Department of Diabetes, Endocrinology, Nutritional Medicine, and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Department of Endocrinology and Metabolic Diseases, Kantonsspital Olten, Olten, Switzerland.
Diabetes Technol Ther. 2022 Nov;24(11):842-847. doi: 10.1089/dia.2022.0210. Epub 2022 Oct 11.
Traditional risk scores for the prediction of type 2 diabetes (T2D) are typically designed for a general population and, thus, may underperform for people with prediabetes. In this study, we developed machine learning (ML) models predicting the risk of T2D that are specifically tailored to people with prediabetes. We analyzed data of 13,943 individuals with prediabetes, and built a ML model to predict the risk of transition from prediabetes to T2D, integrating information about demographics, biomarkers, medications, and comorbidities defined by disease codes. Additionally, we developed a simplified ML model with only eight predictors, which can be easily integrated into clinical practice. For a forecast horizon of 5 years, the area under the receiver operating characteristic curve was 0.753 for our full ML model (79 predictors) and 0.752 for the simplified model. Our ML models allow for an early identification of people with prediabetes who are at risk of developing T2D.
传统的 2 型糖尿病（T2D）风险评分通常是为一般人群设计的，因此，对于糖尿病前期患者的预测效果可能不佳。在这项研究中，我们开发了专门针对糖尿病前期患者的机器学习（ML）模型来预测 T2D 的风险。我们分析了 13943 名糖尿病前期患者的数据，并构建了一个 ML 模型来预测从糖尿病前期到 T2D 的转换风险，整合了关于人口统计学、生物标志物、药物和疾病代码定义的合并症的信息。此外，我们还开发了一个只有八个预测因子的简化 ML 模型，该模型可以很容易地整合到临床实践中。对于 5 年的预测期，我们的全 ML 模型（79 个预测因子）和简化模型的受试者工作特征曲线下面积分别为 0.753 和 0.752。我们的 ML 模型可以早期识别出有患 T2D 风险的糖尿病前期患者。
