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Hypertension. 2002 Jan;39(1):105-10.

Development of predictive models for long-term cardiovascular risk associated with systolic and diastolic blood pressure.

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  • 1Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215-1204, USA. rglynn@rics.bwh.harvard.edu

Abstract

Most existing risk prediction models have not considered the joint contribution of systolic and diastolic blood pressure to cardiovascular risk, and some suggest that there are thresholds below which further reductions of blood pressure yield no additional benefit. We developed multivariate risk prediction models that quantify the risk associated with both systolic and diastolic blood pressure and that can be used to infer the benefits of antihypertensive therapy in populations. Two large clinical trial cohorts, the Physicians' Health Study, composed of 22 071 males (mean age, 53.2 years; median follow-up, 13.0 years), and the Women's Health Study, composed of 39 876 females (mean age, 53.8 years; median follow-up, 6.2 years), were used to develop gender-specific predictive models via Cox regression. End points included myocardial infarction, stroke, coronary artery bypass, angioplasty, and cardiovascular death. Risk reduction estimates were derived by computing reductions associated with incremental lowering of systolic and diastolic blood pressures. In both populations, lower levels of blood pressure predicted lower event rates, with no evidence of a plateau or a J-shaped curve. In males, both systolic and diastolic blood pressures were significantly associated with events (P<0.001), whereas in females, only systolic blood pressure (P<0.001) predicted outcome after multivariate adjustment. Correction for measurement error in blood pressure increased risk estimates by approximately 50%. Differences in systolic blood pressure yielded greater relative risk reductions than did differences in diastolic blood pressure in a combined population of males and females. These predictive models may be useful for risk estimation associated with hypertension in similar populations and may also be used to infer the benefits of antihypertensive therapy.

PMID:
11799087
[PubMed - indexed for MEDLINE]
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