P1548Long-term past, current and usual systolic blood pressure and incident cardiovascular disease: risk prediction using large-scale, routinely recorded clinical data

Abstract

Background: The impact of long-term exposure to elevated systolic blood pressure (SBP) on future cardiovascular disease (CVD) in “real-world” settings, and its relevance to risk prediction, are less investigated. Purpose: To examine the risk of incident CVD in relation to long-term past, current, and usual SBP, and compare their predictive performance, using evidence from large-scale electronic health records (EHR). Methods: Using data extracted from UK primary care linked EHR, we applied a landmark cohort study design, by including patients aged 40 (N≈64,000), 50 (N≈80,000) and 60 (N≈67,000) years (y) at study entry who had recorded SBP and with no prior CVD or previous antihypertensive or lipid-lowering prescriptions at baseline. We estimated past SBP (mean, time-weighted mean, and variability recorded up to 10 years prior to baseline) and usual SBP (correcting current values for past time-dependent SBP variability). We used Cox regression to estimate hazard ratio (HR), and applied Bayesian analysis within a machine learning framework in developing and validating models. To evaluate predictive performance of the models, we used discrimination (area under the curve [AUC]) and calibration metrics. The outcome was incident CVD (first hospitalisation for or death from coronary heart disease or stroke/transient ischaemic attack). Analyses were conducted separately for each age cohort. Results: After a mean follow-up of 8 years, the numbers of patients who developed incident CVD were over 1000 (40y), 3000 (50y) and 5000 (60y). Higher past, current and usual SBP values were separately and independently associated with increased incident CVD risk. Per 20-mmHg rise in SBP, the HR (95% credible interval [CI]) for current SBP for ages 40, 50 and 60 years were 1.18 (1.08 to 1.26), 1.22 (1.18 to 1.30) and 1.22 (1.19 to 1.24); the corresponding HR were stronger in magnitude for past SBP (mean and time-weighted mean) and usual SBP (HR ranged from: 40y=1.31 to 1.41, 50y=1.39 to 1.45 and 60y=1.32 to 1.48). For each age cohort, the AUC (95% CI) for the model that included current SBP, sex, smoking, deprivation, diabetes and lipid profile in the validation sample were: 40y=0.739 (0.730 to 0.746), 50y=0.750 (0.716 to 0.810), and 60y=0.647 (0.642 to 0.658). Adding past SBP mean, time-weighted mean or variability to this model were associated with modest increases in the AUC and all models showed good calibration. Small improvements in the AUC were similarly observed when evaluating models separately for men and women within each age cohort. Conclusion: Using multiple SBP recordings from patients' EHR showed stronger associations with incident CVD than a single SBP measurement, but their addition to multivariate risk prediction models had negligible effects on model performance.