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Accuracy of identifying incident stroke cases from linked healthcare data in UK Biobank

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Abstract

Objective: In UK Biobank (UKB), a large population-based prospective study, cases of many diseases are ascertained through linkage to routinely collected, coded national health datasets. We assessed the accuracy of these for identifying incident strokes. Methods: In a regional UKB sub-population (n=17,249), we identified all participants with ≥1 code signifying a first stroke after recruitment (incident stroke-coded cases) in linked hospital admission, primary care or death record data. Stroke physicians reviewed their full electronic patient records (EPRs) and generated reference standard diagnoses. We evaluated the number and proportion of cases that were true positives (i.e. positive predictive value, PPV) for all codes combined and by code source and type. Results: Of 232 incident stroke-coded cases, 97% had EPR information available. Data sources were: 30% hospital admission only; 39% primary care only; 28% hospital and primary care; 3% death records only. While 42% of cases were coded as unspecified stroke type, review of EPRs enabled a pathological type to be assigned in >99%. PPVs (95% confidence intervals) were: 79% (73%-84%) for any stroke (89% for hospital admission codes, 80% for primary care codes) and 83% (74%-90%) for ischemic stroke. PPVs for small numbers of death record and hemorrhagic stroke codes were low but imprecise. Conclusions: Stroke and ischemic stroke cases in UKB can be ascertained through linked health datasets with sufficient accuracy for many research studies. Further work is needed to understand the accuracy of death record and hemorrhagic stroke codes and to develop scalable approaches for better identifying stroke types.

ID: 132580644