{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:54:59Z","timestamp":1787018099618,"version":"build-2736575974"},"reference-count":59,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"12","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2019,6,18]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">How measures of long\u2010term exposure to elevated blood pressure might add to the performance of \u201ccurrent\u201d blood pressure in predicting future cardiovascular disease is unclear. We compared incident cardiovascular disease risk prediction using past, current, and usual systolic blood pressure alone or in combination.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods and Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      Using data from\n                      <jats:styled-content style=\"fixed-case\">UK<\/jats:styled-content>\n                      primary care linked electronic health records, we applied a landmark cohort study design and identified 80\u00a0964 people, aged 50 years (derivation cohort=64\u00a0772; validation cohort=16\u00a0192), who, at study entry, had recorded blood pressure, no prior cardiovascular disease, and no previous antihypertensive or lipid\u2010lowering prescriptions. We used systolic blood pressure recorded up to 10\u00a0years before baseline to estimate past systolic blood pressure (mean, time\u2010weighted mean, and variability) and usual systolic blood pressure (correcting current values for past time\u2010dependent blood pressure fluctuations) and examined their prospective relation with incident cardiovascular disease (first hospitalization for or death from coronary heart disease or stroke\/transient ischemic attack). We used Cox regression to estimate hazard ratios and applied Bayesian analysis within a machine learning framework in model development and validation. Predictive performance of models was assessed using discrimination (area under the receiver operating characteristic curve) and calibration metrics. We found that elevated past, current, and usual systolic blood pressure values were separately and independently associated with increased incident cardiovascular disease risk. When used alone, the hazard ratio (95% credible interval) per 20\u2013mm\u00a0Hg increase in current systolic blood pressure was 1.22 (1.18\u20131.30), but associations were stronger for past systolic blood pressure (mean and time\u2010weighted mean) and usual systolic blood pressure (hazard ratio ranging from 1.39\u20131.45). The area under the receiver operating characteristic curve for a model that included current systolic blood pressure, sex, smoking, deprivation, diabetes mellitus, and lipid profile was 0.747 (95% credible interval, 0.722\u20130.811). The addition of past systolic blood pressure mean, time\u2010weighted mean, or variability to this model increased the area under the receiver operating characteristic curve (95% credible interval) to 0.750 (0.727\u20130.811), 0.750 (0.726\u20130.811), and 0.748 (0.723\u20130.811), respectively, with all models showing good calibration. Similar small improvements in area under the receiver operating characteristic curve were observed when testing models on the validation cohort, in sex\u2010stratified analyses, or by using different landmark ages (40 or 60\u00a0years).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Using multiple blood pressure recordings from patients\u2019 electronic health records showed stronger associations with incident cardiovascular disease than a single blood pressure measurement, but their addition to multivariate risk prediction models had negligible effects on model performance.<\/jats:p>\n                  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