Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

• Published in: PloS one Vol. 14; no. 2; p. e0211528
• Main Authors: Masconi, Katya L, Matsha, Tandi E, Erasmus, Rajiv T, Kengne, Andre P
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.02.2019; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Biology and Life Sciences; Body mass; Body mass index; Body size; Calibration; Clinical medicine; Datasets; Diabetes; Diabetes mellitus; Diabetes Mellitus - epidemiology; Diagnosis; Discrimination; Family medical history; Female; Genealogy; Genetics; Glucose; Glucose tolerance; Glucose tolerance test; Health risk assessment; Health risks; Health sciences; Humans; Laboratories; Likelihood ratio; Male; Mathematical models; Medical research; Medical screening; Medicine and Health Sciences; Metabolism; Methods; Middle Aged; Model testing; Model updating; Models, Statistical; Physical Sciences; Prediction models; Prevalence; Regression analysis; Regression coefficients; Research and Analysis Methods; Risk; Risk Assessment; South Africa - epidemiology; Statistical analysis; Studies; Systematic review; South Africa
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0211528

Prediction model updating methods are aimed at improving the prediction performance of a model in a new setting. This study sought to critically assess the impact of updating techniques when applying existent prevalent diabetes prediction models to a population different to the one in which they were developed, evaluating the performance in the mixed-ancestry population of South Africa. The study sample consisted of 1256 mixed-ancestry individuals from the Cape Town Bellville-South cohort, of which 173 were excluded due to previously diagnosed diabetes and 162 individuals had undiagnosed diabetes. The primary outcome, undiagnosed diabetes, was based on an oral glucose tolerance test. Model updating techniques and prediction models were identified via recent systematic reviews. Model performance was assessed using the C-statistic and expected/observed (E/O) events rates ratio. Intercept adjustment and logistic calibration improved calibration across all five models (Cambridge, Kuwaiti, Omani, Rotterdam and Simplified Finnish diabetes risk models). This was improved further by model revision, where likelihood ratio tests showed that the effect of body mass index, waist circumference and family history of diabetes required additional adjustment (Omani, Rotterdam and Finnish models). However, discrimination was poor following internal validation of these models. Re-estimation of the regression coefficients did not increase performance, while the addition of new variables resulted in the highest discriminatory and calibration performance combination for the models it was undertaken in. While the discriminatory performance of the original existent models during external validation were higher, calibration was poor. The highest performing models, based on discrimination and calibration, were the Omani diabetes model following model revision, and the Cambridge diabetes risk model following the addition of waist circumference as a predictor. However, while more extensive methods incorporating development population information were superior over simpler methods, the increase in model performance was not great enough for recommendation.