DISCUSSION
cancer, HDL‐C.Over, diabetic, heart failure, T2DM, diabetes
KIDNEY DISEASE, CANCER, DISEASE, LIVER DISEASE, HEART FAILURE, CEREBROVASCULAR DISEASE, DIABETES
This study constructed the first 5‐year all‐cause mortality risk prediction model for patients with T2DM in south China. The model achieved good discrimination and calibration by using highly accessible predictors collected in routine clinical settings, including age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, HbA1c, and HDL‐C.Over the past decade or so, many all‐cause mortality risk prediction models have been constructed for patients with T2DM. However, due to differences in ethnicity, genetics, socioeconomic factors, and disease management approaches, prediction models developed based on specific populations are usually not directly applicable to other populations. Several models have been established to predict the risk of all‐cause mortality in Western populations with T2DM.Notably, the data sources used to develop the models can also affect predictive validity. Whether models developed based on non‐real‐world data can be well applied to the real world remains a question. It has been found that prediction models perform much less well in the clinical trial setting than in the real world.The SLHD is the largest medical database in mainland China, which provided a good basis for the design and implementation of this study. We identified a number of candidate predictors based on data availability and clinical relevance. The final prediction model included age, sex, heart failure, cerebrovascular disease, moderate or severe kidney disease, moderate or severe liver disease, cancer, insulin use, HbA1c, and HDL‐C, which balanced the predictive validity and parsimony of the model well.Glycemic control is definitely crucial for patients with T2DM. Clinically, the main indicator used to evaluate glycemic control status is HbA1c, which reflects the average plasma glucose level over the past 2–3 months. Insulin, as the most effective class of antidiabetic drugs, is commonly used in patients whose plasma glucose levels cannot be well controlled by oral antidiabetic drugs. In general, patients with higher HbA1c levels or those using insulin have a longer duration and more severe disease. Therefore, HbA1c and insulin play a crucial role in predicting the risk of all‐cause mortality among patients with T2DM.This study has some limitations. The first is the data limitation. Although the SLHD is the largest medical database in mainland China, there is a relatively high proportion of missing data for some variables, such as biochemical indicators, duration of diabetes, body mass index, smoking, and alcohol consumption. It is worth noting that such missing data were usually not random and may indicate patient characteristics, health awareness level, and health status. Directly removing observations with missing values would introduce severe bias. Therefore, we performed multiple imputation to handle missing data (widely considered the best approach) and conducted complete case analysis to examine the effect of missing data. Second, due to resource constraints, the prediction model was only internally validated, and further external validation in other populations is advisable. Third, only a 5‐year all‐cause mortality risk prediction model was constructed in this study, and future studies with longer follow‐up periods are needed to update the model to predict the mortality risk at 10, 15, and more years.In conclusion, this study constructed the first 5‐year all‐cause mortality risk prediction model for patients with T2DM in south China, which achieved good discrimination and calibration by using highly accessible predictors collected in routine clinical settings. The model provides a powerful tool for clinicians to identify high‐risk diabetic patients, which can enhance clinical management and facilitate timely intervention, ultimately improving survival quality and extending life expectancy.