Accuracy of Electronic Health Record Data for Identifying Stroke Cases in Large-Scale Epidemiological Studies: A Systematic Review from the UK Biobank Stroke Outcomes Group

Abstract

ObjectiveLong-term follow-up of population-based prospective studies is often achieved through linkages to coded regional or national health care data. Our knowledge of the accuracy of such data is incomplete. To inform methods for identifying stroke cases in UK Biobank (a prospective study of 503,000 UK adults recruited in middle-age), we systematically evaluated the accuracy of these data for stroke and its main pathological types (ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage), determining the optimum codes for case identification.MethodsWe sought studies published from 1990-November 2013, which compared coded data from death certificates, hospital admissions or primary care with a reference standard for stroke or its pathological types. We extracted information on a range of study characteristics and assessed study quality with the Quality Assessment of Diagnostic Studies tool (QUADAS-2). To assess accuracy, we extracted data on positive predictive values (PPV) and—where available—on sensitivity, specificity, and negative predictive values (NPV).Results37 of 39 eligible studies assessed accuracy of International Classification of Diseases (ICD)-coded hospital or death certificate data. They varied widely in their settings, methods, reporting, quality, and in the choice and accuracy of codes. Although PPVs for stroke and its pathological types ranged from 6–97%, appropriately selected, stroke-specific codes (rather than broad cerebrovascular codes) consistently produced PPVs >70%, and in several studies >90%. The few studies with data on sensitivity, specificity and NPV showed higher sensitivity of hospital versus death certificate data for stroke, with specificity and NPV consistently >96%. Few studies assessed either primary care data or combinations of data sources.ConclusionsParticular stroke-specific codes can yield high PPVs (>90%) for stroke/stroke types. Inclusion of primary care data and combining data sources should improve accuracy in large epidemiological studies, but there is limited published information about these strategies.

Decreasing incidence of subarachnoid hemorrhage

“…Inaccurate coding is always a possibility, although previous studies have demonstrated the accuracy of ICD coding in capturing data on subarachnoid hemorrhage 12. The data is retrospective, but while an ‘imperfect tool’, other studies have previously documented the accuracy of large-scale epidemiological databases for research purposes 13. Several previous studies have used this database to capture population-level epidemiological data in this fashion 14 15.…”

The incidence of first stroke in and around pregnancy: A population-based cohort study from Sweden

“…23 Unfortunately, we did not have access to primary care data. In addition, despite the markedly increased risk of stroke around delivery, most strokes in these young women were unrelated to pregnancy.…”

Differentiating Incident from Recurrent Stroke Using Administrative Data: The Impact of Varying Lengths of Look-Back Periods on the Risk of Misclassification

“…Where the first recorded stroke diagnosis in each admission was ICD-10 code I64, “stroke, not otherwise specified as haemorrhage or infarction,” subsequent episodes of care recording a specific stroke diagnosis were interrogated to inform the sub-type; otherwise, I64 coded strokes were considered ischaemic strokes, as evidence in Australia indicates that these cases rarely represent haemorrhagic cerebrovascular events (1%) or transient ischaemic attacks (2%) [20,21]. This approach is recommended in 2 recent systematic reviews of stroke coding validity to increase the sensitivity of ischaemic stroke sub-typing without compromising the PPV [22,23]. …”