Statistical analysis
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All the patients were randomly allocated into training and validation sets at a 2:1 ratio. Baseline characteristics between the training and validation sets were compared using the Student's Cox proportional hazards models were used to develop the all‐cause mortality prediction model. To account for the nonlinearity of the continuous variables (age, HbA1c, total cholesterol, HDL‐C, and LDL‐C), we transformed them using restricted cubic splines with four knots placed at the respective 5th, 35th, 65th, and 95th sample percentiles.The developed model was applied to the 10 imputed validation datasets to evaluate model performance. Discrimination was assessed by estimating Harrell C‐index, with higher values indicating better performance. Calibration was assessed by plotting the predicted event probabilities against the observed event probabilities. The risk prediction model was internally validated using 100 bootstrap samples. In addition, to examine the effect of missing data, we repeated the validation using complete case analysis. All statistical analyses were performed using R language software, version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria).