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.
In a regional UKB subpopulation (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.
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, and 3% death records only. While 42% of cases were coded as unspecified stroke type, review of EPRs enabled a pathologic 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.
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.