Examining the impact of data quality and completeness of electronic health records on predictions of patients risks of cardiovascular disease
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The objective is to 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). The study design is a longitudinal cohort study with a setting of 392 general practices (including 3.6 million patients) linked to hospital admission data. Variation in data quality was assessed using Saez 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. There 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 female was in a range between 7.1 variability into the statistical frailty models; for the highest quintile, this was 10.9 comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 and 62.1 respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients. In conclusion, the 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.
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