Developing a prediction model for all‐cause mortality risk among patients with type 2 diabetes mellitus in Shanghai, China

作者：Jiying Qi, Ping He, Huayan Yao, Yanbin Xue, Wen Sun, Ping Lu, Xiaohui Qi, Zizheng Zhang, Renjie Jing, Bin Cui, Guang Ning · 发表于：Journal of Diabetes · 年份：2022 · DOI：10.1111/1753-0407.13343 · 被引用次数：9 · 研究领域：Chronic Disease Management Strategies、Machine Learning in Healthcare、Diabetes, Cardiovascular Risks, and Lipoproteins

BACKGROUND: All-cause mortality risk prediction models for patients with type 2 diabetes mellitus (T2DM) in mainland China have not been established. This study aimed to fill this gap. METHODS: Based on the Shanghai Link Healthcare Database, patients diagnosed with T2DM and aged 40-99 years were identified between January 1, 2013 and December 31, 2016 and followed until December 31, 2021. All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Cox proportional hazards models were used to develop the all-cause mortality risk prediction model. The model performance was evaluated by discrimination (Harrell C-index) and calibration (calibration plots). RESULTS: A total of 399 784 patients with T2DM were eventually enrolled, with 68 318 deaths over a median follow-up of 6.93 years. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, glycosylated hemoglobin, and high-density lipoprotein cholesterol. The model showed good discrimination and calibration in the validation sets: the mean C-index value was 0.8113 (range 0.8110-0.8115) and the predicted risks closely matched the observed risks in the calibration plots. CONCLUSIONS: This study constructed the first 5-year all-cause mortality risk prediction model for patients with T2DM in south China, with good predictive performance.