title: Risk models to predict hypertension: a systematic review.
abstract: BACKGROUND
  As well as being a risk factor for cardiovascular disease, hypertension is also a health condition in its own right. Risk prediction models may be of value in identifying those individuals at risk of developing hypertension who are likely to benefit most from interventions.
  
  
  RESULTS
  To synthesize existing evidence on the performance of these models, we searched MEDLINE and EMBASE; examined bibliographies of retrieved articles; contacted experts in the field; and searched our own files. Dual review of identified studies was conducted. Included studies had to report on the development, validation, or impact analysis of a hypertension risk prediction model. For each publication, information was extracted on study design and characteristics, predictors, model discrimination, calibration and reclassification ability, validation and impact analysis. Eleven studies reporting on 15 different hypertension prediction risk models were identified. Age, sex, body mass index, diabetes status, and blood pressure variables were the most common predictor variables included in models. Most risk models had acceptable-to-good discriminatory ability (C-statistic>0.70) in the derivation sample. Calibration was less commonly assessed, but overall acceptable. Two hypertension risk models, the Framingham and Hopkins, have been externally validated, displaying acceptable-to-good discrimination, and C-statistic ranging from 0.71 to 0.81. Lack of individual-level data precluded analyses of the risk models in subgroups.
  
  
  CONCLUSIONS
  The discrimination ability of existing hypertension risk prediction tools is acceptable, but the impact of using these tools on prescriptions and outcomes of hypertension prevention is unclear.
authors.affiliation: Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
authors.forename: Justin B
authors.initials: JB
authors.lastname: Echouffo-Tcheugui
authors.forename: G David
authors.initials: GD
authors.lastname: Batty
authors.forename: Mika
authors.initials: M
authors.lastname: Kivimäki
authors.forename: Andre P
authors.initials: AP
authors.lastname: Kengne
mesh_terms: D000465:Algorithms; D006801:Humans; D006973:Hypertension / Q000175:diagnosis*; D015233:Models, Statistical; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors