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dc.contributor.authorMogueo, Amelieen_US
dc.contributor.authorEchouffo-Tcheugui, Justin Basileen_US
dc.contributor.authorMatsha, Tandi Edithen_US
dc.contributor.authorErasmus, Rajiv Timothyen_US
dc.contributor.authorKengne, Andre Pascalen_US
dc.identifier.citationMogueo, A., Echouffo-Tcheugui, J. B., Matsha, T. E. et al. 2015. Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans. BMC Nephrology, 16(94). []en_US
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) <60 ml/min/1.73 m2’ 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 55 years) included, 259 (28.7 %) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73–0.79) for ‘eGFR <60 ml/min/1.73 m2’ 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 <60 ml/min/1.73 m2’ 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_US
dc.publisherBioMed Centralen_US
dc.relation.ispartofBMC Nephrologyen_US
dc.subjectPrediction modelen_US
dc.titleValidation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africansen_US
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