Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans

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dc.contributor.authorMogueo, Amelieen_ZA
dc.contributor.authorEchouffo-Tcheugui, Justinen_ZA
dc.contributor.authorMatsha, Tandien_ZA
dc.contributor.authorErasmus, Rajiven_ZA
dc.contributor.authorKengne, Andreen_ZA
dc.date.accessioned2015-12-07T08:52:12Z
dc.date.available2015-12-07T08:52:12Z
dc.date.issued2015en_ZA
dc.description.abstractBACKGROUND: Chronic kidney disease (CKD) is a global challenge. Risk models to predict prevalent undiagnosed CKD have been published. However, none was developed or validated in an African population. We validated the Korean and Thai CKD prediction model in mixed-ancestry South Africans. METHODS: Discrimination and calibration were assessed overall and by major subgroups. CKD was defined as 'estimated glomerular filtration rate (eGFR) <60ml/min/1.73m 2 ' or 'any nephropathy'. eGFR was based on the 4-variable Modification of Diet in Renal Disease (MDRD) formula. RESULTS: In all 902 participants (mean age 55years) included, 259 (28.7%) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73-0.79) for 'eGFR <60ml/min/1.73m 2 ' and 0.81 (0.78-0.84) for 'any nephropathy' for the Korean model; corresponding values for the Thai model were 0.80 (0.77-0.83) and 0.77 (0.74-0.81). Discrimination was better in men, older and normal weight individuals. The model underestimated CKD risk by 10% to 13% for the Thai and 9% to 93% for the Korean model. Intercept adjustment significantly improved the calibration with an expected/observed risk of 'eGFR <60ml/min/1.73m 2 ' and 'any nephropathy' respectively of 0.98 (0.87-1.10) and 0.97 (0.86-1.09) for the Thai model; but resulted in an underestimation by 24% with the Korean model. Results were broadly similar for CKD derived from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CONCLUSION: Asian prevalent CKD risk models had acceptable performances in mixed-ancestry South Africans. This highlights the potential importance of using existing models for risk CKD screening in developing countries.en_ZA
dc.identifier.apacitationMogueo, A., Echouffo-Tcheugui, J., Matsha, T., Erasmus, R., & Kengne, A. (2015). Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans. <i>BMC Nephrology</i>, http://hdl.handle.net/11427/15652en_ZA
dc.identifier.chicagocitationMogueo, Amelie, Justin Echouffo-Tcheugui, Tandi Matsha, Rajiv Erasmus, and Andre Kengne "Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans." <i>BMC Nephrology</i> (2015) http://hdl.handle.net/11427/15652en_ZA
dc.identifier.citationMogueo, A., Echouffo-Tcheugui, J. B., Matsha, T. E., Erasmus, R. T., & Kengne, A. P. (2015). Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans. BMC nephrology, 16(1), 94.en_ZA
dc.identifier.ris TY - Journal Article AU - Mogueo, Amelie AU - Echouffo-Tcheugui, Justin AU - Matsha, Tandi AU - Erasmus, Rajiv AU - Kengne, Andre AB - BACKGROUND: Chronic kidney disease (CKD) is a global challenge. Risk models to predict prevalent undiagnosed CKD have been published. However, none was developed or validated in an African population. We validated the Korean and Thai CKD prediction model in mixed-ancestry South Africans. METHODS: Discrimination and calibration were assessed overall and by major subgroups. CKD was defined as 'estimated glomerular filtration rate (eGFR) <60ml/min/1.73m 2 ' or 'any nephropathy'. eGFR was based on the 4-variable Modification of Diet in Renal Disease (MDRD) formula. RESULTS: In all 902 participants (mean age 55years) included, 259 (28.7%) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73-0.79) for 'eGFR <60ml/min/1.73m 2 ' and 0.81 (0.78-0.84) for 'any nephropathy' for the Korean model; corresponding values for the Thai model were 0.80 (0.77-0.83) and 0.77 (0.74-0.81). Discrimination was better in men, older and normal weight individuals. The model underestimated CKD risk by 10% to 13% for the Thai and 9% to 93% for the Korean model. Intercept adjustment significantly improved the calibration with an expected/observed risk of 'eGFR <60ml/min/1.73m 2 ' and 'any nephropathy' respectively of 0.98 (0.87-1.10) and 0.97 (0.86-1.09) for the Thai model; but resulted in an underestimation by 24% with the Korean model. Results were broadly similar for CKD derived from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CONCLUSION: Asian prevalent CKD risk models had acceptable performances in mixed-ancestry South Africans. This highlights the potential importance of using existing models for risk CKD screening in developing countries. DA - 2015 DB - OpenUCT DO - 10.1186/s12882-015-0093-6 DP - University of Cape Town J1 - BMC Nephrology LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans TI - Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans UR - http://hdl.handle.net/11427/15652 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/15652
dc.identifier.urihttp://dx.doi.org/10.1186/s12882-015-0093-6
dc.identifier.vancouvercitationMogueo A, Echouffo-Tcheugui J, Matsha T, Erasmus R, Kengne A. Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans. BMC Nephrology. 2015; http://hdl.handle.net/11427/15652.en_ZA
dc.language.isoengen_ZA
dc.publisherBioMed Central Ltden_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 Licenseen_ZA
dc.rights.holder2015 Mogueo et al.en_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourceBMC Nephrologyen_ZA
dc.source.urihttp://www.biomedcentral.com/bmcnephrol/en_ZA
dc.subject.otherCKDen_ZA
dc.subject.otherDiscriminationen_ZA
dc.subject.otherCalibrationen_ZA
dc.subject.otherValidationen_ZA
dc.subject.otherPrediction modelen_ZA
dc.titleValidation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africansen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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