文献类型：期刊文献

英文题名：Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world

作者：Du, Zouxi[1,2,8];Liu, Xiaoning[3];Li, Jiayu[2];Min, Hang[1,2,8];Ma, Yuhu[5];Hua, Wenting[1,2,8];Zhang, Leyuan[4];Zhang, Yue[1,2,8];Shang, Mengmeng[4];Chen, Hui[6];Yin, Hong[7];Tian, Limin[1,2,8]

第一作者：Du, Zouxi

通信作者：Tian, LM[1];Tian, LM[2];Tian, LM[3]

机构：[1]Lanzhou Univ, Sch Clin Med 1, Lanzhou, Gansu, Peoples R China;[2]Gansu Prov Hosp, Dept Endocrinol, Lanzhou, Gansu, Peoples R China;[3]Lanzhou Univ, Sch Publ Hlth, Dept Epidemiol & Hlth Stat, Lanzhou, Gansu, Peoples R China;[4]Gansu Univ Tradit Chinese Med, Clin Med Coll 1, Lanzhou, Gansu, Peoples R China;[5]Lanzhou Univ, Hosp 1, Dept Anesthesiol, Lanzhou, Gansu, Peoples R China;[6]Lanzhou Univ, Hosp 2, Dept Endocrinol, Lanzhou, Gansu, Peoples R China;[7]First Peoples Hosp Lanzhou, Dept Geriatr, Lanzhou, Gansu, Peoples R China;[8]Clin Res Ctr Metab Dis, Lanzhou, Gansu, Peoples R China

第一机构：Lanzhou Univ, Sch Clin Med 1, Lanzhou, Gansu, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Sch Clin Med 1, Lanzhou, Gansu, Peoples R China;[2]corresponding author), Gansu Prov Hosp, Dept Endocrinol, Lanzhou, Gansu, Peoples R China;[3]corresponding author), Clin Res Ctr Metab Dis, Lanzhou, Gansu, Peoples R China.

年份：2024

外文期刊名：ACTA DIABETOLOGICA

收录：;Scopus(收录号：2-s2.0-85208779544);WOS:【SCI-EXPANDED(收录号:WOS:001352541600001)】；

基金：This study was supported by the Science and Technology Major Project, Gansu Province (No.22ZD6FA033).

语种：英文

外文关键词：Type 1 diabetes mellitus; Diabetic nephropathy; Machine learning; Interpretable model; External validation

摘要：AimsStudies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk of DN among individuals with T1DM.MethodsThis was a retrospective, multicenter study conducted across 19 hospitals in Gansu Province, China (No: 2022-473). In total, 1368 patients were eligible for analysis among 1633 collected T1DM patients from January 2016 to December 2023. Recursive feature elimination using random forest and fivefold cross-validation was conducted to identify key features. Among the 12 initial ML algorithms, the optimal ML model was developed and validated externally in a distinct population, and its predictive outcomes were explained via the SHapley additive exPlanations method, which offered personalized decision insights.ResultsAmong the 1368 T1DM patients, 324 had DN. The extreme gradient boosting (XGBoost) model, which achieved optimal performance with an AUC of 83% (95% confidence interval [CI]: 76-89), was selected to predict the risk of DN among T1DM patients. The DN predictive model included variables such as T1DM duration, postprandial glucose (PPG), systolic blood pressure (SBP), glycated hemoglobin (HbA1c), serum creatinine (Scr) and low-density lipoprotein cholesterol (LDL-C). External validation confirmed the reliability of the model, with an AUC of 76% (95% CI: 70-82).ConclusionsThe ML prediction tool has potential for advancing early and precise identification of the risk of DN among T1DM patients. Although successful external validation indicated that the developed model can provide a promising strategy for clinical adoption and help improve patient outcomes through timely and accurate risk assessment, additional prospective data and further validation in diverse populations are necessary.