Risk models to predict hypertension: a systematic review.

<h4>Background</h4>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 intervent...

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Autores principales:	Justin B Echouffo-Tcheugui, G David Batty, Mika Kivimäki, Andre P Kengne
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/768f5fe6054044138f484d01d53b0247
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spelling	oai:doaj.org-article:768f5fe6054044138f484d01d53b02472021-11-18T07:38:27ZRisk models to predict hypertension: a systematic review.1932-620310.1371/journal.pone.0067370https://doaj.org/article/768f5fe6054044138f484d01d53b02472013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23861760/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods and findings</h4>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.<h4>Conclusions</h4>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.Justin B Echouffo-TcheuguiG David BattyMika KivimäkiAndre P KengnePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e67370 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Justin B Echouffo-Tcheugui G David Batty Mika Kivimäki Andre P Kengne Risk models to predict hypertension: a systematic review.
description	<h4>Background</h4>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.<h4>Methods and findings</h4>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.<h4>Conclusions</h4>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.
format	article
author	Justin B Echouffo-Tcheugui G David Batty Mika Kivimäki Andre P Kengne
author_facet	Justin B Echouffo-Tcheugui G David Batty Mika Kivimäki Andre P Kengne
author_sort	Justin B Echouffo-Tcheugui
title	Risk models to predict hypertension: a systematic review.
title_short	Risk models to predict hypertension: a systematic review.
title_full	Risk models to predict hypertension: a systematic review.
title_fullStr	Risk models to predict hypertension: a systematic review.
title_full_unstemmed	Risk models to predict hypertension: a systematic review.
title_sort	risk models to predict hypertension: a systematic review.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/768f5fe6054044138f484d01d53b0247
work_keys_str_mv	AT justinbechouffotcheugui riskmodelstopredicthypertensionasystematicreview AT gdavidbatty riskmodelstopredicthypertensionasystematicreview AT mikakivimaki riskmodelstopredicthypertensionasystematicreview AT andrepkengne riskmodelstopredicthypertensionasystematicreview
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Risk models to predict hypertension: a systematic review.

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