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Predicting the Risk of Sudden Cardiac Death Leon Glass Isadore Rosenfeld Chair in Cardiology, McGill University, Montreal, Quebec
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Predicting the Risk of Sudden Cardiac Death

Nov 30, 2021

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Page 1: Predicting the Risk of Sudden Cardiac Death

Predicting the Risk of Sudden

Cardiac Death

Leon Glass

Isadore Rosenfeld Chair in

Cardiology, McGill University,

Montreal, Quebec

Page 2: Predicting the Risk of Sudden Cardiac Death

• Disclosure – Licensing and research

contract with Medtronic

Page 3: Predicting the Risk of Sudden Cardiac Death
Page 4: Predicting the Risk of Sudden Cardiac Death

Histogram of ΔRR Intervals during AF

Data from www.physionet.org

Page 5: Predicting the Risk of Sudden Cardiac Death

Patents describing algorithm are

licensed to Medtronic • Medtronic Reveal XT™ ICM

Offers Important Insights into Patients’ Irregular Heart Rhythms, Including Atrial Fibrillation

• MINNEAPOLIS – Feb. 11, 2009 – Medtronic, Inc. (NYSE: MDT) today announced the commercial availability of its Reveal XT™ Insertable Cardiac Monitor (ICM) in the United States, along with the nation’s first implant of the new device.

The revolutionary Reveal LINQ™

Insertable Cardiac Monitoring System

is designed to help your doctor quickly

diagnose and treat irregular heartbeats

that may be related to unexplained

fainting.

Page 6: Predicting the Risk of Sudden Cardiac Death

• “Given the desirability of accurate risk

stratification [for sudden cardiac death]

and the long history of research in this

area, it is important to understand why the

field is not further advanced.” Jeffrey

Goldberger et al. Circulation 2011

Page 7: Predicting the Risk of Sudden Cardiac Death

Key Questions

• Laboratory: Understand complex rhythms

and transitions between rhythms in model

cardiac systems?

• Clinical: What therapies are available?

• Clinical: Who is at high risk for

tachyarrhythmic sudden cardiac death?

Page 8: Predicting the Risk of Sudden Cardiac Death

Dynamics in chick heart

cells – transitions

to reentrant dynamics

Thanks to my colleague

Alvin Shrier

Page 9: Predicting the Risk of Sudden Cardiac Death

A Resetting Experiment (in chicken

heart)

Page 10: Predicting the Risk of Sudden Cardiac Death

Chaos in periodically stimulated

heart cells

(Guevara, Glass, Shrier Science ,1981)

Predict chaos based on

1D circle maps determined

from resetting experiments.

Resetting depends on phase

of stimulus.

Page 11: Predicting the Risk of Sudden Cardiac Death

) Reentrant

arrhythmias:

period of oscillation

is set by a reentrant

circuit NOT a

pacemaker

Page 12: Predicting the Risk of Sudden Cardiac Death

Macroscope for Studying Dynamics in Tissue Culture

Bub, Hodge,Shrier

Page 13: Predicting the Risk of Sudden Cardiac Death

Pacemaker

Nagai, Gonzalez, Shrier, Glass, PRL (2000)

Dynamics in a Ring of Cardiac Cells

Page 14: Predicting the Risk of Sudden Cardiac Death

Reentry

Page 15: Predicting the Risk of Sudden Cardiac Death

Cardiac Ballet

Page 16: Predicting the Risk of Sudden Cardiac Death

FitzHugh-Nagumo Model of Propagation

Page 17: Predicting the Risk of Sudden Cardiac Death

Spiral waves in cardiac tissue -

role in atrial and ventricular

tachycardias?

• Theoretical predictions – Wiener and

Rosenblueth, Krinski, Winfree et al.

• Experimental observations – Jalife,

Witkowski and many others subsequently

Page 18: Predicting the Risk of Sudden Cardiac Death

Bursting Rhythms in Cardiac 2D Tissue Culture

Bub, Glass, Publicover, Shrier, PNAS (1998)

Page 19: Predicting the Risk of Sudden Cardiac Death

density

a glycyrrhetinic acid

0 mm 5 mm 10 mm

high

periodic periodic burst

mid

periodic burst burst

low

irreg.burst irregular irregular

(Bub, Glass, Shrier, PRL 2005)

Page 20: Predicting the Risk of Sudden Cardiac Death

R=3, q=0.35

R=1.8, q=0.35

Target patterns (‘periodic’)

bursting

Cellular automata model – pacemakers,

heterogeneity, fatigue, connectivity

Page 21: Predicting the Risk of Sudden Cardiac Death

Universal organization: Fatigue vs

Coupling

(Bub, Glass, Shrier, PRL 2005)

Page 22: Predicting the Risk of Sudden Cardiac Death

Spiral formation in tissue culture

with a central obstacle

Quail et al., Physical Review Letters 2014

Page 23: Predicting the Risk of Sudden Cardiac Death

Role of asymmetry in

determining chirality of spirals

Quail et al., unpublished 2014

Page 24: Predicting the Risk of Sudden Cardiac Death

Key Questions

• Laboratory: Understand complex rhythms

in model cardiac systems?

• Clinical: What therapies are available?

• Clinical: Who is at high risk for

tachyarrhythmic sudden cardiac death?

Page 25: Predicting the Risk of Sudden Cardiac Death

Implantable Cardioverter Defibrillators (ICD)

reduce the incidence of sudden death due to rapid

arrhythmias

But it is difficult to assess which patients will benefit from an ICD

Page 26: Predicting the Risk of Sudden Cardiac Death

Cheney's change-out: Vice president's ICD replaced

July 30, 2007 Steve Stiles

Washington, DC - Vice President Dick Cheney's implantable

cardioverter-defibrillator (ICD) was replaced Saturday at George Washington

University Hospital, news outlets reported over the weekend, citing a statement

made by his deputy press secretary Megan McGinn. There were no

complications, and the vice president emerged from the hospital about four

hours after he entered, according to the reports.

The procedure had been scheduled after a check of the device during the vice

president's annual physical last month showed the battery was nearing the end

of its lifetime. The device was implanted in 2001 and, according to the vice

president's office, on no occasion did it deliver a shock. The lead system

wasn't replaced.

Cheney's cardiovascular health history is one of the world's most publicly

documented. As reported over the years by heartwire and news outlets virtually

everywhere, it includes four heart attacks before he became vice president, a

CABG, two PCIs, and an episode of deep venous thrombosis on a recent

international tour.

Page 27: Predicting the Risk of Sudden Cardiac Death

Re: Cost of ICD

Last year when I was in the hospital,

I was told it would cost $30,000 for the ICD

and the procedure.

We lost count at $100,000 for the complete hospitalization.

I am thankful I was able to pay my insurance premiums.

From the internet

Page 28: Predicting the Risk of Sudden Cardiac Death

Key Questions

• Laboratory: Understand complex rhythms

in model cardiac systems?

• Clinical: What therapies are available?

• Clinical: Who is at high risk for

tachyarrhythmic sudden cardiac death?

Page 29: Predicting the Risk of Sudden Cardiac Death

Sudden death. Why did this 82 yr old

woman die at 19:13:53 and not at

16:05:55 ?

www.physionet.org

Page 30: Predicting the Risk of Sudden Cardiac Death

Most sudden cardiac death occurs

in people few risk factors

(Myerburg et al., 1997)

Page 31: Predicting the Risk of Sudden Cardiac Death

(Huikuri et al. EHJ 2009)

CARISMA

Page 32: Predicting the Risk of Sudden Cardiac Death

More PVCs increase the risk of

death

Kostis et al. 1987

Page 33: Predicting the Risk of Sudden Cardiac Death

Cardiac Arrhythmia Suppression Trial (CAST).

NEJM 321:406 (1989); 324:781 (1991)

Page 34: Predicting the Risk of Sudden Cardiac Death

Analysis of arrhythmias in people

• Risk stratification for sudden cardiac death

• First analyze mechanisms of arrhythmia in

individual patients (this is NOT commonly

done now)

Page 35: Predicting the Risk of Sudden Cardiac Death

Dynamics of PVCs

Page 36: Predicting the Risk of Sudden Cardiac Death

What is the mechanism of this rhythm?

16 year old boy who had an atrial septal

defect corrected at a young age who

fainted on the lunch line in a school

cafeteria. Does he have a high risk for

SCD?

Page 37: Predicting the Risk of Sudden Cardiac Death

Pure Parasystole

Page 38: Predicting the Risk of Sudden Cardiac Death

Rules of Pure Parasystole Count the number of sinus beats between PVCs beats. In this sequence: (1) there are 3 integers; (2) one is odd; (3) the sum of the two smaller is one less than the largest.

Glass, Goldberger, Belair (1986)

Page 39: Predicting the Risk of Sudden Cardiac Death

Stochastic Model of Parasystole

Page 40: Predicting the Risk of Sudden Cardiac Death

Heartprint of a Patient

Page 41: Predicting the Risk of Sudden Cardiac Death

Other mechanisms than

parasystole

Page 42: Predicting the Risk of Sudden Cardiac Death

Heartprint from a patient who had sudden cardiac death - Record 47 -

PhysioBank’s Sudden Cardiac Death Database

Page 43: Predicting the Risk of Sudden Cardiac Death

Potassium channel defects

• Long QT due to repolarization defects

• Possibility for early afterdepolarizations

leading to PVCs and inducing tachycardia

• Rule of “bigeminy” – bigeminal patterns

tend to perpetuate. Possible reason –

following a PVC there is a compensatory

pause leading to a longer recovery time

and long QT with a PVC

Page 44: Predicting the Risk of Sudden Cardiac Death

New Indices for Risk Assessment

• The coupling interval (short versus long)

• The maximum value of the NIB

• Examine data from the CARISMA study –

(data from patients who have had a heart

attack and who have poor cardiac

function)

(Lerma, Ghanem, Gorelick, Glass, Huikuri,

2013)

Page 45: Predicting the Risk of Sudden Cardiac Death

Kaplan-Meier plots for CARISMA

Data

Page 46: Predicting the Risk of Sudden Cardiac Death

• “Given the desirability of accurate risk

stratification [for sudden cardiac death]

and the long history of research in this

area, it is important to understand why the

field is not further advanced.” Jeffrey

GOLDBERGER et al. Circulation 2011

• Possible Reasons : (i) impossible; (ii) the

wrong people are studying the problem;

(iii) the data concerning arrhythmias is not

good enough; (iv) new strategy needed –

mechanisms in individual patients.

Page 47: Predicting the Risk of Sudden Cardiac Death

Acknowledgments

Collaborators: Michael Guevara, Alvin Shrier, Glen Ward, Ary Goldberger, Jacques Bélair, Hiroyuki Ito, Verena Schulte-Frohlinde, Taishin Nomura, Eugene Stanley, Plamen Ivanov, Gil Bub, Hortensia González, Yoshihiko Nagai, Katsumi Tateno, Kevin Hall, Jacques Billette, David Christini, Claudia Lerma, Chiu Fan Lee, Ben Steinberg, Alex Hodge, Min-Young Kim, Bart Borek, Alex Gorelick, Raja Ghanem, Heikki Huikuri, TK Shajahan, Thomas Quail, Manli Marquez

Funding Sources: NSERC, CIHR, MITACS, NIH (National Resource for Complex Physiologic Signals), Canadian Heart and Stroke Foundation, Medtronic