Epilepsy is a common, and often genetic, neurological disorder. Few guidelines exist to help medical providers or insurance companies decide when to order or cover epilepsy panels for patients with epilepsy. The most recent guidelines were published by NSGC after this study's data collection. Since 2017, the Genetic Testing Stewardship Program (GTSP) at UPMC Children's Hospital of Pittsburgh (CHP) has been utilizing a set of internally developed epilepsy panel (EP) testing criteria to facilitate appropriate EP ordering practices. The purpose of this study was to assess these testing criteria by determining their sensitivities and positive predictive values (PPV). Retrospective chart review of the electronic medical record (EMR) was performed for 1242 CHP Neurology patients that were evaluated for a primary diagnosis of epilepsy between 2016 and 2018. One hundred and nine patients had EPs at various testing laboratories. Of the patients that met criteria, 17 had diagnostic EPs and 54 had negative EPs. Criteria were organized into category groupings (C1-C4), and analyzed alone for C1, in pairs for C2, etc. The highest sensitivity and PPV results in each category grouping were: C1 (64.7%, 60%); C2, (88%, 30.3%); C3, (94.1%, 27.1%); C4, (94.1%, 25.4%). Family history was crucial to increasing sensitivity. Confidence intervals (CIs) narrowed as category grouping level increased, though this was not statistically significant due to the considerable CI overlap across category groupings. The PPV from C4 was applied to the untested population cohort and predicted 121 patients with unidentified positive EPs. This study presents data supporting the predictive capabilities of EP testing criteria and suggests the addition of a family history criterion. This study impacts public health by encouraging the adoption of evidence-driven insurance policies and by suggesting guidelines to ease EP ordering and coverage decisions, which could potentially improve patient access to EP testing.
Keywords: chart review; genetic testing; policy; predictive; public health.
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