Recent development of risk-prediction models for incident hypertension: An updated systematic review

Dongdong Sun, Jielin Liu, Lei Xiao, Ya Liu, Zuoguang Wang, Chuang Li, Yongxin Jin, Qiong Zhao and Shaojun Wen

PLOS ONE, 2017, vol. 12, issue 10, 1-19

Abstract: Background: Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative. Methods: Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc. Results: From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. Conclusions: The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0187240

DOI: 10.1371/journal.pone.0187240

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