JMIR medical informatics · 2026
Development and Validation of a Machine Learning-Based Model for Predicting All-Cause Mortality Risk in Patients With Type 2 Diabetes Mellitus Combined With Hypertension: National Cohort Study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Ding Wenlong, Fang Lei, Fang Cunming
Аннотация
BACKGROUND: Type 2 diabetes mellitus (T2DM) combined with hypertension significantly increases mortality risk, yet accurate risk prediction models remain limited.
OBJECTIVE: We aimed to develop and validate machine learning-based models to predict all-cause mortality in patients with T2DM and hypertension.
METHODS: We analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2018 linked with mortality data up to December 31, 2019. Adult participants (aged ≥20 years) with concurrent T2DM and hypertension were included. Five machine learning algorithms were developed and compared: random forest, light gradient boosting machine, decision tree, extreme gradient boosting, and logistic regression. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis.
RESULTS: A total of 2428 participants were included (mean age 62.05, SE 0.33 years; n=1218, 50.15% female). During a median follow-up of 6.75 (IQR 4.20-8.90) years, among the 2428 patients, 719 (29.6%) deaths occurred. The random forest model demonstrated superior performance (AUC=0.873, 95% CI 0.856-0.891) compared to light gradient boosting machine (AUC=0.785), decision tree (AUC=0.732), extreme gradient boosting (AUC=0.792), and logistic regression (AUC=0.783). Key predictive features included age, race, chronic kidney disease, BMI, and blood urea nitrogen. The model exhibited excellent calibration and clinical utility across various risk thresholds.
CONCLUSIONS: Our machine learning-based model provides accurate all-cause mortality prediction for patients with T2DM and hypertension, potentially supporting clinical decision-making and risk stratification in this high-risk population.
OBJECTIVE: We aimed to develop and validate machine learning-based models to predict all-cause mortality in patients with T2DM and hypertension.
METHODS: We analyzed data from the National Health and Nutrition Examination Survey from 1999 to 2018 linked with mortality data up to December 31, 2019. Adult participants (aged ≥20 years) with concurrent T2DM and hypertension were included. Five machine learning algorithms were developed and compared: random forest, light gradient boosting machine, decision tree, extreme gradient boosting, and logistic regression. Model performance was evaluated using area under the curve (AUC), calibration plots, and decision curve analysis.
RESULTS: A total of 2428 participants were included (mean age 62.05, SE 0.33 years; n=1218, 50.15% female). During a median follow-up of 6.75 (IQR 4.20-8.90) years, among the 2428 patients, 719 (29.6%) deaths occurred. The random forest model demonstrated superior performance (AUC=0.873, 95% CI 0.856-0.891) compared to light gradient boosting machine (AUC=0.785), decision tree (AUC=0.732), extreme gradient boosting (AUC=0.792), and logistic regression (AUC=0.783). Key predictive features included age, race, chronic kidney disease, BMI, and blood urea nitrogen. The model exhibited excellent calibration and clinical utility across various risk thresholds.
CONCLUSIONS: Our machine learning-based model provides accurate all-cause mortality prediction for patients with T2DM and hypertension, potentially supporting clinical decision-making and risk stratification in this high-risk population.