Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

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dc.contributor.authorMasconi, Katya Len_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: Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans. METHODS: Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment. RESULTS: Seven hundred thirty-seven participants (27% male), mean age, 52.2years, were included, among whom 130 (17.6%) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95% confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment. CONCLUSIONS: The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings.en_ZA
dc.identifier.apacitationMasconi, K. L., Matsha, T., Erasmus, R., & Kengne, A. (2015). Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa. <i>Diabetology & Metabolic Syndrome</i>, http://hdl.handle.net/11427/15653en_ZA
dc.identifier.chicagocitationMasconi, Katya L, Tandi Matsha, Rajiv Erasmus, and Andre Kengne "Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa." <i>Diabetology & Metabolic Syndrome</i> (2015) http://hdl.handle.net/11427/15653en_ZA
dc.identifier.citationMasconi, K., Matsha, T.E., Erasmus, R.T., & Kengne, A.P. (2015).Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa. Diabetology & Metabolic Syndrome, 7(1), 42.en_ZA
dc.identifier.ris TY - Journal Article AU - Masconi, Katya L AU - Matsha, Tandi AU - Erasmus, Rajiv AU - Kengne, Andre AB - BACKGROUND: Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans. METHODS: Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment. RESULTS: Seven hundred thirty-seven participants (27% male), mean age, 52.2years, were included, among whom 130 (17.6%) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95% confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment. CONCLUSIONS: The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings. DA - 2015 DB - OpenUCT DO - 10.1186/s13098-015-0039-y DP - University of Cape Town J1 - Diabetology & Metabolic Syndrome LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa TI - Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa UR - http://hdl.handle.net/11427/15653 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/15653
dc.identifier.urihttp://dx.doi.org/10.1186/s13098-015-0039-y
dc.identifier.vancouvercitationMasconi KL, Matsha T, Erasmus R, Kengne A. Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa. Diabetology & Metabolic Syndrome. 2015; http://hdl.handle.net/11427/15653.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 Masconi et al.; licensee BioMed Central.en_ZA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_ZA
dc.sourceDiabetology & Metabolic Syndromeen_ZA
dc.source.urihttp://www.dmsjournal.com/en_ZA
dc.subject.othertype 2 diabetesen_ZA
dc.subject.othermixed-ancestry South Africansen_ZA
dc.titleIndependent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africaen_ZA
dc.typeJournal Articleen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceArticleen_ZA
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