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Sudden cardiac arrest: Studies on risk and outcome
Blom, M.T.
Publication date
2014
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Final published version
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Blom, M. T. (2014). Sudden cardiac arrest: Studies on risk and outcome. Boxpress.
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SUDDEN CARDIAC ARREST
Studies on risk and outcome
Sudden Cardiac Arrest: studies on risk and outcome
Academic thesis, University of Amsterdam, The Netherlands Copyright © 2014 M.T. Blom, Amsterdam, The Netherlands ISBN: 978-90-8891-960-2
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Financial support by the Dutch Heart Foundation and the Acadamic Medical Center – University of Amsterdam for the publication of this thesis is gratefully acknowledged.
SUDDEN CARDIAC ARREST
Studies on risk and outcome
ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad van doctor
aan de Universiteit van Amsterdam
ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel
op woensdag 8 oktober 2014, te 12:00 uur door
Marieke Tabo Blom
Promotores: Prof. dr. A.A.M. Wilde Prof. dr. A. de Boer Co-promotor: Dr. H.L. Tan
Overige leden: Prof. dr. A.P.M. Gorgels Prof. dr. L. de Haan Prof. dr. H.G.M. Leufkens Prof. dr. S.E.J.A. de Rooij Prof. dr. M.C.J.M. Sturkenboom Prof. dr. A.H. Zwinderman Faculteit der Geneeskunde
CONTENTS
1. Introduction and outline 7
PART I: Markers of SCA risk on the ECG from non-cardiac risk factors
2. Increased prevalence of electrocardiogram markers for sudden cardiac
arrest in epilepsy 17
3. Brugada syndrome ECG is highly prevalent in schizophrenia 33
4. Differential Changes in QTc Duration during In-hospital Haloperidol Use. 53
5. Haloperidol and peri-operative changes in the QTc-interval: a
prospective in-hospital study. 67
PART II: non-cardiac risk factors for SCA in the general community
6. Genetic, clinical and pharmacological determinants of out-of-hospital cardiac arrest: rationale and outline of the Amsterdam Resuscitation
Studies (ARREST) registry 87
7. Cardiac sodium channels and inherited electrophysiologic disorders: an
update on the pharmacotherapy. 101
8. Sudden cardiac arrest associated with use of a non-cardiac drug that
reduces cardiac excitability: evidence from bench, bedside, and community. 125
9. Patients with obstructive pulmonary disease have an increased risk of
ECG documented out-of-hospital cardiac arrest. 149
PART III: survival after out-of-hospital cardiac arrest
10. Reduced in-hospital survival rates of out-of-hospital cardiac arrest
11. Women are less likely to be resuscitated during out-of-hospital cardiac
arrest than men 187
12. Improved survival after out-of-hospital cardiac arrest and use of
205
13. General summary and discussion 225
Nederlandse samenvatting Dankwoord
List of Publications PhD Portfolio CV
Chapter 1
9
Sudden cardiac arrest (SCA) is a leading cause of death in industrialized countries, affecting 180,000-250,000 individuals per year in the United States.1,2 In Europe,
275,000 out-of-hospital SCAs are treated by emergency medical services annually.3 The
largest absolute number of SCA events occurs out-of-hospital in the general population in persons who are not considered to be at high risk for SCA. Although the majority of out-of-hospital SCAs (
sign of a cardiovascular disease.4-6 Out-of-hospital SCAs are often lethal, with survival
rates ranging from 3% to 19%.7-9
outcome of a culmination of multiple factors. A combination of inherited and/or acquired disorders, stressors and circumstantial triggers eventually initiate an SCA. SCA victims in the general population are hence a very heterogeneous group to study.
In order to reduce the number of deaths due to SCA, two main approaches can be distinguished: 1) identify those at risk in order to prevent SCA, and 2) optimize out-of-hospital resuscitation care and in-hospital post-resuscitation care to improve survival after SCA.
There are many ways to investigate the complex interplay of risk factors of SCA. These risk factors can be studied in the general population (applying to all) or in certain risk groups (persons with a higher a-priori risk). Risk factors may be sought at all levels: inherited and/or acquired factors, stressors and circumstantial triggers can play a role at the health care system level, in the direct social and physical environment of the person, and in the body itself, at system, organ or (sub)cellular level. Mechanisms playing at the (sub)cellular level may result in increased risk of a particular drug at the population level. Other mechanism may only play a role in certain patient groups and/or in certain situations. In many cases, SCA risk can be appreciated through a ‘multiple hit model’. need to coincide in order to initiate the cascade of events eventually leading to SCA. Knowledge about these interacting mechanisms may facilitate preventive measures to avoid lethal combinations of inherited and/or aquired risk factors. For example, a non-risk factors as medication use, stressors or disease. Informed advice to patients will enable them to avoid these combinations.
In order to explore proposed mechanisms involved in SCA risk, the risk of the occurrence of SCA itself can be studied, but also of markers of SCA risk, such as certain abnormalities at the electrocardiogram (ECG). For instance, can we see more ECG-abnormalities in a patient group that is known to have a higher risk of SCA when
compared to a control group? Or can the established effects on the ECG of a certain drug be recognized in a patient group that has multiple co-morbidities? Indeed, several include QTc-prolongation,10,11 a Brugada ECG-pattern12,13 and an early repolarization
pattern (ERP).14
into the mechanism underlying increased SCA risk. Also, it may provide insight into the importance of an established mechanism (i.e., QT-prolongation caused by potassium channel blocking drugs) in a certain population.
The second approach to prevent deaths due to SCA is to improve survival chances after an SCA has occurred. This applies in particular to out-of-hospital cardiac arrest (OHCA), because the majority of lethal SCAs occur out-of-hospital, and survival gains of improved treatments are highest here. Determinants of survival after OHCA can be patient characteristics (including medical history), the setting and adequate recognition of the OHCA, and elements of the chain-of-care involved when resuscitation is initiated. Chances on survival are highest when an OHCA victim has a so-called shockable
Presence of a shockable initial rhythm depends on the direct cause of the OHCA, but also on timely recognition: while OHCA is usually caused by VF, VF will deteriorate into, mostly untreatable, asystole within minutes.
recent initiatives to improve out-of-hospital care were primarily aimed at decreasing
15 Evaluation
of these initiatives and other determinants of survival after OHCA are of vital importance in order to maximize outcome of resuscitation attempts.
It is not possible to address all issues at all levels concerning SCA at once. Nonetheless, the AmsteRdam REsuscitation STudies (ARREST), of which several projects are presented in this thesis, seeks to facilitate the study of SCA at all the levels mentioned above, if possible in interaction with each other. ARREST was set up to establish the determinants of outcome of OHCA and gain insight into the genetic, clinical and pharmacological determinants of OHCA in the general population. Its aims are deliberately set this wide since it recognizes that OHCA, both the risk of its occurrence and the odds of surviving from it, is a particularly multifactorial phenomenon.
Chapter 1
11
outcome. While cardiovascular risk factors such as heart failure and cardiac ischemia are well-known to contribute to SCA risk from several large population-based studies16,
studies, to which ARREST aims to contribute. Apart from examples from ARREST, this thesis presents several studies that examine SCA risk markers on the ECG. Part I of this thesis presents four studies based on ECG-data, whereas Part II and Part III provide an anthology of ARREST studies.
Chapter 2 presents a cross-sectional, retrospective study comparing ECGs in patients
with epilepsy with community-dwelling controls. In a recent community-based study, we found that people with epilepsy had a 2-3 fold increased risk of SCA.17 In the study
presented here, we aimed to determine whether ECG-risk markers of SCA (severe QTc-prolongation, Brugada ECG-pattern, ERP) are more prevalent in people with epilepsy than in community-dwelling controls. Chapter 3 has a similar approach in a different patient group, i.e., patients with schizophrenia. While SCA risk is increased in schizophrenia, the causes of this association are not fully resolved.18 In this study, we
compared ECG markers of SCA-risk, in particular, Brugada-ECG pattern and QTc-prolongation, between patients with schizophrenia and community-dwelling controls.
Chapter 4 and 5 both investigate the effects of the
antipsychotic drug haloperidol on the QT interval in two in-hospital populations. Haloperidol has established QT-prolonging effects on the ECG by blocking cardiac potassium channels,19
comorbidities is less well-studied. In chapter 4 the QT-interval before, during and after haloperidol use was measured, along with medication use and clinical variables in order to identify risk factors for potentially dangerous QT prolongation. In chapter 5, and investigate whether effects on the ECG of haloperidol can be distinguished in a perioperative setting.
The second part of this thesis presents studies performed with data from the ARREST registry, and focuses on non-cardiac risk factors for SCA in the general community.
Chapter 6 describes the rationale and outline of the part of ARREST that was
set up to study genetic, clinical and pharmacological determinants of OHCA, providing examples of possible study designs. Chapter 7 presents a review of the cardiac sodium channel and inherited electrophysiological disorders, and provides an overview on pharmacotherapy. The cardiac sodium channel plays a pivotal role in the propagation of
electrical activity through the heart. Studying this ion channel provides valuable insights into the electrophysiological mechanisms that are at play at the level of the cardiac e.g., the use of drugs prescribed for the treatment of non-cardiac disease, such as antidepressants and anti-epileptics. Chapter 8 is an example of how the combination of molecular-genetic work and patient data with a population study establishes increased SCA risk of nortriptyline, an antidepressant drug, while providing insight into the mechanism behind this increase. Chapter 9 presents a community based case-control study that investigates whether patients with obstructive pulmonary disease have an and/or respiratory drug use.
outcome of resuscitation attempts after OHCA, and determinants thereof. Chapter
10 presents a study that analyzes whether patients with obstructive pulmonary disease
have a lower survival rate after OHCA than patients without obstructive pulmonary disease. Patient’s co-morbidities may determine survival after OHCA, and guide post-resuscitation care. Chapter 11 examines gender differences and aims to study whether access to and outcome of resuscitation attempts after OHCA differs between men and women. Finally, Chapter 12 examines whether temporal trends can be distinguished in neurologically intact survival after OHCA, and, if so, if a change in survival is
Chapter 1
13 1. Chugh SS, Reinier K, Teodorescu C, et al. Epidemiology of sudden cardiac death: clinical and research
implications. Prog Cardiovasc Dis
2.
Stroke Statistics Subcommittee. Heart disease and stroke statistics--2011 update: a report from the American Heart Association. Circulation
3. Atwood C, Eisenberg MS, Herlitz J, Rea TD. Incidence of EMS-treated out-of-hospital cardiac arrest in Europe. Resuscitation.
4. Myerburg RJ. Sudden cardiac death: exploring the limits of our knowledge. J Cardiovasc Electrophysiol. 5. Stecker EC, Vickers C, Waltz J, et al. Population-based analysis of sudden cardiac death with and
Death Study. J Am Coll Cardiol.
6.
status and challenges for the future. Eur Heart J.
7. Aufderheide TP, Yannopoulos D, Lick CJ, et al. Implementing the 2005 American Heart Association Guidelines improves outcomes after out-of-hospital cardiac arrest. Heart Rhythm
8. Berdowski J, Berg RA, Tijssen JG, Koster RW. Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies. Resuscitation
9. Blom MT, Beesems SG, Homma PC, et al. Improved survival after out-of-hospital cardiac arrest and
This thesis, 2014.
10. Algra A, Tijssen JG, Roelandt JR, Pool J, Lubsen J. QTc prolongation measured by standard 12-lead electrocardiography is an independent risk factor for sudden death due to cardiac arrest. Circulation.
11. Straus SM, Kors JA, de Bruin ML, et al. Prolonged QTc interval and risk of sudden cardiac death in a population of older adults. J Am Coll Cardiol
12. Matsuo K, Akahoshi M, Nakashima E, et al. The prevalence, incidence and prognostic value of the Brugada-type electrocardiogram: a population-based study of four decades. J Am Coll Cardiol.
13. Postema PG, van Dessel PF, Kors JA, Linnenbank AC, van Herpen G, Ritsema van Eck HJ, van Geloven N, de Bakker JM, Wilde AA, Tan HL. Local depolarization abnormalities are the dominant pathophysiologic mechanism for type 1 electrocardiogram in brugada syndrome a study of electrocardiograms, vectorcardiograms, and body surface potential maps during ajmaline provocation.
J Am Coll Cardiol.
14. Haïssaguerre M, Derval N, Sacher F, et al. Sudden Cardiac Arrest Associated with Early Repolarization. 15. Berdowski J, Blom MT, Bardai A, Tan HL, Tijssen JG, Koster RW. Impact of onsite or dispatched
Circulation.
16. Zipes DP, Wellens HJ. Sudden cardiac death. Circulation.
17. Bardai A, Lamberts RJ, Blom MT, et al. Epilepsy is a risk factor for sudden cardiac arrest in the general population. PLoS One.
18. Koponen H, Alaräisänen A, Saari K, et al. Schizophrenia and sudden cardiac death: a review. Nord J
19. Mörtl D, Agneter E, Krivanek P, Koppatz K, Todt H. Dual rate-dependent cardiac electrophysiologic effects of haloperidol: slowing of intraventricular conduction and lengthening of repolarization. J Cardiovasc Pharmacol
CHAPTER 2
R.J. Lamberts, M.T. Blom, J. Novy, M. Belluzzo, A. Seldenrijk, B.W. Penninx, J.W. Sander, H.L. Tan, R.D. Thijs
People with epilepsy are at increased risk of sudden cardiac arrest (SCA) due to based study. We aimed to determine whether ECG-risk markers of SCA are more prevalent in people with epilepsy.
In a cross-sectional, retrospective study, we analyzed the ECG-recordings of 185 people with refractory epilepsy and 178 controls without epilepsy. Data on epilepsy characteristics, cardiac comorbidity, and drug use were collected and general ECG variables (heart rate [HR], PQ- and QRS-intervals) assessed. We analyzed ECGs for three markers of SCA risk: severe QTc-prolongation (male >450 msec, female >470 msec), Brugada ECG-pattern, and early repolarization pattern (ERP). Multivariate regression models were used to analyze differences between groups and to identify associated clinical and epilepsy-related characteristics.
People with epilepsy had higher HR (71 vs. 62 bpm, p<0.001) and a longer PQ-interval (162.8 vs. 152.6 msec, p=0.001). Severe QTc-prolongation and ERP were ERP: 34 vs. 13%, p<0.001), while the Brugada ECG-pattern was equally frequent in both groups (2% vs. 1%, p>0.999). After adjustment for covariates epilepsy remained associated with ERP (ORadj 2.4, 95% CI 1.1-5.5) and severe QTc-prolongation (ORadj 9.9, 95% CI 1.1-1317.7).
ERP and severe QTc-prolongation appear to be more prevalent in people with refractory epilepsy. Future studies must determine whether this contributes to increased SCA risk in people with epilepsy.
Chapter 2
19
A recent community-based study found that people with epilepsy had a 2-3 times
SCA.1 The 12-lead standard ECG is a potential low-cost screening test for SCA risk.
QTc-prolongation,2-4 Brugada ECG-pattern (Brugada-ECG), 5 and early repolarization
pattern (ERP).6-9
comparing people with epilepsy and without epilepsy, mild QTc-prolongation was reported in those with epilepsy,10-12 while others reported similar QTc-durations in both
groups,13-14 or QTc-shortening.15-16 The number of people with severe QTc-prolongation
was not reported in these studies.
Brugada-ECG is characteristic of Brugada syndrome, an inherited disease associated with disrupted cardiac depolarization.17 Sudden death in young people with
structurally normal hearts in epilepsy and Brugada syndrome occurs mainly during rest or sleep.17-19 ERP, long considered a benign and more common variant of the
Brugada-than in healthy controls.20-21
predictor of SCA in several population-based studies.6-9
We hypothesize that the prevalence of severe QTc-prolongation, Brugada-ECG, and ERP is increased in people with epilepsy, which may (partly) explain the higher SCA risk in epilepsy.
22
who were assessed at one epilepsy tertiary referral centerbetween September 2009 and April 2011. In all, a resting 12-lead ECG was recorded as part of the routine assessment on initial evaluation.23 The anonymized data were obtained as part of an audit into
epilepsy-associated comorbidities, which was approved as such by the local ethics committee. As all data was acquired during routine clinical care no informed consent was required.
Controls were drawn from a sub-study of the Netherlands Study of Depression and Anxiety.24 They were 18-65 years old, randomly selected from a general practitioners’
A resting 12-lead ECG was recorded in 179 subjects. We excluded all those with a diagnosis of active epilepsy or current use of AEDs (n=1), leaving 178 controls. The study was approved by the local ethics committee. Informed consent was obtained from all participants.
In all participants, conventional characteristics of the 12-lead ECG (heart rate [HR], as type-1 (coved ST-segment elevation in right precordial ECG leads 0.2 mV followed by a negative T-wave with little or no isoelectric separation), type 2 (coved ST-segment elevation in V1-V3 followed by a gradually descending ST-segment elevation remaining 0.1 mV above the baseline and a positive or biphasic T-wave that results in a saddle back 0.1 mV of saddle back type, coved type, or both), according to Brugada syndrome consensus criteria.25
QTc-duration was calculated using Bazett’s formula to correct for HR: QT/ RR.26
Guidelines: >450 msec in men, >470 msec in women.27
elevation 0.1 mV in 2 adjacent leads with either slurring or notching morphology.7,20
Leads V1-V3 were not assessed to avoid confusion with ECG patterns typical of Brugada syndrome. ECGs with intraventricular conduction delay (QRS duration of 0.12s), which precluded reliable assessment of QTc-duration, Brugada-ECG or ERP (n=3, all cases), were excluded from analysis.7,20
An experienced cardiologist (HLT) reviewed all ECGs for Brugada-ECG absence of severe QTc-prolongation and ERP were assessed by two blinded researchers verdict. There was no systematic difference between the reviewers in their analysis of the QTc-interval (paired t-test: 0.59) or ERP (kappa score 0.75).
Variables were collected from medical records (in cases) and on self-reported/assessed information during a face-to-face interview (in controls). These variables were: gender, age, presence of 2 cardiac risk factors (hypertension, hypercholesterolemia, diabetes 1) QT-prolonging medication (www.azcert.org), 2) depolarization-blocking drugs (www.brugadadrugs.org), 3) cardiovascular drugs ( -adrenoreceptor blockers, calcium channel antagonists, converting enzyme inhibitors, diuretics, angiotensin-II receptor blockers, nitrates, platelet aggregation inhibitors and/or statins) and 4)
lipid-Chapter 2
21
Among AEDs, QT-prolonging drugs were phenytoin and felbamate, while depolarization-blocking drugs included carbamazepine, oxcarbazepine, phenytoin, and lamotrigine.
In people with epilepsy, additional data were recorded including epilepsy aetiology (symptomatic/non-symptomatic), history of epilepsy surgery (yes/no), age of onset and duration of epilepsy, seizure frequency ( 1 vs. <1/month), polytherapy ( 2 AEDs), presence of a learning disability, and family history of epilepsy ( 2 family members with epilepsy).
Differences between cohorts in baseline characteristics and ECG-parameters were analyzed using 2-statistics for categorical variables (Pearson/Fisher’s Exact test where appropriate) and the Student’s t-test/Mann-Whitney U test for continuous variables. We performed multivariate logistic regression models to determine whether epilepsy was independently associated with Brugada-ECG, severe QTc-prolongation, or ERP. associated (p<0.1) with outcome, whereas the second model included only those determinants that also changed the point estimate by 5%. As severe QTc-prolongation was not seen in controls, we used penalized logistic regression analysis to perform multivariate analysis applying the same strategy as above. Among people with epilepsy the same approach was used to determine which clinical (comorbidities and medication use) and epilepsy characteristics were associated with these SCA-predictors. Statistics
Windows, Chicago IL, USA).
ECGs of 185 people with epilepsy and 178 controls were analyzed (Table 1). People more frequently used drugs with prolonging or depolarization-blocking effects. QT-prolonging drugs used were AEDs (46%), antidepressants (30%), antipsychotics (20%), or antiemetics (5%), whereas depolarization-blocking drugs were almost exclusively AEDs (99%). The prevalence of 2 cardiac risk factors, heart disease, cardiovascular medication, and lipid lowering drugs did not differ between groups.
People with epilepsy had a higher HR (71 vs. 62 bpm, p<0.001), a longer PQ-interval, interval (89 vs. 91 msec, p=0.07). Mean QTc-duration was also longer: 405 vs. 394 msec, p<0.001. Brugada-ECG was equally prevalent in both groups (2% vs. 1%, p>0.999). The prevalence of both severe QTc-prolongation (5% vs. 0%, p= 0.002) and
Figure 1) was higher in cases than in controls: Table 1.
(n=185) (n=178) P-value Male gender 85 (46%) 65 (37%) 0.068 Mean age, years 38 (13.3) 48 (12.5) <0.001
7 (4%) 11 (6%) 0.293 2 (1%) 2 (1%) 0.969 3 (2%) 3 (2%) 0.962 QT-prolonging drugs 39 (21%) 1 (1%) <0.001 145 (78%) 1 (1%) <0.001 Cardiovascular drugs 32 (17%) 26 (15%) 0.484 Lipid lowering drugs 9 (5%) 6 (3%) 0.475 Heart rate, beats per min 70.7 (11.4) 61.8 (9.8) <0.001 PQ, msec 162.8 (26.0) 152.6 (32.6) 0.001 QRS, msec 88.7 (13.8) 91.0 (10.3) 0.066 QTc, msec 404.8 (33.0) 393.5 (24.8) <0.001 3 (2%) 2 (1%) >0.999 10 (5%) 0 (0%) 0.002 62 (34%) 23 (13%) <0.001 50 (27%) 20 (11%) <0.001
Chapter 2
23
Apart from epilepsy, severe QTc-prolongation was univariately associated with (female) gender, (lower) age, (higher) HR, and use of depolarization-blocking drugs (Supplemental Table s1). QT-prolonging drugs were not used by those with severe QTc-prolongation. Due to the absence of severe QTc-prolongation in the control cohort, it was not possible to separate the effects of epilepsy and use of depolarization-blocking drugs (99% of which were AEDs) in multivariate analysis. Therefore, only epilepsy, gender, age, and HR were entered in the model (penalized logistic regression, Table 2). After correction for these variables epilepsy remained associated with severe QTc-prolongation (Table 2, Model A: ORadj 9.9 (1.1-1317.7).
in cases (n=185) and controls (n=178)
(n=185) (n=178) Brugada-ECG 3 (2%) 2 (1%) 1.5 (0.2-8.8) NA NA Severe QTc- 10 (5%) 0 (0%) 21.0 (2.7-2708.2) 9.9 (1.1-1317.7) 9.9 (1.1-1317.7) ERP 62 (34%) 23 (13%) 3.4 (2.0-5.8) 2.3 (1.0-5.5) 2.4 (1.1-5.5)
e-1) or ERP (Table e-2). entered.
ERP was univariately associated with epilepsy (male) gender, heart disease (higher) HR and the use of QT-prolonging, depolarization-blocking, and cardiovascular drugs (Supplemental Table s2). Multivariate analysis showed epilepsy to be independently associated with ERP: Table 2, Model B: ORadj 2.4 (95% CI 1.1-5.5).
In those with epilepsy (n=185) none of the epilepsy characteristics were associated with either severe QTc-prolongation or ERP (Supplemental Tables s3 and s4).
We systematically analyzed the prevalence of three ECG-risk markers of SCA and found that severe QTc-prolongation and ERP were more frequent in people with refractory epilepsy.
Our study had some limitations. There were several differences between cases and controls: people with epilepsy were younger and more likely to be male. Younger age may result in a lower QTc-interval and a higher ERP prevalence.28,29 In
view of the relatively small age differences in our study, however, only minor effects on QTc prolongation and ERP should be expected. Accordingly, epilepsy status and QTc-prolongation after correction for age. ERP is more frequently found in males, but epilepsy status remained an independent determinant after accounting for gender differences.29 As for severe QTc-prolongation, the association with epilepsy status and
with previous studies, HR was higher in cases than in controls: this may be due to epilepsy-related abnormalities of cardiac autonomic balance.30 HR is incorporated in
an overestimation of QTc-duration in people with higher HR: particularly those with epilepsy.26 We, therefore, included HR in the multivariate analysis of both severe
QTc-using Bazett’s formula and for study comparability, we did not use alternative QT correction formulae.
We found that QTc-duration was increased in people with epilepsy when compared with controls. This is concordant with some,10-12 but not all previous
studies.13-16
or medication use between study populations. We analyzed people with refractory, more severe epilepsy than in previous studies. QTc-duration was dichotomized in one study (>440 msec, yes vs. no), allowing comparison between ours and their results. In in cases and controls.11
Chapter 2
25
prolongation (>450msec in men, >470msec in women), is recommended, however, by the current guidelines of the European Society of Cardiology and has been used in the more recent large-scale prospective, population-based studies of SCA-risk.3,27 We
therefore believe our criteria to be more clinically relevant.
In our analysis of severe QTc-prolongation, we could not separate the effects of epilepsy and use of depolarization-blocking drugs. Severe QTc-prolongation was only present in people with epilepsy and depolarization-blocking drugs (predominantly AEDs) were used almost exclusively by this group. We believe the use of depolarization-blocking drugs is more likely a proxy for epilepsy severity than directly affecting cardiac repolarization. In this study, use of depolarization-blocking drugs was not related with severe QTc-prolongation when analyzing only the cohort with epilepsy. Use of depolarization-blocking AEDs was not associated with QTc-prolongation in cross-sectional studies,11-14 nor in a prospective drug trial.31 QT-prolonging drugs are more
likely to contribute to severe QTc-prolongation, but none of the individuals with this ECG risk marker used these drugs.
In the multivariate analysis of ERP, we could separate the effects of epilepsy and depolarization-blocking drugs and found that the latter variable was not an independent determinant. Due to the higher prevalence of this SCA risk marker in our population, the evidence for the association of epilepsy with ERP is stronger than with severe QTc-prolongation.
of SCA.1 ERP was associated with a 1.7-fold increased risk of SCA in a recent
meta-analysis,32 and seizures may facilitate the transition from ERP into Brugada-ECG.33
Severe QTc-prolongation is associated with a three-fold increased risk of SCA, which may be aggravated by additional peri-ictal QTc-prolongation.34,35
Severe QTc-prolongation, ERP, and certain epilepsy syndromes are associated with sodium and potassium channel mutations.36,37 Conceivably, a single mutation
expressed in heart and brain might confer both a propensity for epilepsy and an innate vulnerability to cardiac arrhythmias, thereby linking epilepsy with these ECG-markers and SCA.
Routine performance of a 12-lead ECG in all adults with suspected epilepsy is recommended by the NICE guidelines but not listed in the AES/AAN guidelines.24,38
The diagnostic yield of this practice has not yet been determined. Our study suggests that an increased prevalence of severe QTc-prolongation and ERP occurs in people with epilepsy. Routine ECG-evaluation in people with epilepsy may be of importance in guiding clinicians in their choice of AED therapy, e.g. avoidance of QT-prolonging or depolarization-blocking drugs in people with ECG-markers of increased SCA risk.
Study designed by RJL, JWS, HLT and RDT. Data was collected by RJL, MTB, JN, MB, AS and BP. Data analyzed by RJL, MTB, JWS, HLT and RDT. Manuscript drafted by RJL and MTB and critically reviewed
The authors are grateful to S. Balestrini for her assistance with data collection, to Dr. M. Tanck for his statistical assistance, and to Dr. GS Bell for reviewing the manuscript.
JWS receives research support from Epilepsy Society, the Dr. Marvin Weil Epilepsy Research Fund, Eisai, GSK, UCB Pharma, the World Health Organization, and the National Institutes of Health (NIH), and has been consulted by and received fees for lectures from GSK, Viropharma, Eisai and UCB Pharma. RDT receives research support from NUTS Ohra Fund, Medtronic, and AC Thomson Foundation, and has received fees for lectures from Medtronic, UCB Pharma and GSK. JWS and RDT are members of
NIH (NBIH/NINDS –1P20NS076965-01). JN was supported by the Swiss National Science Foundation-Fellowships for prospective researchers and the SICPA Foundation, Prilly, Switzerland
This study was supported by the Dutch Epilepsy Foundation (project number 10-07), Christelijke Vereniging voor de Verpleging van Lijders aan Epilepsie (Nederland), and Netherlands Organization for
Chapter 2
27 1. Bardai A, Lamberts RJ, Blom MT, et al. Epilepsy is a risk factor for sudden cardiac arrest in the general
population. PLoS One
2. Algra A, Tijssen JG, Roelandt JR, et al. QTc prolongation measured by standard 12-lead electrocardiography is an independent risk factor for sudden death due to cardiac arrest. Circulation
3. Straus SM, Kors JA, de Bruin ML, et al. Prolonged QTc interval and risk of sudden cardiac death in a population of older adults. J Am Coll Cardiol
4. Soliman EZ, Prineas RJ, Case LD, et al. Electrocardiographic and clinical predictors separating atherosclerotic sudden cardiac death from incident coronary heart disease. Heart
5. Matsuo K, Akahoshi M, Nakashima E, et al. The prevalence, incidence and prognostic value of the Brugada-type electrocardiogram: a population-based study of four decades. J Am Coll Cardiol
6. Tikkanen JT, Anttonen O, Junttila MJ, et al. Long-term outcome associated with early repolarization on electrocardiography. N Engl J Med
7. Sinner MF, Reinhard W, Mueller M, et al. Association of Early Repolarization Pattern on ECG with Risk of Cardiac and All-Cause Mortality: A Population-Based Prospective Cohort Study (MONICA/ KORA). PLoS Medicine
8. Haruta D, Matsuo K, Tsuneto A, et al. Incidence and prognostic value of early repolarization pattern in the 12-lead electrocardiogram. Circulation
early repolarization pattern in a population-based study. Am J Cardiol
10. Drake ME, Reider CR, Kay A. Electrocardiography in epilepsy patients without cardiac symptoms.
Seizure
11. Neufeld G, Lazar JM, Chari G, et al. Cardiac Repolarization Indices in Epilepsy Patients. Cardiology
Epilepsy Res
13. Akalin F, Tirtir A, Yilmaz Y. Increased QT dispersion in epileptic children. Acta Paediatr
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14. Krishnan V, Krishnamurthy KB. Interictal 12-lead electrocardiography in patients with epilepsy.
Epilepsy Behav
15. Teh HS, Tan HJ, Loo CY, et al. Short QTc in epilepsy patients without cardiac symptoms. Med J Malaysia
16. Ramadan M, El-Shahat N, A Omar A, et al. Interictal electrocardiographic and echocardiographic changes in patients with generalized tonic-clonic seizures. Int Heart J
17. Postema PG, van Dessel PF, Kors JA, et al. Local depolarization abnormalities are the dominant pathophysiologic mechanism for type 1 electrocardiogram in brugada syndrome: a study of electrocardiograms, vectorcardiograms, and body surface potential maps during ajmaline provocation.
J Am Coll Cardiol
18. Surges R, Thijs RD, Tan HL, et al. Sudden unexpected death in epilepsy: risk factors and potential pathomechanisms. Nat Rev Neurol
19. Lamberts RJ, Thijs RD, Laffan A, et al. Sudden unexpected death in epilepsy: People with nocturnal seizures may be at highest risk. Epilepsia
20. Haïssaguerre M, Derval N, Sacher F, et al. Sudden Cardiac Arrest Associated with Early Repolarization.
New Engl J Med
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23. NICE clinical guideline 137. The epilepsies: the diagnosis and management of the epilepsies in adults and children in primary and secondary care. http://publications.nice.org.uk (Accessed 17 Jan 2014) 24. Penninx BW, Beekman AT, Smit JH, et al. The Netherlands Study of Depression and Anxiety
(NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res
25. Wilde AA, Antzelevitch C, Borggrefe M, et al. Proposed diagnostic criteria for the Brugada syndrome: consensus report. Circulation
26. Luo S, Michler K, Johnston P, et al. A comparison of commonly used QT correction formulae: the effect of heart rate on the QTc of normal ECGs. J Electrocardiol
27. Committee for Proprietary Medicinal Products. Points to consider: the Assessment of the Potential for QT Interval Prolongation by Non-Cardiovascular Medicinal Products. London, 1997.
28. Mangoni AA, Kinirons MT, Swift CG, et al. Impact of age on QT interval and QT dispersion in healthy subjects: a regression analysis. Age Ageing
29. Walsh JA, Ilkhanoff L, Soliman EZ, et al. Natural history of the early repolarization pattern in a biracial cohort: CARDIA (Coronary Artery Risk Development in Young Adults) Study. J Am Coll Cardiol
30. Sevcencu C, Struijk JJ. Autonomic alterations and cardiac changes in epilepsy. Epilepsia
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31. Saetre E, Abdelnoor M, Amlie JP, et al. Cardiac function and antiepileptic drug treatment in the elderly: a comparison between lamotrigine and sustained-release carbamazepine. Epilepsia
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32. Wu SH, Lin XX, Cheng YJ, et al. Early repolarization pattern and risk for arrhythmia death: a meta-analysis. J Am Coll Cardiol
33. Gussak I, Antzelevitch C. Early repolarization syndrome: clinical characteristics and possible cellular and ionic mechanisms. J Electrocardiol
34. Surges R, Scott CA, Walker MC. Enhanced QT shortening and persistent tachycardia after generalized seizures. Neurology
35. Seyal M, Pascual F, Lee CY, et al. Seizure-related cardiac repolarization abnormalities are associated with ictal hypoxemia. Epilepsia
congenital long QT syndrome and epilepsy. Neurology
37. Watanabe H, Nogami A, Ohkubo K, et al. Electrocardiographic characteristics and SCN5A mutations
Circ Arrhythm Electrophysiol
seizure in adults (an evidence-based review): report of the Quality Standards Subcommittee of the American Academy of Neurology and the American Epilepsy Society. Neurology
Chapter 2 29 (n=353) P-value 10 (100%) 175 (50%) 0.002 Male gender 1 (10%) 149 (42%) 0.051 Age, years 35.6 (12.5) 43.0 (13.8) 0.095 0 (0%) 18 (5%) >0.999 0 (0%) 4 (1%) >0.999 0 (0%) 6 (2%) >0.999 Hypertension 0 (0%) 39 (11%) 0.609 0 (0%) 34 (10%) 0.608 Diabetes Mellitus 0 (0%) 12 (3%) >0.999 QT-prolonging drugs 0 (0%) 40 (11%) 0.610 9 (90%) 137 (39%) 0.002 Cardiovascular drugs 0 (0%) 58 (16%) 0.375 Lipid lowering drugs 0 (0%) 15 (4%) >0.999 Heart rate, beats per min 83.6 (5.8) 65.8 (11.2) <0.001
PQ, msec 162.8 (25.8) 159.5 (24.8) 0.676 QRS, msec 85.2 (7.9) 89.9 (12.3) 0.228 QTc, msec 481.0 (10.8) 396.9 (26.7) <0.001 0 (0%) 5 (1%) >0.999 3 (30%) 82 (23%) 0.705 3 (30%) 67 (19%) 0.413
(n=363)
ERP (n=85) No ERP (n=278) P-value 62 (73%) 123 (44%) <0.001 Male gender 43 (51%) 107 (38%) 0.047 Age, years 41.9 (14.2) 43.1 (13.7) 0.473 3 (4%) 15 (6%) 0.775 3 (4%) 1 (1%) 0.041 4 (5%) 2 (1%) 0.029 QT-prolonging drugs 14 (16%) 26 (9%) 0.067 51 (60%) 95 (34%) <0.001 Cardiovascular drugs 19 (22%) 39 (14%) 0.067
Lipid lowering drugs 4 (5%) 11 (4%) 0.758 Heart rate, beats per min 69.2 (12.7) 65.4 (11.0) 0.015
PQ, msec 160.6 (27.2) 159.2 (24.1) 0.666 QRS, msec 89.5 (14.3) 89.9 (11.6) 0.817 QTc, msec 400.2 (33.3) 399.0 (28.7) 0.743 0 (0%) 5 (2%) 0.595 3 (4%) 7 (3%) 0.705
Chapter 2 31 (n=175) P-value 3 (30%) 77 (44%) 0.518 0 (0%) 14 (8%) >0.999 14.8 (8.4) 14.2 (10.9) 0.874 20.8 (11.8) 23.9 (14.2) 0.499 9 (100%) 155 (96%) >0.999 8 (80%) 149 (85%) 0.650 2 (1-4) 2 (1-6) 0.168 QT-prolonging drugs 0 (0%) 39 (5%) 0.124 9 (90%) 136 (78%) 0.693 Learning disability 2 (20%) 28 (16%) 0.666 1 (10%) 21 (12%) >0.999
ERP (n=62) No ERP (n=123) P-value 28 (45%) 52 (42%) 0.709 5 (8%) 9 (7%) >0.999 15.4 (11.8) 13.7 (10.3) 0.327 23.0 (14.4) 24.1 (13.9) 0.594 55 (98%) 109 (95%) 0.429 53 (85%) 104 (85%) 0.868 2 (1-6) 2 (1-5) 0.894 QT-prolonging drugs 13 (21%) 26 (21%) 0.979 51 (82%) 94 (76%) 0.363 Learning disability 8 (13%) 22 (18%) 0.385 5 (8%) 17 (14%) 0.254
CHAPTER 3
M.T. Blom, D. Cohen, A. Seldenrijk, B.W. Penninx, G. Nijpels, C.D. Stehouwer, J.M. Dekker, H.L. Tan
The causes of the increased risk of sudden cardiac death (SCD) in schizophrenia are not resolved. We aimed to establish whether 1) ECG markers of SCD-risk, in particular, Brugada-ECG pattern, are more prevalent among schizophrenia patients, 2) increased prevalence of these ECG markers in schizophrenia is explained by confounding factors, notably sodium-channel blocking medication.
In a cross-sectional study, we analyzed ECGs of a cohort of 275 schizophrenia patients, along with medication use. We determined whether Brugada-ECG was present, and assessed standard ECG measures (heart rate, PQ-, QRS- and comparable age (NESDA cohort, N=179), and, to account for assumed increased ageing-rate in schizophrenia, with 20-year older individuals (Hoorn cohort, n=1168), using multivariate regression models.
cohort (11.6%) compared with NESDA controls (1.1%) or Hoorn controls (2.4%). Moreover, schizophrenia patients had longer QT-intervals (410.9 vs. 393.1 and 401.9 msec, both p<0.05), an increased proportion of mild or severe QTc-prolongation (13.1% and 5.8% vs. 3.4% and 0.0% [NESDA], vs. 5.1 and 2.8%, [Hoorn]), and higher heart rates (80.8 vs. 61.7 and 68.0 beats per minute, both p<0.05). Prevalence of Brugada-ECG was still increased (9.6%) when schizophrenia patients without sodium-channel blocking medication were compared to either control cohort.
Brugada-ECG has increased prevalence among schizophrenia patients. This association is not explained by use of sodium-channel blocking medication.
Chapter 3
35
Patients with severe mental illness have a 14-32 years reduced life expectancy1.
Schizophrenia is associated with increased standardized mortality ratios for all-cause death2, cardiovascular death3, and sudden cardiac death (SCD)4. The causes for
SCD-risk in schizophrenia are unresolved5. SCD is mostly caused by lethal cardiac
arrhythmias resulting from disrupted cardiac electrophysiology (depolarization and/or repolarization)6. Many researchers ascribe SCD in schizophrenia to antipsychotics, as
antipsychotics may cause such disruptions7. For instance, increased SCD-risk during
(SGA) is commonly ascribed to their repolarization-blocking effects, signaled by QTc-prolongation8,9. However, individual susceptibility is crucial. Co-morbidities that
more prevalent among schizophrenic individuals3. The possibility that inherited factors
are also relevant has so far received less recognition.
These considerations prompted us to conduct the present study. We systematically compared ECGs of a cohort of schizophrenia patients to ECGs of two cohorts of non-schizophrenia control subjects, and took co-variates for ECG abnormalities into account. Our primary aim was to establish whether ECG markers of SCD-risk are more prevalent in schizophrenia patients than in non-schizophrenic controls. This included the Brugada ECG pattern (Brugada-ECG) and QTc duration. Our secondary aim was to study whether differences in prevalence of these ECG markers may be explained by use of sodium-channel blockers or QTc-prolonging drugs and/or presence of cardiovascular risk factors.
In a cross-sectional study, all outpatients at the Department of Severe Mental Illness, Mental Health Care Center-North Holland North (n=603), typically in psychiatric care for >10 years, were asked to participate in yearly metabolic screening in February 2008 - January 2011. Among 387 patients who agreed to participate, 275 with DSM-disorder) were included in the study cohort (Schizophrenia cohort). This study was conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all participants who underwent ajmaline testing10.
We compared the patients of the Schizophrenia cohort with age-comparable control persons from the Netherlands Study of Depression and Anxiety (NESDA), an ongoing longitudinal cohort study in the Netherlands11. We used data from 179 individuals,
selected randomly from a large sample of control volunteers in the NESDA study without psychiatric disorder, of whom ECG and cardiovascular measurements were made (cardiovascular subsample)12.
Since several studies suggested that schizophrenic patients have higher biologic age relative to their calendar age with commensurately increased prevalence of cardiovascular and metabolic disorders13,14, we studied a second control cohort ~20 years
older than the study cohort: the Hoorn Study cohort, in which participants, selected randomly from the population registry of the town of Hoorn, have been followed since 1989 15. After excluding 7 participants whose medication data were missing, and 4
participants who used antipsychotics, we studied ECGs of 1168 participants (Hoorn cohort).
Brugada-ECG according to the Brugada Syndrome (BrS) consensus criteria (Figure 431-450 msec, female 451-470 msec) or severe (male >450 msec, female >470 msec)16.
The diagnosis BrS requires typical ST-segment elevations in right precordial ECG leads (type-1 Brugada-ECG) and events suggestive of cardiac arrhythmia or a family history of BrS or SCD. In most BrS patients, the baseline ECG is only suspicious for provocation testing with a sodium channel-blocking drug (Figure 1)10. Accordingly, schizophrenia patients with baseline type-2/3 Brugada-ECG were invited for ajmaline testing in the Academic Medical Center. All patients with type-1 Brugada-ECG (at baseline or after ajmaline testing) underwent DNA screening of SCN5A17. SCN5A Moreover, family screening was offered. In the NESDA and Hoorn cohorts, ajmaline testing could not be performed. Therefore, the prevalences of baseline Brugada-ECG (type-1 or type-2/3) were compared between cohorts.
Chapter 3 37 V1 V2 B A V1 V2 10.
Risk factors for cardiovascular disease and SCD (previous myocardial infarction, hypertension, hypercholesterolemia, diabetes mellitus, body mass index) were derived (control cohorts)12,15. Drug use during ECG recording or ajmaline testing was derived from patient records (Schizophrenia cohort), questionnaire (Hoorn cohort), or drug container inspection (NESDA cohort). We assessed use of sodium-channel blockers19, QTc-prolonging drugs20, FGA, SGA, antidepressants, cardiovascular drugs (nitrates,
To analyze differences between cohorts in prevalence of co-morbidities and medication variables and analyses of variance for continuous variables. We performed multivariate logistic regression analyses to assess differences between cohorts in Brugada-ECG, Multivariate linear and logistic regression analyses were performed to investigate differences in quantitative ECG parameters, correcting for sex and factors that were sodium-channel blocker use and ECG outcomes, we compared schizophrenic patients
(N=275) (N=179)
Hoorn (N=1168)
% % %
Male gender 195 70.9 66 36.9 517 44.3
Mean age, years (mean, SD) 44.8 9.9 47.7 12.5 66.4 6.7 †
Mean BMI (mean, SD) 27.6 5.5 25.4 4.5 26.6 3.5
Smoking 181 65.8 n.a. n.a.
4 1.5 2 1.1 61 5.9
Hypertension 39 14.5 24 13.4 472 40.4
45 16.7 22 12.3 439 37.6
Diabetes mellitus 22 8.2 3 1.7 116 10.1
77 28.0 1 0.6 75 6.4
QT-interval prolonging drugs 177 64.4 2 1.1 44 3.8
Cardiovascular drugs 15 5.5 15 8.4 271 23.2
Lipid lowering drugs 29 10.5 10 5.6 94 8.0
236 85.8 0 0.0 0 0.0 18 6.5 1 0.6 5 0.4 69 25.1 0 0.0 9 0.8 13 4.7 0 0.0 4 0.3 49 17.8 SGA only 147 53.5 40 14.5 39 14.2 Clozapine 91 33.1 47 17.1 Aripiprazol 30 10.9 Risperidon 23 8.4 16 5.8 Haloperidol 14 5.1 Pimozide 12 4.4
Chapter 3
39
with or without sodium-channel blockers, and performed separate analyses with patients from the Schizophrenia cohort who used no sodium-channel blockers during ECG recording, employing the statistical methods described above. All statistics were performed in SPSS (version 20.0 for Mac, Chicago IL, USA).
Table 1 shows patient characteristics. Compared with NESDA controls, schizophrenia patients were slightly younger and more often male. They had higher body mass index (27.6 vs. 25.4, p <0.05), higher prevalence of diabetes mellitus (8.0 vs. 1.7%, p<0.05) and a trend towards higher lipid lowering drug use (10.5 vs. 5.6% p=0.07). (tricyclic antidepressants: 6.5%, selective serotonin receptor inhibitors: 25.1%, amitriptyline (10 mg OD) for neuralgy. Higher antidepressants and antipsychotics use caused higher use of sodium-channel blockers (28.0 vs. 0.6%) and QTc-prolonging drugs (64.4 vs. 1.1%).
Compared with Hoorn controls, schizophrenia patients were more often male and ~20 years younger (by design). Prevalence of diabetes mellitus was comparable (8.2 vs. 10.1%), but schizophrenia patients had lower prevalence of previous myocardial infarction, hypertension, and hypercholesterolemia (1.5, 14.2, and 16.7% vs. 5.9, 40.4, and 37.6%, all p<0.05). Accordingly, they used less often cardiovascular drugs (5.5 vs. 23.2%, p<0.05), but had comparable use of lipid lowering drugs. They used 0.8%, other: 4.7 vs. 0.3%, all p<0.05). Antipsychotics were only used by schizophrenia 14% used no antipsychotics. Sodium-channel blockers and QTc-prolonging drugs were used more frequently by schizophrenia patients (28.0 vs. 6.4% and 64.4 vs. 3.8%).
In the Schizophrenia cohort, 32 patients (11.6%) had Brugada-ECG at baseline: one had type-1 Brugada-ECG, while 31 had type-2/3 Brugada-ECG (Table 2). This was Brugada-ECGs, while 2 (1.1%) and 28 (2.4%, both p<0.05 vs. Schizophrenia cohort), respectively, had type-2/3 Brugada-ECG. Figure 2 shows Brugada-ECG prevalences in the study cohorts, and reported prevalences21. In the Schizophrenia cohort, ajmaline
testing was offered to the 31 patients with type-2/3 Brugada-ECG, accepted by 23, and found positive in 10. Thus, at least 11 patients had type-1 Brugada-ECG at baseline or after ajmaline testing (4% of all schizophrenia patients, 6 men, age 48.1±10.2 years). One patient had a mutation in SCN5A (c.3956G>T). Family screening was offered to all 11 patients with type-1 Brugada-ECG, but only conducted in 5 relatives of 4 patients, had Brugada-ECG. Supplemental Table s1 shows ECG parameters, co-morbidities, and medication use of all patients with type-1 Brugada-ECG. Four schizophrenia patients with type-2/3 Brugada-ECG who declined ajmaline testing provided additional medical at age <45 years had occurred in the family, and no family members suffered from schizophrenia.
Compared to NESDA controls, schizophrenia patients had higher heart rate (80.8 vs. 61.7 beats per minute, p<0.05) and longer QTc-interval (410.9 vs. 393.1 msec, p<0.05). They also had higher proportion of mild or severe QTc-prolongation (13.1% and 5.8% vs. 3.4% and 0.0%), but not when corrected for relevant covariates (BMI, diabetes mellitus status, use of sodium-channel blockers or QTc-prolonging drugs). Compared to Hoorn controls, schizophrenia patients had higher heart rate (80.8 vs. 68.0 beats per minute, p<0.05), but shorter QRS-interval (91.6 vs. 101.8 msec, p<0.05) and PR-interval (159.8 vs. 174.0 msec, p<0.05). QTc-interval was not different (410.9 vs. 401.9 msec, p=0.251). The proportion of mild or severe QTc-prolongation was higher different when corrected for relevant covariates (BMI, cardiovascular medication use, use of sodium-channel blockers or QTc-prolonging drugs).
To study whether use of sodium-channel blockers affected prevalence of Brugada-ECG and Brugada-ECG parameters in schizophrenia, we compared schizophrenia patients who used sodium-channel blockers (n=77) to those who did not (n=198) (Table 2). The groups differed in PR and QRS duration (165.8 vs. 157.4 and 94.5 vs. 90.5 msec,
Chapter 3 41 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% Schizophrenia with Na-channel blocking drugs (77) NESDA (179) Hoorn(1168) (138,050)Europe 8.0% 7.0% 6.0% 1.3% 15.6% 0.0% 0.0% 0.02% 1.1% 2.4% 0.13% Type 1 ECG Type 2 and 3 ECG 9.0% 10.0% 11.0% 12.0% 0.0% 9.6% Schizophrenia without Na-chan-nel blocking drugs
(198)
13.0% 14.0% 15.0%
(N=179) Hoorn (N=1168) (N=198) % % % % % 32 11.6 13 16.9 19 9.6 2 1.1 28 2.4 †§ 36 13.1 14 18.2 22 11.1 6 3.4 60 5.1 16 5.8 7 9.1 9 4.5 0 0.0 33 2.8 Heart r at e, bea ts per minut e 80.8 16.9 82.9 16.0 80.0 17.2 61.7 9.8 68.0 11.9 †§ 91.6 11.8 94.5 13.6 90.5 10.8 91.0 10.3 101.8 16.9 †§ 159.8 21.5 165.8 22.7 157.4 20.6 152.5 32.6 174.0 25.3 †§ 410.9 29.8 416.3 31.9 408.8 28.7 393.1 25.1 401.9 28.5
Chapter 3
43
both p<0.05), but were otherwise similar. Prevalence of Brugada-ECG was not different (16.9 vs. 9.6%, p=0.091).
To study whether ECG differences between the Schizophrenia and control cohorts may be attributed to use of sodium-channel blockers, we compared ECG parameters between schizophrenia patients who used no sodium-channel blockers with both control cohorts. Compared with NESDA controls, this schizophrenia subset had more Brugada-ECG (9.6 vs 1.1%, p<0.05), higher heart rate (80.0 vs. 61.7, p<0.05), and longer QTc-duration (408.8 vs. 393.1, p<0.05). Mild or severe QTc-prolongation was more prevalent in this schizophrenia subset (11.1 and 4.5% vs. 3.4 and 0.0%, prolonging drugs and diabetes).
Similarly, compared with Hoorn controls, this schizophrenia subset had higher prevalence of Brugada-ECG (9.6 vs. 2.4%, p<0.05), higher heart rate, and shorter QRS- and PR-interval. Mild or severe QTc-prolongation was more prevalent in this when corrected for relevant covariates (sex, BMI, QT-prolonging drugs and diabetes).
We found that Brugada-ECG has higher prevalence in schizophrenia patients than in similarly aged or ~20 years older non-schizophrenic controls. Importantly, the prevalence blocking drugs (notably antipsychotics). In contrast, while we also found, in accordance with previous studies7,22,23, that schizophrenia is associated with QTc-prolongation,
prolongation was largely explained by confounding factors, including use of QTc-prolonging (antipsychotics) drugs.
As many as 4% of schizophrenia patients had type-1 Brugada-ECG compared to an estimated prevalence in the general population of 0.05%21. This suggests a higher
prevalence of BrS in schizophrenia. This could partly explain the increased SCD risk in schizophrenia. Indeed, the yearly SCD incidence in our Schizophrenia cohort (19/8561 patient years = 0.2%, not shown) is higher than the incidence in the general population in the Netherlands (0.1%)24. Schizophrenia patients with BrS would be vulnerable to
the arrhythmia-causing effects of sodium-channel blocking medication, which include many antipsychotics25, particularly in combination with the increased prevalence of
cardiovascular risk factors that increase SCD-risk. Still, it must be noted that type-1 Brugada-ECG per se
to ascertain or obtain in schizophrenia patients. For instance, unexplained syncope (a often resulting from the blood pressure lowering effects of antipsychotics. Moreover, we had little opportunity to obtain a family history, as most patients had sparse contact with their relatives, and most available relatives declined investigation. Therefore, although the association between schizophrenia and Brugada-ECG suggested that BrS is more prevalent in schizophrenia, we could not prove this. It could be argued that use of antipsychotics may provoke a Brugada-ECG, thereby facilitating easier detection by ECG analysis. This may especially apply to sodium-channel blockers25, 26. Still, >50% of
patients with Brugada-ECG used no sodium-channel blockers (Supplemental Table s1), use of sodium-channel blockers. Furthermore, it is unlikely that use of sodium-channel blockers alone results in Brugada-ECG when an innate factor is absent27. Therefore, the
high prevalence of Brugada-ECG found here can probably not be solely attributed to use of sodium-channel blocking antipsychotics.
Future studies are required to establish the causes for the increased prevalence of Brugada-ECG (or BrS) in schizophrenia. Emerging evidence indicates that schizophrenia and acute psychosis may impact on cardiac electrophysiology22. Accordingly, genetic
studies suggest that the pathobiology of schizophrenia involves various voltage-gated ion channels28-30. Because these proteins also control cardiac electrophysiology, variants
in their encoding genes (KCNH2, CACNA1C) may increase arrhythmia and SCD-risk.
We did not screen KCNH2, CACNA1C in the patients with Brugada-ECG, but only
SCN5A, because SCN5A is the only gene routinely screened at our institution in BrS
patients17. Nevertheless, our observations lend support to the more general notion that
(nonstructural) brain disease and (electrical) heart disease share common underlying pathomechanisms. For instance, in epilepsy, too, the increased incidence of SCD31
may stem from expression of the same (mutant) ion channel in brain and heart32,33.
Furthermore, a recent study34 showed that Neuregulin1, related with both epilepsy and
schizophrenia, is also associated with SCD. Autonomic dysregulation may also explain the association between BrS and schizophrenia, being reported in both conditions. However, while reduced vagal tone exists in schizophrenia patients (including those not using antipsychotics)35 increased vagal tone may unmask Brugada-ECG and cause SCD
in BrS26.
If proven in future studies that increased prevalence of Brugada-ECG in schizophrenia of excess SCD-risk in schizophrenia patients, especially those using (antipsychotic or non-antipsychotic) medication that blocks cardiac depolarization. The risk for lethal cardiac arrhythmias in BrS is mediated by dysfunctional (impaired) depolarizing ion
Chapter 3
45
channels, notably, the cardiac sodium-channel. BrS-patients, through their innately impaired cardiac sodium-channels (reduced depolarization reserve), are particularly vulnerable to additional sodium-channel block exerted by some antipsychotics (and other drugs). BrS-patients may also be more vulnerable to the depolarization-blocking effects of concomitant conditions36. In particular, acute myocardial ischemia/infarction
(more likely to occur in schizophrenic patients, given their higher prevalence of diabetes) impairs cardiac depolarization37. BrS-patients may have particularly increased risk to
suffer lethal cardiac arrhythmias during acute myocardial ischemia/infarction. Thus, the combined effects of higher prevalence of Brugada-ECG and concomitant factors that impair cardiac depolarization such as drug use or (risk factors for) ischemic heart disease may partly explain the increased incidence of SCD in schizophrenia.
Similarly, we found increased prevalence of QTc-prolongation in schizophrenia patients. While mostly mild and not hazardous per se, this QTc-prolongation may
identify individuals at increased risk for lethal cardiac arrhythmias and SCD if concomitant factors that cause further QTc-prolongation (e.g., cardiac hypertrophy or heart failure caused by hypertension or heart disease) are also present. However, QTc-prolongation observed in schizophrenia patients was largely explained by confounding factors, suggesting that QTc-prolongation is not strongly associated with schizophrenia
per se, in contrast with the occurrence of Brugada-ECG. We used Bazett’s formula for
heart rate correction, since this method is most widely employed and allows for easy comparison with other studies. Although Bazett’s formula may overestimate QTc-duration at higher heart rates when compared to other rate correction methods, it is not resolved which method best captures co-variates of QT duration38.
A major strength of our study is that it involved a large group of schizophrenia patients with data on medication use and relevant co-morbidities during ECG recording. introduced yearly cardiovascular screening among schizophrenic patients enabled us to cohorts.
Our study has some limitations. We were unable to perform ajmaline testing in both control cohorts, and 8 schizophrenia patients declined ajmaline testing. Furthermore, data on family history of SCD in the Schizophrenia cohort was limited due
We found a strongly elevated prevalence of Brugada-ECG in schizophrenia patients.
schizophrenia patients, and prudent prescription of sodium-channel blockers, to minimize SCD-risk39.
The authors wish to express their gratitude to Jan Peetoom, internist, who performed the initial ECG analysis in the schizophrenia cohort, Remco Boerman, nurse practitioner, who recorded all ECGs in the schizophrenia cohort, and Irene Beems, who contributed to data collection. The authors greatly appreciate the contributions of Paulien Homma and Loes Bekkers for data collection and data entry, Julien Barc and Leander Beekman for DNA analysis, and thank Patrick Souverein for his help in analyzing medication data.
Vici 918.86.616), the Dutch Medicines Evaluation Board (MEB/CBG), the European Community’s Seventh Framework Programme (FP7, grant 241679, ARITMO), and Biobanking and Biomolecular Research Infrastructure The Netherlands (BBMRI-NL). The infrastructure for the NESDA study (www. nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant 10-000-1002) and supported by participating universities and mental health care organizations (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen, Lentis, GGZ Friesland, GGZ Drenthe,
Research (NIVEL), and Netherlands Institute of Mental Health and Addiction (Trimbos Institute). Additional cardiovascular measurements were supported by the Netherlands Heart Foundation (Grant Number 2006B258). The Hoorn Study was funded by the EMGO Institute VUmc, and has received grants from the Netherlands Diabetes Research Foundation and the Netherlands Organization for Health Research and Development.
Chapter 3
47 1. Tiihonen J, Lönnqvist J, Wahlbeck K, et al. 11-year follow-up of mortality in patients with 2. Saha S, Chant D, McGRath J. A systematic review of mortality in schizophrenia. Arch Gen Psychiatry. 3. De Hert M, Correll CU, Bobes J, et al. Physical illness in patients with severe mental disorders. I. 4. Appleby L, Thomas S, Ferier N, Lewis G, Shaw J, Amos T. Sudden unexplained death in psychiatric 5. Koponen H, Alaräisänen A, Saari K, et al. Schizophrenia and sudden cardiac death: a review. Nord J 6. Myerburg RJ, Castellanos A. Emerging paradigms of the epidemiology and demographics of sudden 7. Timour Q, Frassati D, Descotes J, Chevalier P, Christé G, Chahine M. Sudden death of cardiac origin 8. Ray WA, Chung CP, Murray KT, Hall K, Stein CM. Atypical antipsychotic drugs and the risk of 9. Haddad PM, Anderson IM. Antipsychotic-related QTc prolongation, torsade de pointes and sudden 10. Wilde AA, Antzelevitch C, Borggrefe M, et al. Proposed diagnostic criteria for the Brugada syndrome:
of Depression and Anxiety (NESDA): rationale, objectives and methods. Int J Methods Psychiatr Res. 12. Seldenrijk A, van Hout HP, van Marwijk HW, et al. Carotid atherosclerosis in depression and anxiety: 13. Kirkpatrick B, Messias E, Harvey PD, Fernandez-Egea E, Bowie CR. Is schizophrenia a syndrome of 14. Jeste DV, Wolkowitz OM, Palmer BW. Divergent trajectories of physical, cognitive, and psychosocial 15. De Vegt F, Dekker JM, Jager A, et al. Relation of impaired fasting and postload glucose with incident 16. Committee for Proprietary Medicinal Products. Points to consider: the Assessment of the Potential for
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of a possible pathogenic link between congenital long QT syndrome and epilepsy. Neurology. 34. Huertas-Vazquez A, Teodorescu C, Reinier K, et al. A Common Missense Variant in the Neuregulin1
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35. Bär KJ, Wernich K, Boettger S, et al. Relationship between cardiovagal modulation and psychotic state 36. Bardai A, Amin AS, Blom MT, et al. Sudden cardiac arrest associated with use of a non-cardiac drug that reduces cardiac excitability: evidence from bench, bedside, and community. Eur Heart J. 37. Shaw RM, Rudy Y. Electrophysiologic effects of acute myocardial ischemia: a theoretical study of 38. Malik M, Hnatkova K, Kowalski D, Keirns JJ, van Gelderen EM. QT/RR Curvatures in Healthy 39. Schneeweiss S, Avorn J. Antipsychotic agents and sudden cardiac death: how should we manage the
Chapter 3 49 SCD 1 Male 45 61 399 114 172 Aripipr az ol, Ipr atr opium No No No 2 Male 36 81 404 94 140 Clo zapine Yes No , but 1 st degr ee mood disor der No 3 Male 41 99 405 99 163 Clomipr amine No No No 4 55 110 423 87 127 Clo zapine No Yes, 1 st degr ee No 5 Male 61 80 424 120 144 No No No
SCD 6 59 71 426 130 208 Hypert ension, Lor az epam, Nortrip tyline, Pan topr oz ol, , No No No 7 60 80 431 142 230 Hypert ension Yes Yes, 1 st degr ee No 8 Male 56 69 432 104 216 Hypert ension Pimo zide, Aripipr az ol, Bisopr olol, Aliskir en Yes Unkno wn 9 Male 35 72 442 116 156 No No No 10 41 70 449 112 200 Nortrip tyline No No Yes, 2 nd degr ee 11 40 113 450 98 180 Nortrip tyline No No No
Chapter 3
51
prevalence in 7 European countries.
Type 1 Type 2 and 3
275 0.36 11.27 179 0.00 1.12 1,168 0.00 2.40 Austria 52,097 0.02 0.03 Denmark 18,974 0.00 0.07 3,021 0.00 0.60 36,309 0.03 0.20 Germany 4,149 0.00 0.00 Greece 11,488 0.02 0.20 Italy 12,012 0.02 0.26 21.
CHAPTER 4
M.T. Blom, A. Bardai, B.C. van Munster, M. Nieuwland, H. de Jong, D.A. van Hoeijen, A.M. Spanjaart, A. de Boer, S.E. de Rooij, H.L. Tan
