Risk of rapid progression to dialysis in patients with type 2 diabetes mellitus with and without diabetes-related complications at diagnosis

Predicting coronary heart disease in Chinese diabetics using machine learning

Diabetes, a common chronic disease worldwide, can induce vascular complications, such as coronary heart disease (CHD), which is also one of the main causes of human death. It is of great significance to study the factors of diabetic patients complicated with CHD for understanding the occurrence of diabetes/CHD comorbidity. In this study, by analyzing the risk of CHD in more than 300,000 diabetes patients in southwest China, an artificial intelligence (AI) model was proposed to predict the risk of diabetes/CHD comorbidity. Firstly, we statistically analyzed the distribution of four types of features (basic demographic information, laboratory indicators, medical examination, and questionnaire) in comorbidities, and evaluated the predictive performance of three traditional machine learning methods (eXtreme Gradient Boosting, Random Forest, and Logistic regression). In addition, we have identified nine important features, including age, WHtR, BMI, stroke, smoking, chronic lung disease, drinking and MSP. Finally, the model produced an area under the receiver operating characteristic curve (AUC) of 0.701 on the test samples. These findings can provide personalized guidance for early CHD warning for diabetic populations.

Diabetes Pay-for-Performance Program Participation and Dialysis Risk in Relation to Educational Attainment: A Retrospective Cohort Study

Pay-for-performance (P4P) programs for diabetes care enable the provision of comprehensive and continuous health care to diabetic patients. However, patient outcomes may be affected by the patient's educational attainment. The present retrospective cohort study aimed to examine the effects of the educational attainment of diabetic patients on participation in a P4P program in Taiwan and the risk of dialysis. The data were obtained from the National Health Insurance Research Database of Taiwan. Patients newly diagnosed with type 2 diabetes mellitus (T2DM) aged 45 years from 2002 to 2015 were enrolled and observed until the end of 2017. The effects of their educational attainment on their participation in a P4P program were examined using the Cox proportional hazards model, while the impact on their risk for dialysis was investigated using the Cox proportional hazards model. The probability of participation in the P4P program was significantly higher in subjects with a junior high school education or above than in those who were illiterate or had only attained an elementary school education. Subjects with higher educational attainment exhibited a lower risk for dialysis. Different educational levels had similar effects on reducing dialysis risk among diabetic participants in the P4P program.