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. 2021 Sep;44(9):1221-1229.
doi: 10.1038/s41440-021-00673-7. Epub 2021 May 31.

Development of a risk prediction model for incident hypertension in Japanese individuals: the Hisayama Study

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Development of a risk prediction model for incident hypertension in Japanese individuals: the Hisayama Study

Emi Oishi et al. Hypertens Res. 2021 Sep.

Abstract

The identification of individuals at high risk of developing hypertension can be of great value to improve the efficiency of primary prevention strategies for hypertension. The objective of this study was to develop a risk prediction model for incident hypertension based on prospective longitudinal data from a general Japanese population. A total of 982 subjects aged 40-59 years without hypertension at baseline were followed up for 10 years (2002-12) for the incidence of hypertension. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg, diastolic blood pressure (DBP) ≥ 90 mmHg, or the use of antihypertensive agents. The risk prediction model was developed using a Cox proportional hazards model. A simple risk scoring system was also established based on the developed model. During the follow-up period (median 10 years, interquartile range 5-10 years), 302 subjects (120 men and 182 women) developed new-onset hypertension. The risk prediction model for hypertension consisted of age, sex, SBP, DBP, use of glucose-lowering agents, body mass index (BMI), parental history of hypertension, moderate-to-high alcohol intake, and the interaction between age and BMI. The developed model demonstrated good discrimination (Harrell's C statistic=0.812 [95% confidence interval, 0.791-0.834]; optimism-corrected C statistic based on 200 bootstrap samples=0.804) and calibration (Greenwood-Nam-D'Agostino χ2 statistic=12.2). This risk prediction model is a useful guide for estimating an individual's absolute risk for hypertension and could facilitate the management of Japanese individuals at high risk of developing hypertension in the future.

Keywords: Epidemiology; General population; Hypertension; Risk factors; Risk prediction model.

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