Primary care electronic medical records can be used to predict risk and identify potentially modifiable factors for early and late death in adult onset epilepsy.

OBJECTIVE: To use clinically informed machine learning to derive prediction models for early and late premature death in epilepsy. METHODS: This was a population-based primary care observational cohort study. All patients meeting a case definition for incident epilepsy in the Health Improvement Network database for inclusive years 2000-2012 were included. A modified Delphi process identified 30 potential risk factors. Outcome was early (within 4 years of epilepsy diagnosis) and late (4 years or more from diagnosis) mortality. We used regularized logistic regression, support vector machines, Gaussian naive Bayes, and random forest classifiers to predict outcomes. We assessed model calibration, discrimination, and generalizability using the Brier score, mean area under the receiver operating characteristic curve (AUC) derived from stratified fivefold cross-validation, plotted calibration curves, and extracted measures of association where possible. RESULTS: We identified 10 499 presumed incident cases from 11 194 182 patients. All models performed comparably well following stratified fivefold cross-validation, with AUCs ranging from 0.73 to 0.81 and from 0.71 to 0.79 for early and late death, respectively. In addition to comorbid disease, social habits (alcoholism odds ratio [OR] for early death = 1.54, 95% confidence interval [CI] = 1.12-2.11 and OR for late death = 2.62, 95% CI = 1.66-4.16) and treatment patterns (OR for early death when no antiseizure medication [ASM] was prescribed at baseline = 1.33, 95% CI = 1.07-1.64 and OR for late death after receipt of enzyme-inducing ASM at baseline = 1.32, 95% CI = 1.04-1.66) were significantly associated with increased risk of premature death. Baseline ASM polytherapy (OR = 0.55, 95% CI = 0.36-0.85) was associated with reduced risk of early death. SIGNIFICANCE: Clinically informed models using routine electronic medical records can be used to predict early and late mortality in epilepsy, with moderate to high accuracy and evidence of generalizability. Medical, social, and treatment-related risk factors, such as delayed ASM prescription and baseline prescription of enzyme-inducing ASMs, were important predictors.

Outcomes of stereoelectroencephalography exploration at an epilepsy surgery center.

OBJECTIVES: Epilepsy surgery is offered in resistant focal epilepsy. Non-invasive investigations like scalp video EEG monitoring (SVEM) help delineate epileptogenic zone. Complex cases may require intracranial video EEG monitoring (IVEM). Stereoelectroencephalography (SEEG)-based intracerebral electrode implantation has better spatial resolution, lower morbidity, better tolerance, and superiority in sampling deep structures. Our objectives were to assess IVEM using SEEG with regard to reasoning behind implantation, course, surgical interventions, and outcomes. MATERIALS AND METHODS: Seventy-two admissions for SEEG from January 2014 to December 2018 were included in the study. Demographic and clinical data were retrospectively collected. RESULTS: The cohort comprised of 69 adults of which 34 (47%) had lesional MRI. Reasons for SEEG considering all cases included non-localizing ictal onset (76%), ictal-interictal discordance (21%), discordant semiology (17%), proximity to eloquent cortex (33%), nuclear imaging discordance (34%), and discordance with neuropsychology (19%). Among lesional cases, additional reasons included SVEM discordance (68%) and dual or multiple pathology (47%). Forty-eight patients (67%) were offered resective surgery, and 41 underwent it. Twenty-three (56%) had at least one year post-surgical follow-up of which 14 (61%) had Engels class I outcome. Of the remaining 23 who were continued on medical management, 4 (17%) became seizure-free and 12 (51%) had reduction in seizure frequency. CONCLUSION: SEEG monitoring is an important and safe tool for presurgical evaluation with good surgical and non-surgical outcomes. Whether seizure freedom following non-surgical management could be related to SEEG implantation, medication change, or natural course needs to be determined.