Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database

Trends in enzyme-inducing antiseizure medication use: A retrospective analysis among adults with epilepsy

BACKGROUND

Enzyme-inducing antiseizure medications (EIASMs) were associated with drug interactions and long-term adverse effects. Therefore, it was suggested that epilepsy treatment should be started with non-EIASMs, and in patients treated with EIASMs, replacement with non-EIASMs should be evaluated OBJECTIVE: To assess potent EIASM use among patients with epilepsy at their first visit in our epilepsy outpatient clinic.

METHODS

We retrospectively reviewed the computerized database and the medical records of all the patients who had their first visit in our outpatient epilepsy clinic during a 10-year period (2012-2021). Of 730 patients with ASM treated epilepsy, 243 (33%) were receiving potent EIASMs.

RESULTS

The annual potent EIASM use decreased from 35.1 % in 2012 to 11.8 % in 2021. Most of the patients who received potent EIASM had their first visit during 2012-2015 compared to the following years (56.8 % vs 43.2 %) (p = 0.0001). Patients with epilepsy receiving potent EIASMs were older (44.3 vs 34.7) (p = 0.0001), more likely men (60.9 % vs 47.2 %) (p = 0.001), with longer disease duration (13 vs 9.3 y) (p = 0.0001), higher rate of neuropsychiatric comorbidity (37 % vs 27.9 %) (p = 0.014), and were treated with more ASMs (1.6 vs 1.3) (p = 0.0001) compared to patients receiving non-EIASMs.

CONCLUSIONS

Potent EIASM use has been declining over the past decade. Additional efforts to further decrease EIASM use should be exerted among all patients with ASM-treated epilepsy, with emphasis on men with focal epilepsy and epilepsy duration > 10 years.

Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records

OBJECTIVES

Chart review as the current gold standard for phenotype evaluation cannot support observational research on electronic health records and claims data sources at scale. We aimed to evaluate the ability of structured data to support efficient and interpretable phenotype evaluation as an alternative to chart review.

MATERIALS AND METHODS

We developed Knowledge-Enhanced Electronic Profile Review (KEEPER) as a phenotype evaluation tool that extracts patient's structured data elements relevant to a phenotype and presents them in a standardized fashion following clinical reasoning principles. We evaluated its performance (interrater agreement, intermethod agreement, accuracy, and review time) compared to manual chart review for 4 conditions using randomized 2-period, 2-sequence crossover design.

RESULTS

Case ascertainment with KEEPER was twice as fast compared to manual chart review. 88.1% of the patients were classified concordantly using charts and KEEPER, but agreement varied depending on the condition. Missing data and differences in interpretation accounted for most of the discrepancies. Pairs of clinicians agreed in case ascertainment in 91.2% of the cases when using KEEPER compared to 76.3% when using charts. Patient classification aligned with the gold standard in 88.1% and 86.9% of the cases respectively.

CONCLUSION

Structured data can be used for efficient and interpretable phenotype evaluation if they are limited to relevant subset and organized according to the clinical reasoning principles. A system that implements these principles can achieve noninferior performance compared to chart review at a fraction of time.