Hernan Nicolas Lemus

Objective: Intracranial electroencephalography (iEEG) practices and diagnostic testing vary among institutions, limiting standardization and optimization of surgical outcomes. This study estimates resection rates and surgical outcome effect sizes after iEEG and develops individual patient‐level data (IPD) models using the missing‐indicator method to approximate real‐world clinical uncertainties and practice variability in iEEG and surgical decision‐making. Methods: Databases were queried for studies reporting ≥12‐month outcomes after epilepsy surgery in patients with drug‐resistant epilepsy (DRE) who underwent iEEG. Random‐effects meta‐analysis estimated pooled outcome proportions. IPD analysis was performed using multivariable generalized linear regression and logistic regression models with cross‐validation and calibration assessment. Missing values for key predictors were retained as a “not specified” category to evaluate the impact of incorporating uncertainty in common clinical data on outcome prediction. Results: Primary outcomes were seizure freedom (Engel class IA or International League Against Epilepsy [ILAE] 1) and favorable seizure outcomes (Engel I or ILAE 1–2). Of 4975 studies identified, 115 met the inclusion criteria. The pooled frequency of patients undergoing resection after iEEG was 67% (95% confidence interval [CI] 60%–74%), 52% achieved seizure freedom (95% CI 48%–56%), and 58% achieved a favorable outcome (95% CI 54%–62%). IPD analysis included 1305 patients from 29 studies and identified a lesional magnetic resonance imaging (MRI; odds ratio [OR] 1.94, CI 1.48–2.55, p < .0001) and a cortical malformation (OR 1.47, CI 1.08–2.00, p = .01) as independent predictors of a favorable outcome, whereas iEEG implantation strategy (bilateral implantation OR .61, 95% CI .42–.89, p = .01) was an independent predictor of a less favorable outcome. Significance: About two‐thirds of patients undergoing iEEG proceeded to resection, and about half achieved seizure freedom. Multivariable models incorporating clinical uncertainty and practice variability were well calibrated. However, modest discrimination suggests that routinely available clinical variables alone have limited ability to predict individual surgical outcomes. More standardized and granular clinical and practice‐related data may improve individualized prognostication after iEEG.

Background and ObjectivesLate-onset unexplained epilepsy (LOUE) has been linked to accelerated cognitive decline. However, the role of neurodegenerative proteins and whether they exert their effect...

Objective: Electronic health records (EHRs) are increasingly used to conduct research and evaluate epilepsy quality of care. We examined the accuracy of International Classification of Diseases, 9th and 10th Revision, Clinical Modification (ICD‐9‐CM, ICD‐10‐CM)‐ and antiseizure medicine (ASM)‐based algorithms for adult epilepsy.Methods: Data from a diverse New York multicenter EHR were queried to identify encounters between January 1, 2012 and September 20, 2018 coded with an epilepsy/seizure ICD‐CM code or an ASM. Eight hundred adults were randomly selected (350 with epilepsy‐related codes, 150 with an ASM, and 300 with drug‐resistant epilepsy codes). With chart review defined as the reference standard, sensitivity (Sn), specificity (Sp), negative predictive value (NPV), positive predictive value (PPV), and Youden index (YI) were calculated to evaluate various ICD‐9‐CM‐, ICD‐10‐CM‐, ± ASM‐based algorithms' accuracy in predicting epilepsy.Results: Ninety‐four algorithms were tested. A total of 435 (54.4%) patients had definite epilepsy. Estimates ranged as follows: YI = .18–.68, Sn = .59–.95, Sp = .52–.97, PPV = .67–.92, and NPV = .51–.93. The best algorithms were as follows. Highest YI for ICD‐9‐CM was single encounter with 345 (except 345.2 or 345.3) or 345.2, 345.3, or 780.3 with an ASM (Sn = .95, Sp = .73, PPV = .81, NPV = .92, YI = .68). Highest Y1 for ICD‐10‐CM was one encounter with G40 in primary diagnostic position or ≥2 encounters with G40 in any diagnostic position (Sn = .82, Sp = .85, PPV = .87, NPV = .80, YI = .67). Highest sensitivity was any encounter with ICD‐9‐CM 345 or 780.39 or ICD‐10‐CM G40, G41, or R56.9 (Sn = .96, Sp = .57, PPV = .73, NPV = .93, YI = .53). Highest specificity was ≥1 hospitalization with ICD‐9‐CM 345.x (except 345.2 and 345.3) or ICD‐10‐CM G40.x (Sn = .21, Sp = .97, PPV = .89, NPV = .51, YI = .18).Significance: We identified ICD‐9/10‐CM‐based case definitions (with and without ASM) that were sensitive and specific for epilepsy. Ultimately, extensive algorithms are provided to help inform case definition selection according to future study aims.

Objective: Temporal encephaloceles (TEs) are seen in patients with drug‐resistant epilepsy (DRE); yet they are also common incidental findings. Variability in institutional pre‐surgical epilepsy practices and interpretation of epileptogenic network localization contributes to bias in existing epilepsy cohorts with TE, and therefore the relevance of TE in DRE remains controversial. We sought to estimate effect sizes and sample sizes necessary to demonstrate clinically relevant improvements in seizure outcome with different surgical approaches.Methods: We searched Medline, Embase, and Cochrane to identify studies reporting the outcomes of epilepsy surgery after 12 months in patients with DRE and TE. The main outcome was seizure freedom or favorable seizure outcome. We also assessed the rates of seizure freedom among patients with DRE, TE, and the following covariables: use of intracranial electroencephalography (iEEG), side of the encephalocele, sex, and type of surgical resection (anterior temporal lobectomy [ATL] vs lesionectomy). Random‐effects meta‐analysis was used to calculate the proportion of patients attaining seizure outcomes.Results: We identified 332 studies, of which 15 (282 patients) met inclusion criteria for meta‐analysis. Seizure‐freedom rate was 65% (95% confidence interval [CI] 53–76), whereas the favorable outcome rate was 78% (95% CI 70–85). There was no significant interstudy heterogeneity. Patients with TE undergoing iEEG (risk ratio [RR] 0.80, 95% CI 0.74–0.87) had a lower chance of a favorable seizure outcome. A power analysis estimated a sample size of 28 932 patients with TE (13 764 with ATL) necessary to determine a significant difference in seizure freedom between limited resection and ATL.Significance: Retrospective cohort studies demonstrate good outcomes after TE resection regardless of the extent of resection. Prohibitively large sample sizes necessary to observe outcome differences between surgical approaches and presurgical predictors indicate that improved biomarkers and mechanistic understanding of TE epileptogenicity are needed.

Purpose: To investigate the utility of a 15-minute online module to improve the self-confidence and knowledge of neurology trainees when screening an EEG. Methods: We developed a fast, convenient, and accessible 15-minute online module to teach basic concepts of EEG screening using a five-step approach. To assess the efficacy of the module among neurology trainees, three surveys were developed. The EEG module and surveys were distributed to neurology trainees at multiple institutions in the United States. Associations between continuous variables were evaluated using t-test and ANOVA test. Results: A total of 199 residents from 7 different adult neurology residency programs participated in the study. We obtained a response rate of 38% (76/199) for EEG survey 1 and 25% (49/199) for the demographic survey, among junior and senior residents; 22 senior residents completed EEG survey 2. The online EEG module improved confidence when interpreting an EEG among junior residents (1.67 vs. 2.56, p < 0.0001). Senior residents improved their EEG survey score after completing the online module (53.9 vs. 68.6%, p < 0.001). Most of the trainees would recommend the online module to other trainees (93.9%) and would consider including it in an introductory course (93.9%). Conclusions: A brief and accessible online EEG module was easily implemented as an early introduction to EEG screening for junior neurology trainees and to improve EEG screening skills for senior trainees. These findings clarify specific areas where EEG learning may be optimized and reinforce the importance of implementing innovative curricula that are accessible and efficient for all neurology trainees.

Objective To describe the frequency of neuropsychiatric complications among hospitalized patients with coronavirus disease 2019 (COVID-19) and their association with pre-existing comorbidities and clinical outcomes. Methods We retrospectively identified all patients hospitalized with COVID-19 within a large multicenter New York City health system between March 15, 2020 and May 17, 2021 and randomly selected a representative cohort for detailed chart review. Clinical data, including the occurrence of neuropsychiatric complications (categorized as either altered mental status [AMS] or other neuropsychiatric complications) and in-hospital mortality, were extracted using an electronic medical record database and individual chart review. Associations between neuropsychiatric complications, comorbidities, laboratory findings, and in-hospital mortality were assessed using multivariate logistic regression. Results Our study cohort consisted of 974 patients, the majority were admitted during the first wave of the pandemic. Patients were treated with anticoagulation (88.4%), glucocorticoids (24.8%), and remdesivir (10.5%); 18.6% experienced severe COVID-19 pneumonia (evidenced by ventilator requirement). Neuropsychiatric complications occurred in 58.8% of patients; 39.8% experienced AMS; and 19.0% experienced at least one other complication (seizures in 1.4%, ischemic stroke in 1.6%, hemorrhagic stroke in 1.0%) or symptom (headache in 11.4%, anxiety in 6.8%, ataxia in 6.3%). Higher odds of mortality, which occurred in 22.0%, were associated with AMS, ventilator support, increasing age, and higher serum inflammatory marker levels. Anticoagulant therapy was associated with lower odds of mortality and AMS. Conclusion Neuropsychiatric complications of COVID-19, especially AMS, were common, varied, and associated with in-hospital mortality in a diverse multicenter cohort at an epicenter of the COVID-19 pandemic.

To investigate neurologists' practice variability in antiseizure medication (ASM) initiation after a first unprovoked seizure based on reported EEG interpretations. We developed a 15-question multiple-choice survey incorporating a standardized clinical case scenario of a patient with a first unprovoked seizure for whom different EEG reports were provided. The survey was distributed among board-certified neurologists practicing in the United States. Associations between categorical variables were evaluated using the Fisher Exact test. Multivariate analysis was performed using logistic regression. A total of 106 neurologists responded to the survey. Most responders (75%-95%) would start ASM for definite epileptiform features on EEG, with similar rates between subgroups differing in years of practice, presence of subspecialty EEG training, and self-reported confidence in EEG interpretation. There was greater variability in practice for nonspecific EEG abnormalities, with sharply contoured activity, sharp transients, and focal delta slowing associated with the highest variability and uncertainty. Neurologists with >5 years of practice experience (21% vs 44%, OR 0.35 [95% CI 0.13-0.89], p = 0.021), subspecialty EEG training (15% vs 50%, OR = 0.17 [95% CI 0.06-0.48], p < 0.001), and greater confidence in EEG interpretation (21% vs 52%, OR 0.24 [95% CI 0.09-0.62], p = 0.001) were less likely to start ASM for ≥2 nonspecific EEG abnormalities and reported greater uncertainty. In multivariate analysis, seniority (p = 0.039) and subspecialty EEG training (p = 0.032) were associated with decreased ASM initiation for nonspecific EEG features. There was substantial variability in ASM initiation practices between board-certified neurologists after a first unprovoked seizure with nonspecific EEG abnormalities. These findings clarify specific areas where EEG reporting may be optimized and reinforces the importance of implementing evidence-based practice guidelines.

Coronavirus disease 2019 (COVID-19) is associated with mortality in persons with comorbidities. The aim of this study was to evaluate in-hospital outcomes in patients with COVID-19 with and without epilepsy. We conducted a retrospective study of patients with COVID-19 admitted to a multicenter health system between March 15, 2020, and May 17, 2021. Patients with epilepsy were identified using a validated International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)/ICD-10-CM case definition. Logistic regression models and Kaplan-Meier analyses were conducted for mortality and non-routine discharges (i.e., not discharged home). An ordinary least-squares regression model was fitted for length of stay (LOS). We identified 9833 people with COVID-19 including 334 with epilepsy. On univariate analysis, people with epilepsy had significantly higher ventilator use (37.70% vs 14.30%, p < .001), intensive care unit (ICU) admissions (39.20% vs 17.70%, p < .001) mortality rate (29.60% vs 19.90%, p < .001), and longer LOS (12 days vs 7 days, p < .001). and fewer were discharged home (29.64% vs 57.37%, p < .001). On multivariate analysis, only non-routine discharge (adjusted odds ratio [aOR] 2.70, 95% confidence interval [CI] 2.00-3.70; p < .001) and LOS (32.50% longer, 95% CI 22.20%-43.60%; p < .001) were significantly different. Factors associated with higher odds of mortality in epilepsy were older age (aOR 1.05, 95% CI 1.03-1.08; p < .001), ventilator support (aOR 7.18, 95% CI 3.12-16.48; p < .001), and higher Charlson comorbidity index (CCI) (aOR 1.18, 95% CI 1.04-1.34; p = .010). In epilepsy, admissions between August and December 2020 or January and May 2021 were associated with a lower odds of non-routine discharge and decreased LOS compared to admissions between March and July 2020, but this difference was not statistically significant. People with COVID-19 who had epilepsy had a higher odds of non-routine discharge and longer LOS but not higher mortality. Older age (≥65), ventilator use, and higher CCI were associated with COVID-19 mortality in epilepsy. This suggests that older adults with epilepsy and multimorbidity are more vulnerable than those without and should be monitored closely in the setting of COVID-19.