Performance of a prediabetes risk prediction model: A systematic review

BackgroundsThe prediabetes population is large and easily overlooked because of the lack of obvious symptoms, which can progress to diabetes. Early screening and targeted interventions can substantially reduce the rate of conversion of prediabetes to diabetes. Therefore, this study systematically reviewed prediabetes risk prediction models, performed a summary and quality evaluation, and aimed to recommend the optimal model.MethodsWe systematically searched five databases (Cochrane, PubMed, Embase, Web Of Science, and CNKI) for published literature related to prediabetes risk prediction models and excluded preprints, duplicate publications, reviews, editorials, and other studies, with a search time frame of March 01, 2023. Data were categorized and summarized using a standardized data extraction form that extracted data including author; publication date; study design; country; demographic characteristics; assessment tool name; sample size; study type; and model-related indicators. The PROBAST tool was used to assess the risk of bias profile of included studies.Findings14 studies with a total of 15 models were eventually included in the systematic review. We found that the most common predictors of models were age, family history of diabetes, gender, history of hypertension, and BMI. Most of the studies (83.3%) had a high risk of bias, mainly related to under-reporting of outcome information and poor methodological design during the development and validation of models. Due to the low quality of included studies, the evidence for predictive validity of the available models is unclear.InterpretationWe should pay attention to the early screening of prediabetes patients and give timely pharmacological and lifestyle interventions. The predictive performance of the existing model is not satisfactory, and the model building process can be standardized and external validation can be added to improve the accuracy of the model in the future.