Risk of in-Hospital Cardiac Arrest Among Medicare Beneficiaries Undergoing Video Electroencephalographic Monitoring Ahmed A. Malik, MD, Naseeb Ullah, MD, Malik M. Adil, MD, and Adnan I. Qureshi, MD Zeenat Qureshi Stroke Institute, St Cloud, MN, USA

Abstract

Journal of Vascular and Interventional Neurology, Vol. 8

Purpose—Sudden cardiac death is the dominant reason of sudden unexpected death in epilepsy (SUDEP). Anecdotal reports have documented cardiac arrest during video electroencephalographic (EEG) monitoring. We performed this study to determine the rate of cardiac arrest and need for cardiac resuscitation during video EEG monitoring. Methods—We used inpatient data from the Centers for Medicare and Medicaid Services (CMS)’s Linkable 2008–2010 Medicare Data Entrepreneur’s Synthetic Public Use File. Using the International Classification of Diseases 9th revision (ICD-9) primary diagnosis codes, we identified patients with epilepsy. We used the primary or secondary ICD-9 procedure codes to identify patients who underwent video EEG during admission. For primary endpoints, we identified patients who suffered cardiac arrest and those who underwent cardiorespiratory resuscitation (CPR). Results—A total of 6,087 patients (mean age 76±12 years; 3,354 women) were included; 5,597 patients had a primary diagnosis of epilepsy and no video EEG, 240 patients had a primary diagnosis of epilepsy and underwent video EEG, and 250 patients underwent a video EEG without any diagnosis of epilepsy. A total of 12 patients (0.2%, 95% CI: 0.7–0.8) suffered a cardiac arrest during their admission. Three patients (0.1%) underwent CPR during their admission. There was no in-hospital mortality. None of the patients in those undergoing video EEG suffered cardiac arrest or underwent CPR. Conclusion—While the risk of cardiac arrest during video EEG monitoring may exist, the rate of such events was negligible in our study comprising of elderly Medicare patients. Keywords Cardiac arrest; cardiopulmonary resuscitation; epilepsy; video EEG

Introduction Sudden cardiac death is the dominant reason of sudden unexpected death in epilepsy (SUDEP) due to the proarrhythmic effect of epileptic discharges [1]. The incidence of SUDEP is higher among persons with treatment-resistant epilepsy (up to three to nine per 1,000 person-years) compared to those with chronic epilepsy under adequate control (0.35 to one to two per 1,000 person-years) [2]. An increased sympathetic flow and decreased parasympathetic activity during and after seizures result in cardiac arrhythmias [3]. Anecdotal reports have documented cardiac arrest during video electroencephalographic

(EEG) monitoring due to the induction of seizures by sleep deprivation and withholding of antiepileptic medication. Such risk has prompted several institutions to perform video EEG monitoring in settings that are adept to responding to cardiac emergencies. We performed this study to determine the rate of cardiac arrest and need for cardiopulmonary resuscitation (CPR) during video EEG monitoring. Material and Methods We used data from the Centers for Medicare and Medicaid Services (CMS)’s Linkable 2008-2010 Medicare

Vol. 8, No. 4, pp. 39–42. Published October, 2015. All Rights Reserved by JVIN. Unauthorized reproduction of this article is prohibited Corresponding Author: Ahmed Malik, MD, Zeenat Qureshi Stroke Institute, 519 2nd St N, St Cloud, MN 56303, USA. [email protected]

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Data Entrepreneur’s Synthetic Public Use File (DE-SynPUF). The file contains synthesized data taken from a 5% random sample of medicare beneficiaries in 2008 and their claims from 2008 to 2010. (CMS Linkable 2008-2010 Medicare DE-SynPUF Codebook http:// www.cms.gov/Research-Statistics-Data-and-Systems/ Statistics-Trends-andReports/SynPUFs/ DE_Syn_PUF.html). The DE-SynPUF consists of five types of data: beneficiary summary, inpatient claims, outpatient claims, carrier claims, and prescription drug events. (CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) http://www.cms.gov/ Research-Statistics-Data-and-Systems/Statistics-TrendsandReports/SynPUFs/DE_Syn_PUF.html).

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In our study, we used the data from the inpatient claims files. We used the International Classification of Diseases 9th revision (ICD-9) primary diagnosis codes 345.00–345.99 to identify patients with epilepsy and those with status epilepticus (ICD-9 diagnosis code 345.3). We also used the primary or secondary ICD-9 procedure code 89.19 to identify patients who underwent a video EEG during their admission. The patients with a primary diagnosis of epilepsy were further divided into those who underwent video EEG and those who did not undergo video EEG monitoring during their admission. We further obtained demographic data (age, gender, and race/ethnicity), and data on the comorbid medical conditions, stroke/TIA, ischemic heart disease, and diabetes mellitus from the beneficiary claims files. Data on additional medical comorbid conditions (hypertension [primary or secondary ICD-9 codes 401.0, 401.1, and 401.9]; status epilepticus [primary or secondary ICD-9 diagnosis code 345.3]; nicotine dependence [primary or secondary ICD-9 diagnosis code 305.1]) were obtained from the inpatient claims files. The length of hospital stay for each of these patient groups was also determined. For our primary endpoints, we identified patients who suffered cardiac arrest (primary or secondary ICD-9 diagnosis code 427.5) and those who underwent CPR (secondary ICD-9 procedure code 99.60) during hospitalization. We also identified patients who suffered paroxysmal supraventricular tachycardia (primary or secondary ICD-9 diagnosis code 427.0), paroxysmal ventricular tachycardia (primary or secondary ICD-9 diagnosis code: 427.1 and 427.2), atrial fibrillation (primary or secondary ICD-9 diagnosis code 427.31), and atrial flutter (primary or secondary ICD-9 diagnosis code 427.42).

Statistical Methods The IBM SPSS 20 statistical software package (IBM Corp., Armonk, NY) was used for all analyses. We used descriptive statistics to report the rate of cardiac arrest and CPR with 95% confidence interval in patients undergoing video EEG monitoring. We further provided the rates in three groups of patients: patients hospitalized with a primary diagnosis of epilepsy who underwent or did not undergo video EEG monitoring, and those who underwent video EEG monitoring without a primary diagnosis of epilepsy. We compared the rates of cardiovascular risk factors among the three patient populations. We used chi square and analysis of variance (ANOVA) tests for categorical and continuous variables, respectively. Bonferroni correction was applied for determining statistical significance for multiple comparisons.

Results A total of 6,087 patients were included in the study (mean age 76±12 years; 3,354 were women). There were 5,597 patients in the first group (patients with a primary diagnosis of epilepsy and no video EEG during their admission), 240 patients in the second group (patients with a primary diagnosis of epilepsy who underwent video EEG), and 250 patients in the third group (patients who underwent a video EEG without any diagnosis of epilepsy). In the first group of patients, 3,074 (54.9%) were women, 4,590 (82.0%) were white, and 3,133 (56.0%) of patients were aged greater than 75 years. The mean length of hospital stay for patients in this group was five ± four days. The second group of patients consisted of 132 (55.0%) women, 197 (82.1%) whites, and 143 (59.6%) patients aged greater than 75 years. The mean length of hospital stay for patients in this group was five ± four days. In the third group of patients, 148 (59.2%) were women, 126 (50.4%) of these patients were aged greater than 75 years, and 195 (78.0%) were whites. These patients had a mean length of hospital stay of five ± five days. There was no difference in the rates of hypertension, diabetes mellitus, nicotine dependence, and previous stroke/TIA between the three groups. There was also no difference in the length of hospitalization between the three groups (p = 0.20). A total of 12 patients (0.2%, 95% CI: 0.7–0.8) suffered a cardiac arrest during their admission. Of these 12

Malik et al.

patients, none had a primary or secondary diagnosis of status epilepticus. None of these patients had a diagnosis of myocardial infarction. The epilepsy was classified as generalized convulsive epilepsy (ICD-9 diagnosis code 345.10) in three of 12 patients and it was classified as epilepsy, unspecified (ICD-9 diagnosis code 345.90) in nine of 12 patients. Three patients (0.1%), underwent CPR during their admission. There was no in hospital mortality. None of the patients in those undergoing video EEG suffered cardiac arrest or underwent cardiopulmonary resuscitation.

Discussion Journal of Vascular and Interventional Neurology, Vol. 8

Anectodal reports highlight the risk of cardiac arrest and need for CPR in patients with epilepsy. Montepietra et al.[4] reported five patients who had ischemic ECG changes and MI immediately following either convulsive or nonconvulsive seizures. Some of the patients had postictal supraventricular tachycardia or atrial fibrillation, suggesting that tachyarrhythmias may contribute to cardiac arrest in patients with chronic epilepsy [5]. Although video EEG is performed after tapering/withholding of antiepileptic medication, sleep deprivation, during sleep, photostimulation and, in a few cases, hyperventilation, other settings/triggers of seizures, such as alcohol consumption/withdrawal, caffeine, drug intake/withdrawal, stress and fever, were not found specifically in a literature review of seizures during video EEG. The settings which increase the likelihood of seizures during video EEG monitoring are also not separately described in the literature. In one case report, a 16-year-old boy developed severe tachycardia during seizure while undergoing video EEG. The patient died after two months during a generalized tonic clonic seizure [5]. Another study reported on two cases of SUDEP in patients with intractable temporal lobe epilepsy undergoing video EEG. Both patients had secondarily generalized convulsions. Postictal hypoventilation was implicated, which may have resulted in hypoxemia and acidosis leading to bradycardia and asystole [6]. Prominent hypercarbia during the ictal and postictal state in convulsive and nonconvulsive seizures can be seen in one-third of patients with treatment-resistant partial epilepsy [7] and may contribute to cardiac arrest. Seizures can also directly induce bradycardia and asystole [8,9,10]. While the risk of cardiac arrest during video EEG monitoring may exist, the rate of such events was negligible in our study comprising of elderly Medicare patients. In addition to the extremely low incidence, elderly patients

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may have previous cardiopulmonary evaluation and high-risk patients are more likely to receive antiarrhythmic medication and/or implantable defibrillators. The rate of temporal lobe involvement may be lower in elderly patients in whom secondary epilepsy is more frequent [11]. Therefore, the possibility that the rates may be higher among younger patients cannot be excluded. The large number of patients derived from a national sample supports the generalizability of our results. The current data do not support video EEG monitoring in settings with high intensity of support staff and cardiorespiratory monitoring and emergency care availability. However, our identification of events depends upon the accuracy of ICD-9 codes used in medical records. A prospective observational study of the incidence of inhospital cardiac arrest from January 2006 to July 2009 found the ICD-9 code 427.5 for a positive predictive value of 64.4% and a specificity of 99.8% for true cardiac arrest. Sensitivity and negative predictive value increased significantly when additional parameters of evidence of cardiac arrest, such pharmacy crash cart items used and billed for cardiac arrest, were added to the ICD-9 code for cardiac arrest [12].

Conclusion Our study is one of the first studies to provide estimates of cardiac arrest and CPR in elderly patients admitted for video EEG monitoring. Such data will be valuable for determining the appropriate settings for video EEG monitoring.

Disclosures and Conflicts of Interest None of authors has any conflict of interest to disclose

Funding Adnan Qureshi; NIH U01-NS062091-01A2 The material in the manuscript has not been published and is not being considered for publication elsewhere in whole or in part in any language except as an abstract. Acknowledgments None

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42 3. Timmings PL. Sudden unexpected death in epilepsy: Is carbamazepine implicated? Seizure 1998:289–291. 4. Montepietra S, Cattaneo L, Granella F, Montepietra S, Cattaneo L, Granella F, Maurizio A, Sasso E, Pavesi G, Bortone E. Myocardial infarction following convulsive and nonconvulsive seizures. Seizure 2009;18:379–381. 5. Pinto KG, Scorza FA, Arida RM, Pinto KG, Scorza FA, Arida RM, Cavalheiro EA, Martins LD, Machado HR, Sakamoto AC, Terra VC. Sudden unexpected death in an adolescent with epilepsy: all roads lead to the heart? Cardiol J 2011;18(2):194–196. 6. Bateman LM, Spitz M, Seyal M. Ictal hypoventilation contributes to cardiac arrhythmia and SUDEP: report on two deaths in video-EEGmonitored patients. Epilepsia 2010;51(5):916–920. 7. Seyal M, Bateman LM, Albertson TE, Seyal M, Bateman LM, Albertson TE, Lin TC, Li CS. Respiratory changes with seizures in localization-related epilepsy: analysis of periictal hypercapnia and airflow patterns. Epilepsia 2010;51(8):1359–1364.

8. Rocamora R, Kurthen M, Lickfett L, Rocamora R, Kurthen M, Lickfett L, Von Oertzen J, Elger CE. Cardiac asystole in epilepsy: clinical and neurophysiological features. Epilepsia 2003;44:179– 185. 9. Schuele S, Bermeo AC, Alexopoulos AV, Locatelli ER, Burgess RC, Dinner DS, Foldvary-Schaefer N. Video-electrographic and clinical features in patients with ictal asystole. Neurology 2007;69:434–441. 10. Schuele S, Bermeo AC, Locatelli E, Burgess RC, Lüders HO. Ictal asystole: a benign condition? Epilepsia 2008;49:168–171. 11. Luhdorf K, Jensen LK, Plesner AM. Etiology of seizures in the elderly. Epilepsia 1986;27:458–463. 12. Yuen TC, Saner D, Walsh D, Edelson DP. Validation of claims data for determination of in-hospital cardiac arrest incidence. Circulation 2010;122:A60.

Journal of Vascular and Interventional Neurology, Vol. 8

Risk of In-Hospital Cardiac Arrest Among Medicare Beneficiaries Undergoing Video Electroencephalographic Monitoring.

Sudden cardiac death is the dominant reason of sudden unexpected death in epilepsy (SUDEP). Anecdotal reports have documented cardiac arrest during vi...
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