Examining the impact of data quality and completeness of electronic health records on predictions of patients’ risks of cardiovascular disease

Abstract

ObjectiveTo assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3). DesignLongitudinal cohort study. Settings392 general practices (including 3.6 million patients) linked to hospital admission data. MethodsVariation in data quality was assessed using Sáez’s stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices. ResultsThere was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%–16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients. ConclusionsThe considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.