Characterizing the treatment gap in the United States among adult patients with a new diagnosis of epilepsy.

OBJECTIVE: Epilepsy is largely a treatable condition with antiseizure medication (ASM). Recent national administrative claims data suggest one third of newly diagnosed adult epilepsy patients remain untreated 3 years after diagnosis. We aimed to quantify and characterize this treatment gap within a large US academic health system leveraging the electronic health record for enriched clinical detail. METHODS: This retrospective cohort study evaluated the proportion of adult patients in the health system from 2012 to 2020 who remained untreated 3 years after initial epilepsy diagnosis. To identify incident epilepsy, we applied validated administrative health data criteria of two encounters for epilepsy/seizures and/or convulsions, and we required no ASM prescription preceding the first encounter. Engagement with the health system at least 2 years before and at least 3 years after diagnosis was required. Among subjects who met administrative data diagnosis criteria, we manually reviewed medical records for a subset of 240 subjects to verify epilepsy diagnosis, confirm treatment status, and elucidate reason for nontreatment. These results were applied to estimate the proportion of the full cohort with untreated epilepsy. RESULTS: Of 831 patients who were automatically classified as having incident epilepsy by inclusion criteria, 80 (10%) remained untreated 3 years after incident epilepsy diagnosis. Manual chart review of incident epilepsy classification revealed only 33% (78/240) had true incident epilepsy. We found untreated patients were more frequently misclassified (p < .001). Using corrected counts, we extrapolated to the full cohort (831) and estimated <1%-3% had true untreated epilepsy. SIGNIFICANCE: We found a substantially lower proportion of patients with newly diagnosed epilepsy remained untreated compared to previous estimates from administrative data analysis. Manual chart review revealed patients were frequently misclassified as having incident epilepsy, particularly patients who were not treated with an ASM. Administrative data analyses utilizing only diagnosis codes may misclassify patients as having incident epilepsy.

Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

OBJECTIVE: To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR). BACKGROUND: Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers. METHODS: We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was developed from 150 clinic notes from institution #1 (development set), then tested on a separate set of 219 notes from institution #1 (internal test set) with 248 unique seizure frequency elements. The algorithm was separately applied to 100 notes from institution #2 (external test set) with 124 unique seizure frequency elements. Algorithm performance was measured by recall (sensitivity), precision (positive predictive value), and F1 score (geometric mean of precision and recall). RESULTS: In the internal test set, the algorithm demonstrated 70% recall (173/248), 95% precision (173/182), and 0.82 F1 score compared to manual review. Algorithm performance in the external test set was lower with 22% recall (27/124), 73% precision (27/37), and 0.40 F1 score. CONCLUSIONS: These results suggest NLP extraction of seizure types and frequencies is feasible, though not without challenges in generalizability for large-scale implementation.

Combined VNS-RNS Neuromodulation for Epilepsy.

SUMMARY: The vagus nerve stimulator (VNS) and responsive nerve stimulator (RNS) are nonpharmacological devices approved for drug-resistant epilepsy. Vagus nerve stimulator was removed before placing an RNS in clinical trials. Two cases of bilateral mesial temporal epilepsy treated concurrently with VNS and bilateral mesial temporal RNS devices were reported. In each case, the VNS device was turned off temporarily, which allowed for a direct comparison of RNS recordings and efficacy with and without simultaneous VNS stimulation. Temporary VNS cessation lead to increased clinical and electrocorticographic seizures despite continued anti-seizure drugs and RNS stimulation. In one case, VNS eliminated seizures from one epileptogenic area, whereas VNS and RNS were required to treat seizures from the contralateral mesial temporal structure. In another case, VNS effectively decreased seizure spread to the symptomatogenic zone. These cases demonstrate synergistic neuromodulation with concurrent use of VNS and RNS in intractable bitemporal epilepsy.

Population-Based Study of Nonelective Postpartum Readmissions in Women With Stroke, Migraine, Multiple Sclerosis, and Myasthenia Gravis.

OBJECTIVE: To compare maternal obstetric complications and nonelective readmissions in women with common neurologic comorbidities (WWN) vs women without neurologic disorders. METHODS: We performed a retrospective cohort study of index characteristics and acute postpartum, nonelective rehospitalizations from the 2015-2017 National Readmissions Database using ICD-10 codes. Wald χ2 testing compared baseline demographic, hospital, and clinical characteristics and postpartum complications between WWN (including previous stroke, migraine, multiple sclerosis [MS], and myasthenia gravis [MG]) and controls. Multivariable logistic regression models examined odds of postpartum complications and nonelective readmissions within 30 and 90 days for each neurologic comorbidity compared to controls (α = 0.05). RESULTS: A total of 7,612 women with previous stroke, 83,430 women with migraine, 6,760 women with MS, 843 women with MG, and 8,136,335 controls met the criteria for index admission after viable infant delivery. WWN were more likely than controls to have inpatient diagnoses of edema, proteinuria, or hypertensive disorders and to have received maternal care for poor fetal growth. The adjusted odds of a Centers for Disease Control and Prevention severe maternal morbidity indicator were greater for women with previous stroke (adjusted odds ratio [AOR] 8.53, 95% CI 7.24-10.06), migraine (AOR 2.04, 95% CI 1.85-2.26), and MG (AOR 4.45, 95% CI 2.45-8.08) (all p < 0.0001). Readmission rates at 30 and 90 days for WWN were higher than for controls (30 days: previous stroke 2.9%, migraine 1.7%, MS 1.8%, MG 4.3%, controls 1.1%; 90 days: previous stroke 3.7%, migraine 2.5%, MS 5.1%, MG 6.0%, controls 1.6%). Women with MG had the highest adjusted odds of readmission (30 days: AOR 3.96, 95% CI 2.37-6.65, p < 0.0001; 90 days: AOR 3.30, 95% CI 1.88-5.78, p < 0.0001). DISCUSSION: WWN may be at higher risk of severe maternal morbidity at the time of index delivery and postpartum readmission. More real-world evidence is needed to develop research infrastructure and create efficacious interventions to optimize maternal-fetal outcomes in WWN, especially for women with previous stroke or MG.

Using Generalized Polyspike Train to Predict Drug-Resistant Idiopathic Generalized Epilepsy.

INTRODUCTION: The authors tested the hypothesis that the EEG feature generalized polyspike train (GPT) is associated with drug-resistant idiopathic generalized epilepsy (IGE). METHODS: The authors conducted a single-center case-control study of patients with IGE who had outpatient EEGs performed between 2016 and 2020. The authors classified patients as drug-resistant or drug-responsive based on clinical review and in a masked manner reviewed EEG data for the presence and timing of GPT (a burst of generalized rhythmic spikes lasting less than 1 second) and other EEG features. A relationship between GPT and drug resistance was tested before and after controlling for EEG duration. The EEG duration needed to observe GPT was also calculated. RESULTS: One hundred three patients were included (70% drug-responsive and 30% drug-resistant patients). Generalized polyspike train was more prevalent in drug-resistant IGE (odds ratio, 3.8; 95% confidence interval, 1.3-11.4; P = 0.02). This finding persisted when controlling for EEG duration both with stratification and with survival analysis. A median of 6.5 hours (interquartile range, 0.5-12.7 hours) of EEG recording was required to capture the first occurrence of GPT. CONCLUSIONS: The findings support the hypothesis that GPT is associated with drug-resistant IGE. Prolonged EEG recording is required to identify this feature. Thus, >24-hour EEG recording early in the evaluation of patients with IGE may facilitate prognostication.

A nationwide analysis of maternal morbidity and acute postpartum readmissions in women with epilepsy.

OBJECTIVE: To compare maternal delivery hospitalization characteristics and postpartum outcomes in women with epilepsy (WWE) versus women without common neurological comorbidities. METHODS: We performed a retrospective cohort analysis of index characterizations and short-term postpartum rehospitalizations after viable delivery within the 2015-2017 National Readmissions Database using International Classification of Diseases, Tenth Revision codes. Wald chi-squared testing compared baseline demographic, hospital and clinical characteristics and postpartum complications between WWE and controls. Multivariable logistic regression models examined odds of nonelective readmissions within 30 and 90 days for WWE compared to controls (alpha = 0.05). RESULTS: A total of 38,518 WWE and 8,136,335 controls had a qualifying index admission for delivery. Baseline differences were most pronounced in Medicare/Medicaid insurance (WWE: 58.2%, controls: 43%, p < 0.0001), alcohol/substance abuse (WWE: 8.3%, controls: 2.5%, p < 0.0001), psychotic disorders (WWE: 1.2%, controls 0.1%, p < 0.0001), and mood disorder (WWE: 15.5%, controls: 3.7%, p < 0.0001). At the time of delivery, WWE were more likely to have edema, proteinuria, and hypertensive disorders (WWE: 19%, controls: 12.9%, p < 0.0001); a history of recurrent pregnancy loss (WWE: 1%, controls: 0.4%, p < 0.0001); preterm labor (WWE: 7.3%, controls: 4.8%, p < 0.0001), or presence of any Center for Disease Control severe maternal morbidity indicator (WWE: 3.2%, controls: 0.6%, p < 0.0001; AOR 5.16, 95% CI 4.70-5.67, p < 0.0001). A higher proportion of WWE were readmitted within 30 days (WWE: 2.4%, controls: 1.1%) and 90 days (WWE: 3.7%, controls: 1.6%). After adjusting for covariates, the odds of postpartum nonelective readmissions within 30 days (AOR 1.86, 95% CI 1.66-2.08, p-value <0.0001) and 90 days (AOR 2.04, 95% CI 1.83-2.28, p-value <0.0001) were higher in WWE versus controls. INTERPRETATION: Women with epilepsy experienced critical obstetric complications and a higher risk of severe maternal morbidity indicators at the time of delivery. Although relatively low, nonelective short-term readmissions after delivery were higher in WWE than women without epilepsy or other common neurological comorbidities. Further research is needed to address multidisciplinary care inconsistencies, improve maternal outcomes, and provide evidence-based guidelines.

Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.

As automated data extraction and natural language processing (NLP) are rapidly evolving, improving healthcare delivery by harnessing large data is garnering great interest. Assessing antiepileptic drug (AED) efficacy and other epilepsy variables pertinent to healthcare delivery remain a critical barrier to improving patient care. In this systematic review, we examined automatic electronic health record (EHR) extraction methodologies pertinent to epilepsy. We also reviewed more generalizable NLP pipelines to extract other critical patient variables. Our review found varying reports of performance measures. Whereas automated data extraction pipelines are a crucial advancement, this review calls attention to standardizing NLP methodology and accuracy reporting for greater generalizability. Moreover, the use of crowdsourcing competitions to spur innovative NLP pipelines would further advance this field.

Inconsistent reporting of drug-drug interactions for hormonal contraception and antiepileptic drugs - Implications for reproductive health for women with epilepsy.

Drug compendia are the source of safety prescribing information. We assessed the reporting concordance of drug-drug interactions between hormonal contraception and antiepileptic drugs (AEDs) among eight leading international drug compendia. Antiepileptic drugs reported to interact with ≥1 form of hormonal contraception were reviewed. Scaled concordance was quantified using linearly weighted percent agreement (wPA). Differences in interaction severity rankings between hormonal contraception forms were evaluated using the Wilcoxon signed-rank test. There was high agreement among compendia for interactions of combined hormonal contraception interactions with AEDs (wPA = 0.82-0.84), especially potent enzyme-inducing AEDs (wPA = 0.89). However, concordance was reduced for AED interactions with progestin-only contraception (wPA = 0.69-0.81). Extreme interaction reporting discrepancies were found for less potent enzyme-inducing AEDs. The greatest variability in interaction reporting was observed for injectable and intrauterine contraception (wPA = 0.69 and 0.70, respectively), which are the only hormonal contraception options currently classified as not interacting with enzyme-inducing AEDs. Drug-drug interaction reporting variability can have major clinical implications and highlights critical knowledge gaps in the care of women with epilepsy of childbearing age. Further research on AED-contraceptive interactions is needed to standardize compendia reporting and enhance evidence-based clinical guidelines for women with epilepsy.

Fungal cauda equina lesion with delayed cord compression and treatment response.

This is a 24 year old man with profound chronic hydrocephalus found to have a cauda equina abscess composed of Candida albicans. Prior literature reveals a paucity of central nervous system candidiasis. In these previously reported cases, there was evidence of local invasion of surrounding structures; however, this case is a sentinel report of a fungal abscess without evidence of local structural invasion. The patient's course was complicated by clinical and radiographic worsening to cauda equina syndrome, requiring emergent surgical decompression, despite appropriate antifungal treatment. This case illustrates the diagnostic challenge of this rare entity and the need for close follow up with this patient population.