137 Accuracy of using administrative data to identify epilepsy cases: a systematic review of validation studies. Issue 6 (27th May 2022) - Mbizvo, Gashirai K; Bennett, Kyle; Schnier, Christian; Simpson, Colin R; Duncan, Susan E; Chin, Richard FM
Abstract : Purpose: To ascertain the accuracy of using administrative healthcare data to identify epilepsy cases. Method: We searched MEDLINE and Embase from 01/01/1975–03/07/2018 for studies evaluating the diagnostic accuracy of administrative data in identifying epilepsy cases using any disease coding system. Two authors independently screened studies, extracted data, and quality-assessed studies. We assessed PPV, sensitivity, NPV, and specificity. The primary analysis was narrative. Results: Thirty studies were included between 1989–2018. Risks of bias were low, high, and unclear in four, 14, and 12 studies, respectively. Coding systems included ICD-9, ICD-10 and Read Codes, with or without antiepileptic drugs (AEDs). PPVs included ranges of 5.2–100% (Canada), 32.7–96.0% (US), 47.0–100% (UK), and 37.0–88.0% (Norway). Sensitivities included ranges of 22.2–99.7% (Canada), 12.2–97.3% (US), and 79.0–94.0% (UK). Nineteen studies contained ≥1 algorithm with a PPV >80%. Sixteen studies contained ≥1 algorithm with a sensitivity >80%. PPV was highest in algorithms consisting of disease codes (ICD-10 G40–41, ICD-9 345) in combination with ≥1 AED. The addition of symptom codes to this (ICD-10 R56, ICD-9 780.3, 780.39) lowered PPV. Sensitivity was highest in algorithms consisting of symptom codes with ≥1 AED. Whilst using AEDs alone achieved high sensitivities, the associated PPVs were low. Most NPVs and specificities were >90%. Conclusion: In the first global systematic review of this topic, we conclude that it is reasonable to use administrative data to identify people with epilepsy in epidemiological research. mbizvogkm@gmail.com