key: cord-0795455-e8q9pt8k
authors: Galanopoulou, Aristea S.; McArthur, David L.; Ferastraoaru, Victor; Correa, Daniel J.; Cherian, Koshi; Duberstein, Susan; Gursky, Jonathan; Hanumanthu, Rajani; Hung, Christine; Molinero, Isaac; Khodakivska, Olga; Legatt, Alan D.; Patel, Puja; Rosengard, Jillian; Rubens, Elayna; Sugrue, William; Yozawitz, Elissa; Mehler, Mark F.; Ballaban‐Gil, Karen; Haut, Sheryl R.; Malhotra, Rishi; Moshé, Solomon L.; Boro, Alexis
title: Response: Epileptic discharges in acutely ill patients investigated for SARS‐CoV‐2/COVID‐19 and the absence of evidence
date: 2020-10-14
journal: Epilepsia Open
DOI: 10.1002/epi4.12437
sha: 57f61b7faa83418956ba806d9a898914bcc490b4
doc_id: 795455
cord_uid: e8q9pt8k

nan

We appreciate the interest of Drs Rai, Gogia, and Tremont-Lukats in our preliminary report and their attempt to re-evaluate our findings using the Bayesian binomial statistics. 1 Their conclusion that more observations are needed is in close agreement with our manuscript's discussion and conclusions. One reason we published this work as a "preliminary report" was the low sample size of our case series and particularly of the COVID-19-negative group (n = 6), given that our study was done during the peak of COVID-19 pandemic in our region. 2 Along with this limitation, we were also cautious in our manuscript to highlight a number of other possible confounders that should be considered in future studies on the subject, among them false-negative rates of SARS-CoV-2/ COVID-19 testing and associated pre-existing and clinical data, as outlined also in the subsequent paragraphs.

Whether one chooses the Bayesian or frequentist statistics, confidence upon their statistical outputs is strongly dependent on the sample sizes. A simple thought experiment is shown in Figure 1 , using the same dataset that the authors used from our manuscript, that is, the rate of epileptiform discharges (EDs) in the COVID-19-negative (1/6, Group 1 or prior) and COVID-19-positive (9/22, Group 2 or posterior) cohorts. By merely increasing the sample size of the prior tenfold, while maintaining the same proportion of subjects with EDs over the total size (ie, from 1/6 to 10/60), and leaving the posterior (COVID-19-positive) dataset unchanged, both the simple sequential (SS) Bayesian A/B test and Fisher's exact test provide some level of statistical significance.

However, extrapolating findings from small-sample exploratory studies of new patient populations, like our study, to larger populations without collecting real data is hard to recommend. Careful selection of the prior distributions needs to be done to incorporate in the hypothesis factors that may be important in positively or negatively controlling the likelihood of occurrence of a tested outcome. As shown in our cohort, acutely ill patients investigated for COVID-19suspected presentations have multiple clinical confounders that can either increase or decrease the likelihood of appearance of EDs on their EEG, as shown in table 1 of our report. 2 These include comorbid conditions, such as hypoxia/hypoxemia or respiratory failure, metabolic or electrolyte abnormalities, the underlying inflammatory/infectious processes, prior history of epilepsy, and new acute neurological insults, any of which may potentially increase the risk of EDs. In contrast, as discussed in our report, the administration of antiseizure and/or sedative medications was often done in advance of an EEG study, following best clinical practice, and may have reduced the likelihood of observing seizures or EDs in the EEGs. The multitude of all of these confounding factors cannot be modeled with a sample size of 6 or 22, that is a key reason we advocated for further larger-scale studies to expand our preliminary observations and learn the true impact of SARS-CoV-2/COVID-19 infection on potentially activating epileptiform abnormalities.

Our study was the first published case series describing the EEG findings in acutely ill patients who were admitted and investigated for COVID-19, and we reported this not only to increase awareness about the potential impact of the virus on EEG and epileptiform abnormalities but also to encourage more studies on the subject. Subsequent to our report, case series of COVID-19-positive patients with EEG studies (n = 13-111 each) have been published by independent groups; yet, none has incorporated COVID-19-negative patient populations exhibiting similar presentations. Among COVID-19-positive patients, the reported rates of EDs in these recently published studies varied between 0% and 38% 3-10 ; our reported 40.9% rate falls at the higher end of the spectrum. Such a spread of rates of epileptiform EEGs among small-or moderate-scale case series from different institutions exemplifies how differences in inclusion criteria, study design, patient enrollment and demographics, and clinical history may alter outcomes. In addition to the clinical confounders discussed earlier, known factors that may contribute to this broad range of rates of epileptiform EEGs among these studies of acutely ill COVID-19-positive patients include the type of EEGs (mostly brief routine EEGs or longer records), the inclusion criteria with regard to indications for EEGs, or the severity of COVID-19 illness. Similar 2 | LETTER to our study, many of these studies also acknowledged the high percentage of patients on sedatives and/or antiseizure medications at the time of EEG that may decrease the yield of EDs. As also discussed in our report, adequately powered and controlled studies are needed to validate our findings, factoring all the plausible variables, to obtain a more accurate depiction of the likelihood for new EDs in the setting of acute COVID-19 illness. Certainly, the larger sample sizes that are likely to be achieved in the near future, for both COVID-19positive and COVID-19-negative patients, will offer a true depiction of the likelihood for new epileptiform EEG abnormalities in the setting of COVID-19 acute illness. Re-evaluation of the statistical significance of the difference in the rates of epileptiform discharges (EDs) observed in the EEGs of acutely ill COVID-19-negative (Group 1, 16.7%) vs COVID-19-positive (Group 2, 40.9%) patients, considering different sample sizes of the prior (Group 1) distribution. Our original data 2 (n = 6, Group 1) do not yield statistical significance when examined by either the Bayesian A/B test or a frequentist statistical method, Fisher's exact test. Increasing the sample size of the prior to n = 60 produces significant evidence, with both the Bayesian and Fisher's exact tests, favoring the alternate hypothesis, that the rates of epileptiform EEGs are higher in COVID-19-positive patients than in COVID-19-negative patients. The Bayesian A/B test was done using the JASP software (https://jasp-stats.org) and Fisher's exact test using JMP version 10.0.0 (SAS Institute Inc, Cary NC, USA)

Epileptic discharges in acutely ill patients investigated for SARS-CoV-2/COVID-19 and the absence of evidence

EEG findings in acutely ill patients investigated for SARS-CoV-2/COVID-19: a small case series preliminary report

Clinical electroencephalography findings and considerations in hospitalized patients with coronavirus SARS-CoV-2. medRxiv

Continuous EEG findings in patients with COVID-19 infection admitted to a New York academic hospital system

Electroencephalogram (EEG) in COVID-19: a systematic retrospective study

Delirium and encephalopathy in severe COVID-19: a cohort analysis of ICU patients

COVID-19)-associated encephalopathies and cerebrovascular disease: the New Orleans experience

Report on electroencephalographic findings in critically ill patients with COVID-19

Cerebral involvement in COVID-19 is associated with metabolic and coagulation derangements: an EEG study

Continuous Electroencephalography (cEEG) characteristics and acute symptomatic seizures in COVID-19 patients