door steeds meer druk op thorax en

buik/onderlijf, een gevaar vormt voor de longen. Er wordt hierbij een groot bloedvolume in

de borstholte samengebracht, maar de druk die wordt opgebouwd om de grote circulatie te

Chapter 7

126

doorstromen, heerst ook in de longen, waarvan de haarvaten in open verbinding staan met

de buitenlucht. Wanneer deze druk niet wordt beperkt, kan er acuut longoedeem optreden.

Het is dus zaak om de druk op borst en buik nauwkeurig te doseren om dit te voorkomen.

Mogelijk is deze complicatie er de oorzaak van, dat CO‐CPR bij klinische evaluaties nog steeds

als voorkeursmethode naar voren komt.

Vraag 3: Kan meting van de ademhalingsgerelateerde variaties in polsdruk (PPV) voorspellen

hoe een patiënt reageert op extra vloeistof via een infuus?

In hoofdstuk 6 wordt de relatie tussen PPV en veranderingen in het circulerend volume getest,

zowel in (wakkere) proefpersonen als in een computermodel. We stelden vast, dat onder

computer‐gesimuleerde anesthesie PPV een goede maat is voor de vullingstoestand van het

(gezonde) hart. Maar bij de wakkere patiënt/proefpersoon vertoont de PPV veel meer

variaties, vooral onder invloed van het autonome zenuwstelsel. Daardoor is ook een lange

meetperiode noodzakelijk om tot een betrouwbare PPV‐schatting te komen.

Vraag 4: Welke meetmethode voor slag‐op‐slag hartritme variaties (HRV) kan aantonen dat

de toestand van een patiënt verslechtert, zelfs wanneer deze bètablokkers gebruikt?

In hoofdstuk 4 worden hartslag en HRV in staande toestand vergeleken met de liggende. Dit

is gebruikt als ‘model’ om een meer sympathische toestand op te wekken in patiënten met

stabiel hartfalen die o.m. met bètablokkers werden behandeld. We stelden daarbij vast, dat

de gemiddelde hartslag, de standaarddeviatie enkele andere klassieke parameters (zoals

rMSSD en pIBI50) deze verandering wel aantoonden, maar enkele in de literatuur

nieuw‐voorgestelde (‘niet‐lineaire’) parameters niet.

Vraag 5: Is de activiteit van het autonome zenuwstelsel bij patiënten met het syndroom van

Brugada (BrS) aantoonbaar anders dan bij gezonde proefpersonen? Zo ja, in welk opzicht?

In hoofdstuk 5 vergeleken we een groot aantal cardiovasculaire parameters in BrS‐patiënten

met die van een gezonde controlegroep. De meting in de liggende toestand leverde vrijwel

geen verschillen op. Maar bij sympathische stimulatie door de staande stand vonden we dat

een subgroep van de patiënten minder toename van sympathische activiteit vertoonde. Dit

zou bij hen makkelijker tot vasovagale collaps kunnen leiden of zelfs tot ventrikelfibrilleren.

127

CurriculumVitae

PERSONALINFORMATIONName: Yanru Zhang Gender: female Nationality: Chinese Email: [email protected]

EDUCATIONPhD candidate‐ at the Medical Faculty of the University of Amsterdam

Thesis to be defended: Computer Models in bedside Physiology Department of Anatomy, Embryology and Physiology (subdept. Systems Physiology) Academic Medical Center, Univ.of Amsterdam, the Netherlands 2009‐2013

PhD in computer science Thesis: The Study on Modeling and Simulation of the Hemodynamic Effects of Cardiopulmonary Resuscitation; Department of Computer Science, South China University of Technology 2004‐2009

Bachelor of Communication Engineering Department of communication Engineering, Inner Mongolia University 2000‐2004

Accomplishments&PublicationsProject 1:

Finished related research work and published the paper: Zhang Y and Karemaker JM. Abdominal counter pressure in CPR: What about the lungs? An in silico study. Resuscitation 83: 1271‐1276, 2012.

Project 2: Finished related research work and published the paper: Zhang Y, de Peuter OR, Kamphuisen PW, and Karemaker JM. Search for HRV‐parameters that detect a sympathetic shift in heart failure patients on beta‐blocker treatment. Front Physiol 4: 81, 2013.

Project 3: Finished related research work and the paper: ‘A subgroup of Brugada patients shows low orthostatic blood pressures as a sign of decreased sympathetic outflow’ is in process.

Project 4: Finished related research work and the paper: ‘Dynamics of Pulse Pressure Variability and the Difficulty of Predicting Fluid Responsiveness’ is in process.

128

Acknowledgements

I would like to thank my promotor, Prof.dr. J.H.R. Ravesloot, for his special one‐on‐one

tutoring in ‘Physiology & Anatomy’ and many great suggestions, in keeping my project on

schedule. My very great appreciation goes to Dr. J.M. Karemaker, my research supervisor, for

his patient guidance, enthusiastic encouragement and useful critiques of this research work.

My grateful thanks are also extended to Dr. Berend Westerhof, for his advice on my every

project, for being fireman to the fiery debates between John and me. I would also like to

thank Mr. Wim Stok, for his help in doing the methodological data analysis, to Ms.

Anne‐Sophie Bronzwaer, who shared her knowledge and methods with me and many

explanations on details of how the measurement and calculation work and to Mr. André de

Graaf, who solved so many practical problems in these four years

I would also like to extend my thanks to every colleague in the PhD‐students office all along,

who gave me a lot of support and encouragement. Your good humor accompanied me on

every good or bad day in room M01‐217.

My thanks go to every friend I met in the Netherlands; I can’t imagine how boring the life

would have been without you guys. Special thanks to Michel de Leeuw and your family,

hunting good Chinese restaurants with you is the best experience, special thanks also to Qian

Wang, who made me realize how powerful friendship could be.

Finally, I wish to thank my parents for their support and encouragement throughout my

study.

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