  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% 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. 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.
