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Accuracy of identifying incident stroke cases from linked health care data in UK Biobank
Kristiina Rannikmäe1, Kenneth Ngoh2, Kathryn Bush2
1From the Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics (K.R., K.B., R.F., A.H., J.N., C.S., T.W., K.W., Q.Z., C.L.M.S.), and Centre for Clinical Brain Sciences (R.A.-S.S., F.D., N.S., W.W., R.W.), University of Edinburgh; UK Biobank (K.R., K.B., R.F., A.H., J.N., C.S., T.W., K.W., R.W., Q.Z., N.A., C.L.M.S.), Stockport; University of Edinburgh Medical School (K.N., D.E.H., S.O.); and Nuffield Department of Population Health (N.A.), University of Oxford, UK. kristiina.rannikmae@ed.ac.uk.
Insights
UK Biobank (UKB) linked health data accurately identify stroke cases for research. Hospital and primary care codes show high positive predictive value for stroke and ischemic stroke identification.
Area of Science:
- Epidemiology
- Public Health
- Medical Informatics
Background:
- The UK Biobank (UKB) is a large prospective study utilizing linked national health datasets for disease ascertainment.
- Accurate identification of incident strokes is crucial for epidemiological research and understanding disease burden.
Purpose of the Study:
- To assess the accuracy of linked national health datasets for identifying incident stroke cases within the UK Biobank.
- To determine the positive predictive value (PPV) of various data sources and code types for stroke ascertainment.
Main Methods:
- A subpopulation of 17,249 UK Biobank participants with stroke codes in hospital admission, primary care, or death records were identified.
- Stroke physicians reviewed electronic patient records (EPRs) to establish reference standard diagnoses.
- The positive predictive value (PPV) was calculated for all codes combined and stratified by data source and stroke type.
Main Results:
- Of 232 incident stroke-coded cases, 97% had available EPRs for review.
- The overall PPV for any stroke was 79%, with higher PPVs for hospital admission (89%) and primary care codes (80%).
- The PPV for ischemic stroke was 83%, and stroke type could be assigned in >99% of cases after EPR review.
Conclusions:
- Linked health datasets in UK Biobank provide sufficiently accurate ascertainment of stroke and ischemic stroke cases for many research purposes.
- Further research is needed to improve the accuracy of death record and hemorrhagic stroke codes and to develop scalable methods for detailed stroke type identification.
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 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.
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, 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.
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.
