EPI-VALID : Validation of an algorithm for identifying patients with epilepsy in the SNDS using data from the CONSTANCES cohort

TL;DR

This study validates algorithms for identifying epilepsy patients in the French SNDS database using data from the CONSTANCES cohort, highlighting limitations in accuracy and under-reporting issues.

Contribution

It introduces and evaluates specific algorithms for epilepsy identification in SNDS, validated against cohort data, and discusses their limitations.

Findings

Algorithms have low accuracy (17.8% and 35%) in identifying epilepsy.

Under-reporting in the cohort affects prevalence estimates.

Comparison reveals limitations in current identification methods.

Abstract

Introduction: The HAS conducted a study in 2018 using the French National Health Data System (SNDS) on the care pathway of patients with epilepsy. This study used 2 algorithms to identify patients with epilepsy, based on hospitalization for epilepsy, insurance for chronic severe epilepsy, and antiepileptic drug (AE) dispensing, with exclusion of AEs not specific to epilepsy offering a low population estimate, or exclusion of dispensing in a context other than epilepsy (migraine, neuropathic pain, bipolar disorder, alcohol dependence, anxiety) offering a high population estimate. To study the characteristics of these 2 algorithms (low and high populations), we used data from CONSTANCES, a French generalist cohort and its control sample, which are matched to SNDS data. Method: This is a validation study of a method for identifying patients with epilepsy in the SNDS. The study population was patients identified as epileptic according to the algorithms defined in the introduction, applied over the year 2018 and the reference population was all patients for whom there is a mention of epilepsy (free text field) in the inclusion and/or follow-up questionnaires of the CONSTANCES cohort for the years prior to 2019. Results: Among 156,819 patients present in the CONSTANCES cohort in 2018, 689 patients mentioned epilepsy in one of the questionnaires (0.4%). This number is lower than expected: in the literature, the prevalence of epilepsy in the general population is estimated at 0.6-0.7% in adults. This is due to under-reporting and lack of representativeness of epilepsy in the cohort, which we were able to estimate from the control sample data. Aware of these two limitations, we compared the characteristics of the algorithms with those of a control algorithm using insurance for chronic severe epilepsy and/or hospitalization for epilepsy as inclusion criteria in the SNDS. The accuracy of our SNDS algorithms is not very high (17.8% and 35% respectively in high and low populations) compared with the control algorithm (58.1%). However, they do improve sensitivity (+33 and +14 points respectively), with little loss of specificity (-1.2 and -0.3 points respectively). Discussion: The analysis of false-positive patients has enabled us to identify ways of improving the algorithms. Indeed, some AE drugs could be excluded from the inclusion criteria, as they are widely used in another indication. For example, eliminating pregabalin and gabapentin from the high-population algorithm would significantly improve its accuracy (45.3% vs. 17.8%).