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Glucose Derangements and Brain Function in Neonatal Encephalopathy
by
Elana Pinchefsky
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Science University of Toronto
compression, rectifying, smoothing, and time compression. The amplitude display is linear
between 0 to 10 mcV and logarithmic from 10 to 100 mcV. Detailed evaluation is lost, however
long-term trends and evolution of the background over time become more evident with the
compressed time scale (Hellström-Westas et al., 2006).
For aEEG, a minimum of 2 electrodes and a reference are placed for a single channel
montage or 4 electrodes and a reference for a dual channel montage (provides one channel from
each hemisphere) (Figure 1.7). Dual-channel aEEG recordings can provide information about the
presence of unilateral brain injury and can improve seizure identification in these neonates (van
Rooij, de Vries, et al., 2010). Originally, single-channel aEEG placed leads over the parietal
region (P3 and P4) because it is over the cerebrovascular watershed, an area at high risk for
acquired injury. However, the adjacent C3 and C4 channels probably provide similar data for
single-channel aEEG. Most contemporary machines allow the display of dual-channel recordings
(usually C3-P3 and C4-P4), along with the raw EEG from which the aEEG signals are derived.
When reduced channel aEEG is obtained simultaneously to complement ongoing cEEG
monitoring, P3 and P4 may be added to the conventional neonatal recording montage (Shellhaas
et al., 2011).
Figure 1.7 Standard placement for EEG electrodes A) according to the 10-20 system modified for neonates. B)
aEEG lead placement for dual channel aEEG recordings
A. B.
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Generally, there is good correlation between findings on EEG and aEEG (Hellström-
Westas et al., 2006; Shellhaas et al., 2008). aEEG can demonstrate background abnormalities and
sleep wake cycling. Hypothermia does not significantly affect aEEG background activity
(Burnsed et al., 2011; Horan et al., 2007). There are 2 main background classification systems
used. Al Naqeeb et al. created a background classification for term infants which includes 3
categories. The classification is based on aEEG amplitudes. aEEG background is classified as
normal amplitude when the upper margin of aEEG activity is >10 mcV and the lower margin >5
mcV; as moderately abnormal amplitude when the upper margin of aEEG activity is >10 mcV
and the lower margin <5 mcV; and as suppressed when the upper margin of aEEG activity is <10
mcV and lower margin <5 mcV (al Naqeeb et al., 1999).
An alternate classification proposed by Hellstrom-Westas et al. classifies aEEG
background patterns using pattern recognition based on EEG terminology, including
categorization of sleep-wake cycling, which could be used in all newborns. The 5 background
categories were defined as: continuous with minimum (lower) amplitude around (5 to) 7 to 10
mcV and maximum (upper) amplitude of 10 to 25 (to 50) mcV; discontinuous with minimum
amplitude variable, but below 5 mcV, and maximum amplitude above 10 mcV; burst-
suppression as a discontinuous background with minimum amplitude without variability at 0 to 1
(2) mcV and bursts with amplitude >25 mcV; low voltage as a continuous background pattern of
very low voltage (around or below 5 mcV); and inactive, flat describes a primarily inactive
(isoelectric tracing) background below 5 mcV (Figure 1.8). A classification for SWC in the
aEEG was also proposed. SWC is characterized by smooth sinusoidal variations of the
bandwidth, mostly in the minimum amplitude. A wider bandwidth represents the discontinuous
background activity during quiet sleep (tracé alternant EEG in term infants), and the narrower
bandwidth corresponds to the more continuous activity seen during wakefulness and active sleep.
SWC was classified in 3 categories: no SWC (where there is no cyclic variation of the aEEG
background); imminent/immature SWC (when there is some, but not fully developed cyclic
variation of the lower amplitude, and not developed as compared with normative gestational age
representative data); and developed SWC (when there are clearly identifiable sinusoidal
variations between discontinuous and more continuous background activity, with cycle duration
>20 min) (Hellström-Westas et al., 2006).
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Figure 1.8 Examples of aEEG background patterns.
Reproduced from: Stolwijk LJ, Weeke LC, de Vries LS, van Herwaarden MYA, van der Zee DC, et al. (2017)
Effect of general anesthesia on neonatal aEEG—A cohort study of patients with non-cardiac congenital anomalies.
PLOS ONE 12(8): e0183581.
Classifications similar to the pattern classification system had been previously used and
described in cohorts of neonates with HIE and were shown to be correlated with prognosis.
Neonates with continuous normal voltage (CNV) or discontinuous normal voltage (DNV)
background patterns during the first 6 hours after birth were likely to survive without sequelae,
whereas neonates with burst suppression (BS), continuous low voltage (CLV) or flat tracings
(FT) had a high risk for death or neurodevelopmental disability (Hellstrom-Westas et al., 1995;
ter Horst et al., 2004; Toet et al., 1999). aEEG monitoring can be a useful tool in neonates with
HIE to monitor degree of encephalopathy and to detect seizures, as well as to assess recovery
and predict outcome. Its role in outcome prediction will be discussed further below.
Seizures can be identified on aEEG as an abrupt, transient rise in the lower and upper
margins and narrowing of the bandwidth. aEEG can detect approximately one third of neonatal
seizures. Seizures that are infrequent, short (<30 seconds), and low amplitude are difficult to
detect (Lawrence et al., 2009; Mastrangelo et al., 2013; Rakshasbhuvankar et al., 2015;
Shellhaas, Soaita, et al., 2007). Seizure detection may be dependent on user experience (Frenkel
et al., 2011) and on availability of the raw aEEG for confirmation, which improved the
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sensitivity to 76% and specificity to 78% in one study (Shah et al., 2008). Still, aEEG is superior
to clinical detection, as clinicians are poor at clinical recognition of seizure and nonseizure
movements (Malone et al., 2009) and up to 85% of seizures in neonates are subclinical
(electrographic only), meaning they would go undetected without EEG (Bye et al., 1995; Glass
et al., 2016; Naim et al., 2015; Nash et al., 2011; Wietstock et al., 2016; Wusthoff et al., 2011). The majority of seizures occur in the central region (56%), occasionally in the temporal (25%)
and occipital regions (14%), and rarely in the frontal regions (5%) (Shellhaas & Clancy, 2007).
Thus, placement of electrodes over the central region is important for seizure detection.
1.7 Neurophysiology and neurodevelopmental outcomes
1.7.1 Background and neurodevelopmental outcomes
aEEG background abnormalities and recovery have been shown to be predictive of long-term
neurodevelopmental outcome in term neonates with HIE (Awal et al., 2016; Azzopardi & Toby
study group, 2014; Chandrasekaran et al., 2017; Dunne et al., 2017; Skranes et al., 2017;
Spitzmiller et al., 2007; Thoresen et al., 2010; van Laerhoven et al., 2013). An early normal or
mildly abnormal EEG is predictive of a favorable neurodevelopmental outcome (Awal et al.,
2016; Murray et al., 2009; Pressler et al., 2001; Toet et al., 1999; Weeke et al., 2016). Whereas,
EEG background abnormalities, in particular burst suppression, low voltage or a flat trace is
associated with poor long-term neurodevelopmental outcomes (at 12 months of age or older)
(Awal et al., 2016). However, even neonates with mild EEG abnormalities have reduced intact
survival rates. A study that performed cognitive and motor assessments at 5 years of age showed
that 25% of neonates with mild EEG abnormalities 6 hours after birth had lower cognitive scores
that the healthy comparison group recruited at birth (Murray et al., 2016). Also, serial recordings
improve the prognostic accuracy of EEGs, as progressive improvements in the background are
associated with more favorable outcomes (Murray et al., 2009; Nash et al., 2011; Pressler et al.,
2001), and the worsening of a background pattern or the development of an abnormal
background is predictive of unfavorable outcomes (Khan et al., 2008). Thus, the evolution of
aEEG background over time, using a classification system that defines specific patterns of
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change in aEEG over the course of therapeutic hypothermia may also be useful for outcome
prediction, rather than grading only at a discrete time interval (Sewell et al., 2018).
A meta-analysis of 8 studies prior to the introduction of therapeutic hypothermia showed
a pooled sensitivity of 91% and specificity of 88% of early severe aEEG backgrounds to
A recent report by Arndt et al.5 high-lights the importance of subclinical seiz-ures among children with traumatic brain injury (TBI) requiring ICU admission. Among the 87 children studied, a remark-able 42.5% experienced seizures, with subclinical seiz ures (seizures without a clear clinical correlate that would have not been detected without cEEG monitoring) occurring in 16.7% and subclinical status epilepticus (subclinical seizures lasting >15 min, or more than three subclinical seizures per hour) in 13.8% of children. Subclinical seizures and status epilepti-cus were more common among younger children, those with abusive head trauma, and those with intra-axial haemorrhage. Children with subclinical seizures and status epilepticus required increased length of hospital stay and demonstrated worse outcomes, with lower scores on the King’s Outcome Scale for Childhood Head Injury (KOSCHI; scores range from 1–5, with 1 referring to death and 5 indicating good recovery) at hospital discharge.
The study by Arndt et al.5 is important and novel because it is the first to apply cEEG monitoring to a consecutive cohort of children with TBI who were admitted to a paediatric ICU, but a few limit ations must be borne in mind. First, the authors chose to combine two quite hetero geneous cohorts from different institutions, some-what limiting the study’s generalizabil-ity. Second, the authors relied on clinical EEG reporting, which might have been influenced by knowledge of the patient’s clinical status. Although inter-rater reli-ability of EEG reporting was assessed, it was performed using a separate sample of cEEG recordings from another set of pediat ric patients rather from the study parti cipants. It is also unclear what pro-portion of children experienced clinical seizures prior to cEEG monitoring, how soon after injury cEEG monitoring of patients began, and how long it took to record their first seizure. Despite these limitations, this report makes a compelling case that children with moderate or severe TBI—in particular, younger children, and those with abusive head trauma or intra-axial haemorrhage—should undergo cEEG monitoring.
Subclinical seizures present a pressing clinical dilemma: they clearly represent an important biomarker of acute brain injury, but their potential contribution to brain injury remains controversial. Plausible mechanisms for subclinical-seizure-induced
brain injury identified in experimen-tal animal models include excito toxicity, activation of inflammatory cascades, and hypoxia–ischaemia. Evidence for a similar deleterious effect of subclinical seizures in critically ill children and adults is mount-ing. Among adults with acute brain injury, subclinical seizures have been associated with focal metabolic dysfunction, increased intra cranial pressure, and ipsilateral hippo-campal atrophy.6,7 Among both adults and children, electrographic seizures and status epilepticus have been associated with increased mortality and worse short-term outcomes, even after controlling for diag-nosis and injury severity.8,9 Conclusive proof of a causal link between seizures and worse outcomes has, however, remained elusive. To demonstrate that reduction of seizure burden actually improves outcomes, an inter ventional study would probably be required. Fortunately, one such multi centre study is currently underway in critically ill adults.10
The potential for subclinical seizures to cause harm is likely to depend on both the seizure burden and the type and severity of the underlying brain injury (Figure 1). Among patients with anoxic brain injury, the probability of a poor outcome might be so high that seizures have a minimal additional effect on outcome. Among patients with generalized epilepsy of pre-sumed genetic origin (formerly referred to as idiopathic generalized epilepsy), a high seizure burden might be respon-sible for persistent encephalopathy, but once seizures are aborted, patients may recover with few or no long-term sequelae. Among patients with acute focal stroke,
meningo encephalitis, TBI, or septic or meta bolic encepha lopathy, however, out-comes are more variable; in these situ ations, seizures might have a more important modu lating effect on neurological outcome. A clearer understanding of the relation-ships between seizure burden and outcome in the context of a patient’s diagnosis is urgently needed to help guide clinicians about when, and how aggressively, to treat subclinical seizures.
cEEG monitoring remains beyond the reach of many medical centres because of the considerable equipment and staffing costs. Qualified EEG technologists required for electrode application and clinical neuro-physiologists required for interpretation are in short supply, particularly outside normal working hours. Creative strategies can, however, be applied to make cEEG monitoring more feasible. Intensive care nurses can be taught to apply a limited array of EEG electrodes or electrode caps, reducing reliance on EEG technologists. Quantitative EEG trending algorithms that time compress and simplify the EEG display can help to speed up EEG interpretation by clinical neurophysiologists, and enable bedside caregivers to more easily screen for seizures. These and other challenges must be overcome to enable greater access to brain monitoring in both resource-rich and resource-poor settings.
The care of critically ill patients with acute brain injury is challenging. The clinical neurological assessment is often compromised by the use of sedating or paralysing medications. In these situations, the addition of continuous brain monitor-ing has the potential to provide important
Figure 1 | Schematic illustration of potential relationships between seizure burden and outcome. The potential deleterious effects of seizures in the context of acute brain injury are likely to depend on the underlying aetiology. The probability of poor outcome might increase linearly or exponentially with increasing seizure burden, or a threshold might exist, above which seizures are harmful.
Table 1.1 Recent studies of interest on outcomes following electrographic seizures and electrographic status epilepticus in children and neonates
References Population and comorbidities
Type of monitoring
Seizure threshold
When outcome was assessed
Main Findings
Studies in Critically Ill Children Vaewpanich et al. 2016
16 patients with TBI
cEEG SE = ongoing seizure >30 min
Short-term (at discharge and 4-6 wks post- discharge)
- All patients with seizures had poor outcome on the GOS-E peds, and speech pathology neurocognitive/functional evaluations (SPNFE)
Abend et al. 2015
60 patients with normal neurodevelopment prior to PICU admission for AMS
cEEG
SE = seizure >30 min or SB >50% in any 1-h epoch
Long-term (median 2.6 years)
- ES and ESE were associated with worse adaptive functioning scores on multivariate analysis - Non-significant trend to worse scores on behavioral-emotional & executive function scales after ESE
O'Neill et al. 2015
144 patients with TBI
cEEG SE = seizure >30 min or >50% of EEG recording
Short-term (at discharge)
- Presence of seizures did not correlate with discharge disposition - Seizures associated with longer hospital and ICU stay (no difference between ESE compared to ES)
Payne et al. 2014
259 paediatric and neonatal patients admitted to ICU with clinically ordered cEEG
cEEG Maximum hourly SB >20%/h
Short-term (at discharge)
- No association with mortality - ↑ probability and magnitude of neurologic decline with SB > 20% (12 minutes) - On multivariate analysis, every 1% ↑ in maximum hourly SB was associated with a 1.13 odds of neurological decline
Sanchez Fernandez et al. 2014
98 patients with CSE who underwent cEEG monitoring
cEEG SE = seizure >30 min or >50% SB in any 1-h epoch
Short-term (at discharge)
- No association with mortality - Longer PICU stay if ES and significantly longer if ESE
Wagenman et al. 2014
60 patients with normal neurodevelopment prior to PICU admission for AMS
cEEG SE = seizure >30 min or SB >50% in any 1-h epoch
Long-term (median 2.7 years)
- ESE, but not ES, associated with worse long term functional outcome scores and lower health-related quality of life - ESE associated with ↑ risk of subsequent diagnosis of epilepsy
Abend et al. 2013
550 consecutive PICU patients who underwent cEEG monitoring
cEEG SE = seizure >30 min or SB >50% in any 1-h epoch
Short-term (at discharge)
- ESE, but not ES, associated with greater odds of in-hospital death - ↑ PICU LOS if ESE compared to no ES, and compared to ES without ESE
Arndt et al. 2013
87 patients with TBI
cEEG
SE = seizure >15 min or >3 seizures/h
Short-term (at discharge)
- ESE associated with ↑ hospital LOS (no difference in PICU LOS) - ES and ESE associated with ↓ discharge KOSCHI score
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Piantino et al. 2013
19 patients on ECMO
cEEG
SE = seizure >30 min or SB >50% in any 1-h epoch
Short-term (at discharge)
- ES were associated with structural abnormality on MRI - ES not associated with ↑ mortality -Trend for those with ES having more prolonged ECMO course - No difference in rate of complications
Topjian et al. 2013
200 patients with acute encephalopathy
cEEG SE = seizure >30 min or SB >50% in any 1-h epoch
Short-term (at discharge)
- ESE, but not ES, were associated with ↑ risk mortality - No difference in PICU or hospital LOS - ESE, but not ES, were associated with PCPC worsening
Gwer et al. 2012
82 patients with acute nontraumatic coma
cEEG ES = >6 sec; ESE = seizure >30 min or ≥3 seizures/hr
Short-term (at discharge)
- ES and SE associated with greater risk of poor outcome (death or gross motor deficits at discharge) - SE had even greater risk of poor outcome
Kirkham et al. 2012
204 comatose pediatric and neonatal patients
1-3 channel cEEG/ aEEG
Number, total duration, and longest ES
Short-term and long-term for 84 of the survivors
- ES associated with unfavourable short-term outcome (severe handicap or vegetative state) - No association with mortality
Studies in Critically Ill Neonates Glass et al. 2016
426 neonates with clinically suspected +/or EEG seizures
cEEG
High SB=≥7 ES; ESE=seizures >50% of at least 1-h of recording
Short-term (at discharge)
- High SB was a risk factor for mortality, ↑ hospital LOS, and abnormal neurological exam at discharge
Kharoshankaya et al. 2016
47 neonates with HIE
cEEG Total SB >40 min. Maximum hourly SB > 13 min
Long-term (24-48 months)
-Presence of seizures alone was not associated with abnormal outcome - A total SB of >40 min and a maximum hourly SB of >13 min/hr were associated with ↑ risk of abnormal development
Srinivasakumar et al. 2015
69 neonates with HIE
cEEG SB recorded in seconds
Short-term and long-term (18-24 months)
- ↑ SB associated with higher brain injury score - ↑ SB in combined cohort associated with lower performance on all 3 domains of BSID (cognitive, motor, language)
Shah et al. 2014
85 neonates with HIE
aEEG
High SB = SB >15 min per 1-h epoch ESE = SB >30 min per 1-h epoch
Short-term (MRI brain at median 9 days of life)
- High SB independently associated with greater injury on MRI (adjusted for 10 min Apgar and abnormal aEEG background)
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Glass et al. 2011
56 neonates with HIE
cEEG
SE = seizure >30 min or SB >50% of 1–3 hrs of recording time
Short-term (MRI brain at median 5 days of life)
- Moderate to severe MRI injury more common in newborns with seizures, and present in all with SE - Moderate to severe MRI injury associated with seizures that were multifocal, later onset, and ongoing seizures following 20 mg/kg phenobarbital
Nash et al. 2011
41 neonates with HIE
cEEG
SE = seizure >30 min or SB >50% of 1–3 h of recording time
Short-term (MRI brain at median 5 days of life)
- Isolated or recurrent seizures were more frequent in patients with moderate to severe MRI injury compared with those with no or mild injury - SE was only seen in newborns with moderate to severe MRI injury
van Rooij et al. 2010
42 neonates with HIE
aEEG
Single seizure, ≥3 seizures per 30 min, or SE = seizure >30 min
Short-term (MRI brain at mean 5.5 days of life)
- Severity of brain injury on MRI associated with a longer duration of seizure patterns in the blinded group and the whole cohort
Studies in Neonates and Infants with CHD Undergoing Cardiac Surgery Naim et al. 2015
161 neonates with CHD
cEEG
SE = seizure >30 min or SB >30 min in any 1-h epoch
Short-term (at discharge)
- ↑ Mortality among neonates with seizures - No difference in cardiac ICU or hospital LOS
Gaynor et al. 2013
132 neonates and infants (<6 mo) with CHD
cEEG
ES >10 sec Long-term (4 years)
- ES were associated with worse executive function and impaired social interactions/restricted behaviour -ES were not associated with worse cognitive, language, or motor skills
Bellinger et al. 2011
139 infants with CHD (TGA)
cEEG
ES >6 sec Long-term (16 years old)
-ES in the postoperative period was the medical variable most consistently related to worse outcomes (academic achievement, memory, executive function, visual-spatial skills, and social cognition)
aEEG, amplitude integrated electroencephalogram; AMS, altered mental status; BSID, Bayley Scales of Infant Development; cEEG, continuous electroencephalogram; CHD, congenital heart disease; CSE, convulsive status epilepticus; ECMO, extracorporeal membrane oxygenation; ES, electrographic seizures; GOS-E Peds, Glasgow Outcome Scale scores; HIE, hypoxic-ischemic encephalopathy; KOSCHI, King’s Outcome Scale for Childhood Head Injury; LOS, length of stay; PCPC, Paediatric Cerebral Performance Category; SB, seizure burden; SE, status epilepticus; TGA, transposition of the great arteries.
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CRITICALLY ILL NEONATES
Several studies have now shown that neonatal seizures and status epilepticus are associated with
worse outcomes even after adjusting for potential confounding variables, and support the
hypothesis that seizures worsen existing brain injury (see Table 1.1). However, increasing
seizure burden is often accompanied by greater use of antiseizure medications such as
phenobarbital and phenytoin, which have been shown to have neurotoxic effects in rodent
models (Bittigau et al., 2002; Bittigau et al., 2003). Disentangling the potential deleterious
effects of seizures from the potential toxicity due to their treatment remains an important
challenge.
Short-term and surrogate outcomes
Studies in neonates undergoing therapeutic hypothermia for HIE have shown a relationship
between higher seizure burden and worse MRI injury scores in univariate analysis (Glass et al.,
2011; Nash et al., 2011; Srinivasakumar et al., 2015; van Rooij, Toet, et al., 2010). In two of
these studies, all neonates with status epilepticus had moderate to severe injury on imaging
(Glass et al., 2011; Nash et al., 2011). In multivariable analysis, a higher seizure burden on
aEEG (more than 15 minutes in any 1-h epoch) was associated with greater injury on MRI,
independent of aEEG background and Apgar score at 10 min (Shah et al., 2014a).
A multicenter cohort study described 426 term and preterm neonates with various
diagnoses who underwent cEEG to characterize clinically suspected seizures or screen for
subclinical seizures. Electrographic seizures were found in 82% of neonates. In a multivariable
analysis, high seizure burden (defined as ≥7 electrographic seizures in total) was associated with
mortality, longer hospital stay, and abnormal neurological examination at discharge (defined as
abnormalities in consciousness, tone, and/or reflexes) (Glass et al., 2016).
Long-term outcomes
Seizures have also been linked to long-term neonatal outcomes. In a study of 47 neonates with
HIE who underwent cEEG monitoring, outcomes were assessed at 24 to 48 months using either
the Griffiths Mental Development Scales or BSID, as well as clinical diagnoses of cerebral palsy
or epilepsy. The presence of seizures per se was not associated with abnormal long-term
outcomes, but high total seizure burden and maximum hourly seizure burden were associated
with abnormal outcome. For every 1-min increase in total seizure burden the odds of an
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abnormal outcome increased by 2.2%, and for every 1-min increase in maximum hourly seizure
burden the odds of an abnormal outcome increased by 16%. The seizure burden thresholds used
to predict greater odds of abnormal outcome were a total seizure burden more than 40 min or a
maximum hourly seizure burden more than 13 min, and these findings were independent of HIE
severity or the use of therapeutic hypothermia (Kharoshankaya et al., 2016).
Further support for clinically relevant electrographic seizure burden thresholds include
observations that a total neonatal seizure count more than 75 was associated with microcephaly,
severe CP, and failure to thrive (McBride et al., 2000), an ‘ictal fraction’ more than 10 min was
associated with an unfavorable neurodevelopmental outcome at 18 months (Pisani et al., 2008),
and that status epilepticus was a stronger risk factor than recurrent seizures for poor
neurodevelopmental outcome and postneonatal epilepsy at 24 months of age (Pisani et al., 2007).
NEONATES AND INFANTS WITH CONGENITAL HEART DISEASE
Post-operative seizures in children with CHD have been associated with worse outcome. A study
that systematically utilized cEEG for 48 hours in 161 neonates upon return to the ICU following
neonatal cardiac surgery found electrographic seizures in 8%, of whom 62% had ESE. Seizures
were subclinical in 85% of neonates. The presence of electrographic seizures was associated with
higher mortality (Naim et al., 2015).
Studies examining long-term outcomes after cardiac surgery have demonstrated that later
neurodevelopmental difficulties can emerge that may not have been evident at younger ages. The
greater academic and psychosocial demands placed on children as they grow older can reveal
neurodevelopment abnormalities potentially associated with increased seizure burden in the
developing brain. From an initial cohort of 178 neonates and infants with cardiac surgery
requiring cardiopulmonary bypass who underwent 48 h of cEEG monitoring in the postoperative
period, neurodevelopmental outcomes assessed at age 1 year in 114 children showed that
postoperative electrographic seizures were not associated with worse outcomes on the BSID,
except in the subset of children with frontal-onset seizures (Gaynor et al., 2006). When 132
children of the original cohort were subsequently assessed at age 4 years, postoperative
electrographic seizures were associated with worse executive function and impaired social
interactions or restricted behaviours, but were not associated with cognitive, language, or motor
outcomes (Gaynor et al., 2013).
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The Boston Circulatory Arrest Study reported on successive long-term follow-up at age
1, 4, 8, and 16 years in a cohort of 171 infants with transposition of the great arteries that
underwent cardiac surgery requiring cardiopulmonary bypass during infancy. cEEG was
performed for at least 2 h pre-operatively, during surgery and for 48 h following surgery.
However, cEEGs were not interpreted clinically; therefore, electrographic seizures remained
unrecognized and untreated. Electrographic seizures were found in 20% and clinical seizures in
6% (Helmers et al., 1997). At age 4 years, increased seizures in the postoperative period were
associated with lower mean intelligent quotient scores and an increased risk of abnormalities on
neurological exam (Bellinger et al., 1999). At age 8 years, postoperative seizures were associated
with social and attention problems (Bellinger et al., 2009). At age 16 years, seizures in the
postoperative period was the medical variable most consistently related to worse outcomes,
specifically academic achievement, memory, executive function, visual-spatial skills, and social
cognition (Bellinger et al., 2011).
CONCLUSIONS
Seizures are a cardinal sign of underlying brain injury. There is mounting evidence that higher
seizure burden during critical illness in neonates and children is associated with worse
neurodevelopmental outcomes, even after adjusting for potential confounding variables such as
heart rate changes); or unable to assess (e.g. caregiver in front of patient, patient not on video).
Every 6 hours, the presence of reactivity, variability and state changes was graded as 0:present;
1:some/immature; or 2:none.
2.4 Continuous interstitial glucose monitoring
Medtronic iPro2TM professional continuous glucose monitors with EnliteTM sensors (Medtronic
of Canada Ltd., Brampton, Ontario) were placed as early as possible after study enrolment, either
during transport or upon admission to hospital. The sensors were inserted interstitially into the
lateral aspect of the thigh, secured with clear adhesive dressing. In both term and preterm
neonates, this device has been used safely over a 7-day period and has been shown to be well
tolerated (Beardsall et al., 2013; Harris et al., 2010). The iPro2 monitor is a blinded device that
records average interstitial glucose concentrations every 5 minutes. This data was not made
available to the care team due to limited evidence for treating newborns based on these measures.
Clinicians treated newborns for hypoglycemia or hyperglycemia based on current standard of
care using intermittent glucose testing. As per institutional protocol, blood glucose
concentrations less than 2.7 mmol/L were treated with intravenous dextrose or glucagon.
Hyperglycemia was treated with insulin infusion as clinically indicated.
Continuous glucose monitoring was continued for 72 hours, after which the data was
downloaded onto a Windows-based notebook computer running Medtronic software. Only
laboratory blood glucose values were used to calibrate the CGM. Due to software cut-offs,
interstitial glucose readings below 2.2 and above 22.2 mmol/L were recalculated manually from
the raw data.
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The definition of hypoglycemia remains controversial and there are variable thresholds
used in different consensus guidelines and research studies. On the basis of neurophysiological
and neurodevelopmental outcome studies, a commonly used definition of neonatal hypoglycemia
is less than 2.6 mmol/L (Koh et al., 1988; Lucas et al., 1988). However, the PES advises that a
‘safe target’ during the first 48 hours should be close to the mean for a healthy newborn and
above the threshold for neuroglycopenic symptoms (2.8 mmol/L) (Thornton et al., 2015).
Additionally, reference ranges in healthy term newborns may not be appropriate in infants at risk
for impaired metabolic adaptation and at risk for adverse neurodevelopment, such as with
perinatal asphyxia (Harding et al., 2017). The PES considers that in hypoketotic conditions,
ketones and lactate may not be available in sufficient concentrations to serve as an alternative
fuel for the brain and thus there is greater risk of brain energy failure and hypoglycemia-induced
brain damage (Thornton et al., 2015).
Accordingly, hypoglycemia was defined as blood or interstitial glucose ≤2.8 mmol/L and
hyperglycemia as glucose >8.0 mmol/L. Episodes of interstitial glucose derangements were
defined as two or more consecutive data points (≥10 minutes) outside the normal range. Episodes
of glucose derangement were treated as contiguous if they were separated by brief periods of
normoglycemia lasting ≤10 minutes.
For each subject, mean, minimum and maximum glucose levels were calculated based on
the interstitial measurements obtained over each 6-hour epoch. The area under the curve was
calculated as the area of interstitial glucose concentration over/under the normal glucose range
(hours*mmol/L), reflecting the extent and duration of the episode of interstitial glucose
derangement. Specifically, for hypoglycemia the area of interstitial glucose concentrations ≤2.8
mmol/L was calculated and for hyperglycemia the area of interstitial glucose concentrations >8.0
mmol/L was calculated. Glucose variability was quantified using the standard deviation and
mean glucose rate of change per hour (mmol/L/hour).
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2.5 Statistical analysis
All statistical analyses were computed using SPSS, version 20 (SPSS, Inc, Chicago, IL, USA).
Demographic data were summarized by standard descriptive statistics, including
median/interquartile range (IQR) or mean/standard deviation (SD) for continuous variables, or as
number and percentages for categorical variables.
2.5.1 Statistical analysis of aEEG monitoring
The primary analyses compared aEEG scores (background, sleep-wake cycling and seizure
scores) during 6-hour epochs containing episodes of hypoglycemia or hyperglycemia to
normoglycemic epochs using generalized estimating equations for repeated measures with a
linear scale response model. An independent correlation structure and robust variance estimators
were used. Comparisons were made relative to normoglycemia because a prior study reported
greater aEEG background discontinuity at plasma glucose concentrations both above or below
4.0 mmol/L (Wikstrom et al., 2011). Findings were adjusted for clinical markers of hypoxia-
ischemia severity: Apgar scores, umbilical artery pH, and base deficit. Secondary analyses
investigated the association of the aEEG scores with other glucose measures (mean, maximum or
minimum glucose values, duration of glucose disturbance, and area under the curve) and
measures of glucose variability (standard deviation and mean glucose rate of change per hour).
Two-tailed tests with p values of <0.05 were considered statistically significant.
77
Figure 2.1 Summary of aEEG analysis methods. Analysis was performed on 6-h epochs of concomitant CGM and
aEEG. CGM provides average interstitial glucose values every 5-min. Six-hour epochs of CGM were assessed for
the presence or absence hypo- or hyperglycemia, as well as calculation of the mean, minimum, maximum, area
under the curve, standard deviation and mean glucose rate of change in each 6-h epoch. aEEGs were scored every 6-
h for background, sleep wake cycling (SWC) and seizures. Background was graded as: 0:continuous normal voltage
(CNV); 1:discontinuous normal voltage (DNV); 2:burst suppression pattern (BS); 3:continuous low-voltage (CLV);
and 4:flat trace (FT). SWC was graded as: 0:developed cycling; 1:immature cycling; and 2:no cycling. Seizures
were graded as: 0:no seizures; 1:single seizure; 2:repeated seizures; and 3:status epilepticus.
2.5.2 Statistical analysis of cEEG monitoring
The primary analyses compared cEEG scores background scores and presence of seizures during
5-minute epochs with or without episodes of hypoglycemia or hyperglycemia to normoglycemic
epochs using generalized estimating equations for repeated measures with a linear scale response
model. An independent correlation structure and robust variance estimators were used. All
comparisons were again made relative to normoglycemia. Findings were adjusted for clinical
markers of hypoxia-ischemia severity: Apgar scores, umbilical artery pH, and base deficit.
Secondary analyses included comparison of mean interstitial glucose concentration in each 5-
CGM
aEEG background
aEEG SWC
aEEG seizures
aEEG 6hours 6hours 6hours
78
minute epoch with cEEG background scores and presence of seizures. A subgroup analysis was
performed examining the association of hyperglycemia with cEEG background scores only in the
8 patients that had occurrences of hyperglycemia on CGM monitoring as it is hypothesized that
neonates with hyperglycemia may have more severe HIE and thus worse brain activity.
Subgroup analysis was also performed examining the association of hypoglycemia with cEEG
background scores only in the 5 patients that had occurrences of hypoglycemia on CGM. Two-
tailed tests with p values of <0.05 were considered statistically significant.
Figure 2.2 Summary of cEEG analysis methods. Analysis was performed on 5-min epochs of concomitant CGM
and cEEG. CGM provides average interstitial glucose values every 5-min. The presence or absence hypo- or
hyperglycemia in each 5-min epoch as well as the mean interstitial glucose values every 5-min were assessed. cEEG
background was visually graded every 5-min as: 0:Continuous or tracé alternant; 1:tracé alternant with excessive
discontinuity; 2:tracé discontinu; 3:depressed and undifferentiated or burst suppression; 4:very low voltage. Seizures
were marked as present or absent, as well as the duration of seizure in each 5-min epoch.
CGM
cEEG background
cEEG seizure
cEEG5min 5min 5min
79
Chapter 3
Results 3
3.1 Prevalence of glucose derangements in cohort and treatments received
Eighty families were approached to participate in the study and 29 declined (enrollment rate
64%), accordingly 51 neonates were included for analysis. Overall, 41 of 51 neonates (80%) had
abnormal glucose values in the first 3 days of life when including both interstitial values as well
as blood glucose measurements obtained prior to insertion of the CGM. The mean age at the start
of CGM measurements was 8.9 hours of life (SD 3.9 h). There were 13 neonates with
hypoglycemia only (25%), 18 neonates with hyperglycemia only (35%) and 10 neonates with
both hypo- and hyperglycemia (20%). Of the first 51 neonates recruited, 2 had no available
interstitial glucose values. Twenty-five neonates had glucose derangements recorded on CGM
(51%). There were 7 neonates with hypoglycemia only (14%), 15 with hyperglycemia only
(31%) and 3 with both hypo-and hyperglycemia (6%).
During initial resuscitation after birth, 9 neonates required epinephrine, of which 6
developed hyperglycemia within 12 hours after birth (median 2.5 hours of life, IQR 1.94-8.9 h)
and one developed hyperglycemia at 12.67 hours of life. Thirty-three neonates had hypotension
requiring intervention (65%), of which 20 neonates (39%) had hypertension responsive to fluid
boluses. The other 13 neonates (25.5%) required further treatment, including dopamine,
dobutamine, epinephrine, norepinephrine, hydrocortisone or vasopressin. Four of these neonates
developed hyperglycemia after receiving vasopressors.
In the 49 patients with CGM monitoring, there were 22 episodes of hypoglycemia (≤2.8
mmol/L) in 10 neonates, of which there were 16 episodes with a minimum glucose ≤2.6 mmol/L
in 8 neonates. Only three of these episodes on CGM were detected and treated clinically with
D10W IV boluses. Overall, 17 neonates received treatment for hypoglycemia; 14 neonates
required treatment within the first 6 hours of life, 3 only had treatment for hypoglycemia after 6
hours of life, and 4 received both early and later treatment of hypoglycemia. Treatment of
hypoglycemia included IV dextrose boluses or glucagon (IM or infusion). Of the 28 neonates
80
with hyperglycemia episodes since birth, 16 had episodes within the first 6 hours of life. Seven
neonates received treatment for hypoglycemia prior to development of hyperglycemia. Four
neonates required an insulin infusion for treatment of hyperglycemia and all insulin infusions
were started after the first 6 hours of life.
3.2 Amplitude-integrated EEG
3.2.1 Study population
Of the 51 neonates recruited, 6 neonates were excluded from aEEG analysis (2 had no available
interstitial glucose values, 3 had only single channel (C3-C4) recordings and 1 had no aEEG
trace), leaving 45 eligible neonates. Demographic information is summarized in Table 3.1.
Compared to neonates who maintained normoglycemia on CGM, neonates with hypoglycemia
on CGM had higher mean gestational age and higher mean umbilical artery pH. One patient was
not cooled (did not meet institutional criteria for therapeutic hypothermia) and 4 patients died
during the neonatal period. Characteristics of excluded participants are found in Appendix Table
1.
81
Table 3.1 Clinical and demographic data of the full study cohort, and subgroups of neonates with hypoglycemia and hyperglycemia, who were compared to those who maintained normoglycemia on continuous interstitial glucose monitoring
FULL COHORT
n=45
NORMOGLYCEMIA n = 25
HYPOGLYCEMIA n = 7
HYPERGLYCEMIA n = 11
p value p value
Sex 21 ♀: 24 ♂ 12 ♀: 13 ♂ 3 ♀: 4 ♂ 1.000 5 ♀: 6 ♂ 1.000 Gestational age (weeks), mean (SD)
5.2.1 Previous studies validating use of quantitative EEG analyses
Quantitative automated analysis of EEG features may improve the objectivity of EEG
assessment in newborns. Several studies have compared quantitative EEG features with
neuroimaging findings and neurodevelopmental outcomes in neonates with HIE.
Several features of burst suppression have been assessed. A study calculated EEG
discontinuity with a novel algorithm and showed that higher mean discontinuity at 24h and 48 h
were associated with severe cerebral tissue injury on MRI and unfavorable neurodevelopmental
outcome at 24 months old (Dunne et al., 2017). Quantitative analysis of the total suppression
length has also been associated with death or neurodevelopmental disability. The authors argue
that short artefacts can be mistakenly classified as bursts with an automated burst suppression
classifier and can significantly influence the mean IBI but would only slightly reduce the total
suppression length (Flisberg et al., 2011). Iyer et al. examined the distributions of burst area and
duration and their interrelationships, demonstrating their ability to predict neuroimaging and
neurodevelopmental outcomes in a cohort of 20 neonates with HIE. They had extracted 3 novel
burst metrics for each EEG: mean burst duration and its coefficient of variation, scaling exponent
of the cumulative distribution function of the burst areas in each recording, and slope value based
on the relationship between burst duration and burst area (Iyer et al., 2014).
Other quantitative measures have also been assessed in cohorts of neonates with HIE.
The spectral frequency content of bursts occurring during burst suppression can be differentiated
from those during a normal tracé alternant pattern. There is a lower amount of low-frequency
activity in periods of burst suppression, with the relative amount of low- and high-frequency
activity being the parameter that best discriminated between the groups (Thordstein et al., 2010).
A retrospective study showed that total EEG power calculated at a mean age of 8.9 h, was
significantly higher in neonates with no or mild injury on MRI compared to those with
moderate/severe MRI injury (Jain et al., 2017). In a preterm fetal sheep model of HIE, early
hypothermia started within 30 minutes of asphyxia was associated with faster recovery of
spectral edge frequency, reduced seizure burden, and less suppression of EEG power and
amplitude and corresponded with reduced neuronal loss and microglial induction in the striatum
on pathological examination (Wassink et al., 2015). Temko et al. developed a support vector
105
machine classifier for prediction of outcome in neonates with HIE using a multimodal
combination of routine clinical markers, EEG features and heart rate parameters. They evaluated
for neurodevelopmental outcome prediction at 24 months in newborn infants with HIE and
identified 12 multimodal features that provided promising prediction results, 9 of which were
EEG features and 3 were clinical features. EEG achieved the highest performance as a predictor
of clinical outcome (Temko et al., 2015).
Quantitative EEG measures have also been used to assess whether EEG changes occur
with changes in core body temperature during mild therapeutic hypothermia for HIE. A study
using both visual and quantitative EEG measures during rewarming in 15 neonates with HIE
showed that rewarming was associated with a worsening of visual EEG scores which
corresponded to an increase in EEG discontinuity, and was more pronounced in newborns with
severe than moderate HIE. They also examined absolute magnitude and relative spectral
magnitudes and noted an increase in the relative magnitude of slower delta and a decrease in
higher frequency theta and alpha waves with rewarming (Birca et al., 2016). However, another
study that assessed several quantitative features of the aEEG in 10 neonates with HIE found that
severity of HIE but not core body temperature affected the quantitative EEG features (Burnsed et
al., 2011).
As with visual analysis, maturational changes are seen in several quantitative features
with increasing gestational age in preterm neonates with normal neurological follow-up,
including maturational changes in spectral measures with a shift from lower to higher
frequencies, decrease IBI length and length of discontinuous activity (periods with high
interburst-burst ratio) and increase in continuous activity (Niemarkt et al., 2010; Niemarkt et al.,
2011) as well maturational changes in spectral power in EEG bursts which are believed to
correspond to ongoing gyration and postnatal white matter maturation (Jennekens et al., 2012).
Minimal changes in the power spectrum in the first 3 days of life are also noted in healthy term
neonates with normal neurocognitive function at 4 years of age. When comparing the first 6
hours of life to 48 to 72 hours of life, there is overall higher relative power spectrum in the delta
frequency band and lower alpha/delta ratio at 6 hours of life (Castro Conde et al., 2017). Thus,
gestational age and temporal evolution since birth should be considered in analysis of
quantitative measures of discontinuity and spectral measures.
106
Of note, quantitative EEG measures such as amplitude, continuity (skewness, kurtosis
and discontinuity) and frequency content are associated with visual interpretation of the EEG
background. A study had 2 expert electroencephalographers visually grade EEGs of 54 neonates
into 4 categories and showed that a combination of quantitative EEG measures could accurately
predict EEG/HIE grade (Korotchikova et al., 2011). Another study developed a cerebral health
index score using multiple components of the EEG (power spectrum and spectral edge
frequency, entropy, volatility and sub-band spectral entropy) and compared it to clinical
measures of severity of HIE (based on Sarnat scores and visually interpreted EEG). The cerebral
health index score was able to distinguish between normal controls and moderate and severe
encephalopathy and normal versus severe EEG groups (Hathi et al., 2010). Other groups have
also used quantitative EEG features to develop automatic grading scales of the degree of
abnormality of the neonatal EEG (Lofhede et al., 2010; Stevenson et al., 2013) as well as
automated classification of sleep cycling (or referred to with the more general term, brain
activity cycling) (Stevenson et al., 2014).
Furthermore, detrended fluctuation analysis (DFA) has been discussed for CGM as a
method for analyzing scaling behavior in time series and can also be used to investigate the
scale-free amplitude modulation of ongoing neuronal oscillations (Hardstone et al., 2012). In a
complex system, scale-free dynamics can give rise to long-range temporal correlations, which
can be assessed from the EEG using DFA. Multifractal DFA (MF-DFA) can characterize time
series with multiple co-existent dynamic processes that may give rise to the temporal local
fluctuations, including of both small and large magnitudes (Matic et al., 2015). In a cohort of
term neonates with HIE the feasibility of DFA and MF-DFA and its correlation to EEG
background grades was assessed. They demonstrated that DFA could distinguish different grades
of abnormality in the background EEG activity in neonates with HIE, and MF-DFA was superior
to conventional DFA in distinguishing between EEG grades. MF-DFA parameters were also
significantly correlated to IBI intervals. They had visually inspected a large number of DFA
plots and noted that the trends were often multiphasic, with very clear “crossover points” (where
there was a change of slope in the fluctuation function), which are suggestive that the EEG signal
might exhibit multifractal behavior. They thus determined that MF-DFA better represents
neonatal EEG dynamics (Matic et al., 2015).
107
Considering glucose derangements, few previous studies have examined the acute EEG
changes associated with hypo- or hyperglycemia, as previously discussed, some of which used
quantitative EEG measures. A study in young children that was able to detect changes during
hypoglycemia performed quantitative spectral analysis of EEG during a gradual decline in
glucose concentrations induced using insulin. At a plasma glucose of 4 mmol/L, an increase in
delta and theta amplitudes were seen in both groups. At 3 mmol/L, there was a further and more
widespread increase in low-frequency EEG activity, with greater slowing and epileptiform
discharges seen in the diabetic than nondiabetic children (Bjorgaas et al., 1998). Several studies
detected changes with hyperglycemia using quantitative EEG measures. In extremely preterm
neonates, moderate hyperglycemia was significantly associated with greater discontinuity on
aEEG, measured using an automated algorithm to measure average IBI from 10-minute artifact-
free epochs of EEG recordings before each blood sample (Wikstrom et al., 2011). Schumacher et
al. measured on continuous EEG recordings the total absolute band power using spectral EEG
analysis and showed increased blood glucose concentrations were associated with decreased total
absolute band power during the first 3 days of life in premature infants (Schumacher et al.,
2014). A study using CGM and simultaneous cEEG monitoring in children with type 1 diabetes
demonstrated that asymptomatic hyperglycemia was associated with changes in the power
spectra of the different frequency bands during wakefulness and sleep (Rachmiel et al., 2016).
Therefore, several quantitative EEG features have been validated in term neonates with
HIE, are predictive of neuroimaging findings and neurodevelopmental outcomes, and correlate
with visual EEG grading scales. Quantitative EEG analysis may provide a more objective
measure than visual analysis, as well as additional detail and novel measures of EEG complexity.
For example, our visual EEG classification would not capture an increase in slow frequencies or
increase in IBI duration which have been previously reported with hypo- and hyperglycemia.
Thus, the addition of quantitative EEG analysis in our future analyses will provide the
opportunity to assess acute changes in brain activity associated with glucose derangements in
neonates with HIE beyond the features captured in our visual background grading.
108
5.2.2 Application of quantitative EEG analyses in the NOGIN study
Several quantitative EEG features will be assessed in our cohort through a collaborative project
with Dr. Sampsa Vanhatalo, a pediatric neurophysiologist at the University of Helsinki. The
signal will be segmented into 5-minute non-overlapping epochs corresponding to the 5-minute
epochs of mean glucose data available from the continuous interstitial glucose monitor. The
quantitative features are calculated on each epoch for each channel and then averaged across
channels. Quantitative EEG features being assessed will include mean amplitude of the epoch
(mcV), mean frequency of the epoch (Hz) (Stevenson et al., 2013), measures of spectral power
from different frequency ranges (delta 1 [0.5-2Hz], delta 2 [2-4 Hz], theta [4-7 Hz], alpha [7-12
Hz], and beta [12-30 Hz]) (O'Toole et al., 2016), activation synchrony index (measure of
interhemispheric synchrony) (Rasanen et al., 2013), IBI (seconds), duration of bursts (sec), DFA
(Kantelhardt et al., 2001) and MF-DFA (Ihlen, 2012).
The relationship of hypo- or hyperglycemia with the qualitative and quantitative EEG
findings will be analyzed, adjusting for clinical markers of HIE severity (Apgar scores, umbilical
artery pH and base deficit) as well as gestational age and antiseizure medications. Longitudinal
modeling will be used to account for time series data and explore temporal correlations of
glucose levels with EEG changes. The visual and multiple quantitative EEG features will be
assessed to investigate which variables fit better with the changes in glucose over time. We will
incorporate cubic splines in the GEE models to characterize the possibly non-linear relationship
between cEEG features and glucose levels, in particular to investigate the possibility of threshold
effects. Phase shifting of the CGM data will be used to explore the delay that may exist between
the intermittent and continuous glucose measurements.
109
5.3 Short and long-term outcomes
Both short and long-term outcomes will be assessed in our cohort (Figure 5.1) to identify
if the neurophysiological changes associated with glucose derangements are associated with later
outcomes. Previous studies have shown a relationship between EEG background abnormalities
with brain injury on MRI in neonatal encephalopathy (Biagioni et al., 2001; Briatore et al., 2013;
Dunne et al., 2017; Shah et al., 2006), and MRI patterns of injury are predictive of long term
outcomes (Biagioni et al., 2001; Dalmazzo et al., 2011). However, EEG findings have not yet
been linked to outcome in our cohort. Short-term outcomes will include a standardized
neurological examination and MRI at 3-5 days (including 3-dimensional T1 and T2-weighted
sequences, diffusion tensor imaging and MR spectroscopy).
Figure 5.1 Timeline for continuous glucose monitoring, MRI study and follow-up
EEG background abnormalities and recovery have also been shown to be predictive of
long-term neurodevelopmental outcome in neonates with HIE (Azzopardi & Toby study group,
2014; Chandrasekaran et al., 2017; Dunne et al., 2017; Skranes et al., 2017; Spitzmiller et al.,
2007; Thoresen et al., 2010; van Laerhoven et al., 2013). Long-term outcomes are being assessed
in our cohort to identify if the acute changes seen on aEEG and cEEG associated with
hyperglycemia and glucose variability are predictive of long-term neurodevelopmental
outcomes. At 18 and 36 months, children undergo full neurological examination and
standardized tests of visual function by a pediatric neurologist, and cognitive and
neurodevelopmental outcomes will be assessed by a pediatric neuropsychologist using several
measures to assess for language, cognitive, behavioral, visual and motor function.
The optimal timing for assessing children to accurately predict development in childhood
and beyond remains debated. Environmental and educational experiences and supports may alter
developmental trajectories but deficits may also emerge that may not have been evident at
110
younger ages. The greater academic and psychosocial demands placed on children as they grow
older can reveal neurodevelopment abnormalities potentially associated with glucose
derangements that occurred in the neonatal period. The CHYLD study performed initial follow-
up at 2 years and hypoglycemia was not associated with neurodevelopmental impairments
(McKinlay et al., 2015). However, at 4.5 years old, the investigators found that neonatal
hypoglycemia was associated with an increased risk of low executive function and low visual
motor function, with the highest risk in those children that had severe, recurrent, or clinically
undetected hypoglycemia (McKinlay, Alsweiler, et al., 2017). Similarly, in children with
congenital heart disease the investigators showed that postoperative electrographic seizures were
not associated with worse outcomes on the BSID at 1 year of age, except in the subset of
children with frontal-onset seizures (Gaynor et al., 2006). Whereas at 4 years, postoperative
electrographic seizures were associated with worse executive function and impaired social
interactions or restricted behaviours (Gaynor et al., 2013). Furthermore, although the BSID is a
reliable instrument and in particular the subdomain scores can predict later performance (Soysal
et al., 2014), in both term and premature populations, measures of cognitive function during
infancy have been shown to have poor correlation with later childhood IQ and may overestimate
neurocognitive impairments (Aylward, 2004; Hack et al., 2005; Illingworth et al., 1959). Thus,
follow-up at 3 years and beyond will be important in determining whether delays persist into
early childhood.
5.4 Future directions - Summary
This study has demonstrated that acute neurophysiological changes can be detected with glucose
derangements in neonates with encephalopathy. Findings in this study support growing evidence
that it is important to consider the potential harm in allowing permissive hyperglycemia while
trying to prevent hypoglycemia. In several of the previous trials with intensive insulin therapy
for hyperglycemia management there has been an increased incidence of hypoglycemia seen
(Agus et al., 2017; Alsweiler et al., 2012; Macrae et al., 2014; Vlasselaers et al., 2009) and
inconsistant effects on glucose variability (Siegelaar et al., 2010). CGM provide an advantage by
providing continuous glucose trending information that can be acted on rapidly and may protect
111
against sudden unexpected decreases in blood glucose. However, there is currently limited
evidence for treating newborns based on this monitoring. Knowledge about the degree and
duration of hypo- and hyperglycemia that correlate with changes in global brain function will aid
in determining clinically significant target glucose ranges in neonates, leading to the
development of evidence-based algorithms for glucose management and effective
neuroprotection. How management algorithms effect the rate of glucose correction and glycemic
variability and subsequent long-term outcomes will be important to consider. The ultimate goal
is to decrease brain injury and improve long-term outcomes of neonatal encephalopathy.
112
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Appendix 1 - Appendix Table 1
Demographic data of the study cohort; for participants included in aEEG analysis and for participants excluded for incomplete data
COHORT INCLUDED WITH COMPLETE DATA
n=45
EXCLUDED FOR INCOMPLETE DATA
n=6
p value
Sex 21 ♀: 24 ♂ 3♀: 3♂ 1.000 Gestational age (wks), mean (SD)
39.54 (1.40) 39.86 (1.52) 0.645
Birth weight (grams), mean (SD)
3414.36 (548.24) 2745 (487.38) 0.018**
Birth length (cm), mean (SD)
50.14 (3.15) 47.92 (2.11) 0.052
Head circumference (cm), mean (SD)
34.22 (1.31) 33.17 (1.37) 0.123
Maternal diabetes, n (%) Type II Diabetes Mellitus Gestational Diabetes
5 (11) 1 (2) 4 (9)
1 (17) 0(0)
1 (17)
0.548
Maternal hypertension, n (%) Maternal pre-eclampsia/ eclampsia
4 (9)
2 (4.5)
0 (0)
0 (0)
1.000
Apgar score at 5 min, mean (SD)
4.18 (2.39) 4.50 (1.05) 0.572
Umbilical arterial cord pH, mean (SD)
6.99 (0.18) 6.99 (0.23) 0.987
Umbilical arterial cord BE, mean (SD)
-15.13 (6.79) -13.33 (11.37) 0.812
Route of delivery, n (%) Vaginal delivery Cesarean section
20 (44.5) 25 (55.5)
3 (50) 3 (50)
1.000
Sentinel event, n (%) 17 (38) 4 (67) 0.214 Seizures, n (%)* 28 (62) 2(33) 0.214 Clinical seizures only 16 (35.5) 0 (0) Electrographic and/or electroclinical seizures
Continuous variables were analyzed with the student’s T-test, whereas categorical variables were analyzed with Fisher’s exact test Significant p values are shown in bold *Electrographic and/or electroclinical seizures were identified by clinical team on aEEG or cEEG. Clinical seizures only had no seizures
captured on aEEG or cEEG monitoring **The birth weight was significantly lower in excluded neonates and may be related to greater difficulty with CGM insertion in smaller
neonates
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Appendix 2 - Contributions
The author was responsible for the writing and preparation of this original thesis. All of the work
presented, including the planning, data analysis, interpretation of results and writing of the
original research, was performed by the author, with the guidance and expertise of the
individuals listed below. The following contributions to the work in this thesis are formally and
inclusively acknowledged:
Dr. Emily Tam conceived and developed study design, data collection, provided guidance with
analytic approach and interpretation of results, and provided important feedback and detailed
revisions of the thesis.
Dr. Cecil Hahn provided guidance with study design, analytic approach, interpretation of
results, provided expertise for development of EEG scoring and review, supervision for clinical
review and reporting of cEEG studies, and provided important feedback and detailed revisions of
the thesis.
Dr. Rollin Brant provided guidance for statistical analyses.
Dr. Steven Miller and Dr. Aideen Moore provided guidance with study design and analytic
approach. Dr. Vann Chau provided guidance with study design, analytic approach and
contributed help with clinical review and reporting of cEEG studies.
Daphne Kamino was responsible for overall study set-up and management, including managing