013 Validation of an algorithm identifying epilepsy related hospital admissions from routinely collected administrative data. Issue 6 (27th May 2022) - Mitchell, James; Zammit, Louise; Kallis, Constantinos; Dixon, Pete; Grainger, Ruth; Powell, Graham; Marson, Tony
Abstract : Routinely collected administrative datasets can be used to identify healthcare resource use and inform strategic planning and policy development. We evaluate a new comprehensive epilepsy admission algorithm using ICD-10 coding data from these administrative datasets against a gold standard and existing admission identification algorithms. All consecutive admissions to a large UK general hospital meeting predefined criteria were identified over a one-month period. Case note review was performed by independent clinicians and admission classi- fied as epilepsy or not epilepsy related. The Liverpool algorithm and a widely used comparator algorithm were applied to the ICD-10 administrative data and compared to clinician classification. In total 205 admissions were identified. Case-note review (gold standard) identified that 35 cases were epilepsy related admissions, and 156 were not epilepsy related. It was not possible to classify 14 admis- sions due to missing or insufficient data. The Liverpool algorithm incorrectly classified only 17 out of 191 admissions compared to 30 incorrectly classified admissions from a comparator algorithm. The Liverpool algorithm demonstrates sensitivity of 85.7% and specificity of 92.3%. Whilst no perfect algorithm is likely to exist, the Liverpool algorithm identifies hospital admissions due to epileptic seizures using administrative data with improved sensitivity and specificity compared to previous methods. james.mitchell@liverpool.ac.uk