Risk models to predict chronic kidney disease and its progression: a systematic review.

<h4>Background</h4>Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence a...

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Autores principales:	Justin B Echouffo-Tcheugui, Andre P Kengne
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R
Acceso en línea:	https://doaj.org/article/7b9b63f6ef7c40f582e1844502788a07
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spelling	oai:doaj.org-article:7b9b63f6ef7c40f582e1844502788a072021-11-18T05:42:48ZRisk models to predict chronic kidney disease and its progression: a systematic review.1549-12771549-167610.1371/journal.pmed.1001344https://doaj.org/article/7b9b63f6ef7c40f582e1844502788a072012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23185136/?tool=EBIhttps://doaj.org/toc/1549-1277https://doaj.org/toc/1549-1676<h4>Background</h4>Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.<h4>Methods and findings</h4>We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.<h4>Conclusions</h4>The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored.Justin B Echouffo-TcheuguiAndre P KengnePublic Library of Science (PLoS)articleMedicineRENPLoS Medicine, Vol 9, Iss 11, p e1001344 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R
spellingShingle	Medicine R Justin B Echouffo-Tcheugui Andre P Kengne Risk models to predict chronic kidney disease and its progression: a systematic review.
description	<h4>Background</h4>Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.<h4>Methods and findings</h4>We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.<h4>Conclusions</h4>The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored.
format	article
author	Justin B Echouffo-Tcheugui Andre P Kengne
author_facet	Justin B Echouffo-Tcheugui Andre P Kengne
author_sort	Justin B Echouffo-Tcheugui
title	Risk models to predict chronic kidney disease and its progression: a systematic review.
title_short	Risk models to predict chronic kidney disease and its progression: a systematic review.
title_full	Risk models to predict chronic kidney disease and its progression: a systematic review.
title_fullStr	Risk models to predict chronic kidney disease and its progression: a systematic review.
title_full_unstemmed	Risk models to predict chronic kidney disease and its progression: a systematic review.
title_sort	risk models to predict chronic kidney disease and its progression: a systematic review.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/7b9b63f6ef7c40f582e1844502788a07
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Risk models to predict chronic kidney disease and its progression: a systematic review.

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