Automated identification and quality measurement for pediatric convulsive status epilepticus. (20th December 2020) - Hess‐Homeier, David L.; Parikh, Karishma; Basma, Natasha; Vella, Adam E.; Grinspan, Zachary M.
Abstract: Objective: Treatment delays for refractory convulsive status epilepticus (RCSE) are associated with worse outcomes. In the United States, treatment for pediatric RCSE is slower than guidelines recommend. To address this gap, the American Academy of Neurology and Child Neurology Society (AAN/CNS) developed a quality measure: the percentage of RCSE patients that receive third‐line treatment within 60 minutes. We aimed to develop computable phenotypes for convulsive status epilepticus (CSE) and RCSE to automate calculation of the quality measure. Methods: From an observational cohort of children presenting to the emergency department for seizures or epilepsy, we identified presentations of RCSE and its precursors: CSE and benzodiazepine‐resistant status epilepticus (BRSE). These served as a gold standard for computable phenotype development. Using multivariate analyses, we constructed and evaluated statistical models for case identification. We then evaluated adherence to the AAN/CNS RCSE quality measure. Results: From 664 charts, we identified 56 patients with CSE, 36 with BRSE, and 18 with RCSE. Four predictors were used: International Classification of Diseases (ICD) codes, and receiving first‐, second‐, or third‐line agents shortly after presentation to the emergency department (ED). Combinations of these predictors identified CSE with 84% sensitivity and 81% positive predictive value (PPV), BRSE with 67% sensitivity and 89% PPV, and RCSE with 94% sensitivity and 85% PPV. Median (interquartile range [IQR]) time to treatment for first‐line agent was 13 (5‐27) minutes for CSE, second‐line for BRSE was 24 (9.5‐43.5) minutes, and third‐line for RCSE was 52 (27‐87) minutes. Sixty percent of RCSE patients received a third‐line agent within 60 minutes of ED arrival. Significance: RCSE and its precursors can be identified automatically with high fidelity allowing automated calculation of time to treatment and the RCSE quality measure. This has the potential to facilitate quality improvement work and improve care for RCSE.