Risk models to predict hypertension: a systematic review

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dc.contributor.authorEchouffo-Tcheugui, Justin Ben_ZA
dc.contributor.authorBatty, G Daviden_ZA
dc.contributor.authorKivimäki, Mikaen_ZA
dc.contributor.authorKengne, Andre Pen_ZA
dc.date.accessioned2016-01-11T06:56:13Z
dc.date.available2016-01-11T06:56:13Z
dc.date.issued2013en_ZA
dc.description.abstractBACKGROUND: 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. Methods and FINDINGS: 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.en_ZA
dc.identifier.apacitationEchouffo-Tcheugui, J. B., Batty, G. D., Kivimäki, M., & Kengne, A. P. (2013). Risk models to predict hypertension: a systematic review. <i>PLoS One</i>, http://hdl.handle.net/11427/16309en_ZA
dc.identifier.chicagocitationEchouffo-Tcheugui, Justin B, G David Batty, Mika Kivimäki, and Andre P Kengne "Risk models to predict hypertension: a systematic review." <i>PLoS One</i> (2013) http://hdl.handle.net/11427/16309en_ZA
dc.identifier.citationEchouffo-Tcheugui, J. B., Batty, G. D., Kivimäki, M., & Kengne, A. P. (2013). Risk models to predict hypertension: a systematic review. PloS one, 8(7), e67370. doi:10.1371/journal.pone.0067370en_ZA
dc.identifier.ris TY - Journal Article AU - Echouffo-Tcheugui, Justin B AU - Batty, G David AU - Kivimäki, Mika AU - Kengne, Andre P AB - 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. Methods and FINDINGS: 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. DA - 2013 DB - OpenUCT DO - 10.1371/journal.pone.0067370 DP - University of Cape Town J1 - PLoS One LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - Risk models to predict hypertension: a systematic review TI - Risk models to predict hypertension: a systematic review UR - http://hdl.handle.net/11427/16309 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/16309
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0067370
dc.identifier.vancouvercitationEchouffo-Tcheugui JB, Batty GD, Kivimäki M, Kengne AP. Risk models to predict hypertension: a systematic review. PLoS One. 2013; http://hdl.handle.net/11427/16309.en_ZA
dc.language.isoengen_ZA
dc.publisherPublic Library of Scienceen_ZA
dc.publisher.departmentDepartment of Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_ZA
dc.rights.holder© 2013 Echouffo-Tcheugui et alen_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourcePLoS Oneen_ZA
dc.source.urihttp://journals.plos.org/plosoneen_ZA
dc.subject.otherHypertensionen_ZA
dc.subject.otherBlood pressureen_ZA
dc.subject.otherForecastingen_ZA
dc.subject.otherCardiovascular diseasesen_ZA
dc.subject.otherSmoking habitsen_ZA
dc.subject.otherEthnic epidemiologyen_ZA
dc.subject.otherBody mass indexen_ZA
dc.titleRisk models to predict hypertension: a systematic reviewen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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