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

Abstract

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.