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已发表论文
基于机器学习的 2 型糖尿病患者心力衰竭风险预测模型的开发及外部验证
Authors Liu Y , Wang P, Wang M, Chen Y, Kasyanju SM, Yang Y, Yang T , Peng L, Sun M
Received 8 August 2025
Accepted for publication 4 November 2025
Published 13 November 2025 Volume 2025:18 Pages 4177—4191
DOI https://doi.org/10.2147/DMSO.S558687
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Rebecca Baqiyyah Conway
Yuqing Liu,1 Ping Wang,2 Min Wang,3 Yan Chen,2 Sania Martin Kasyanju,1 Yuhong Yang,1 Tao Yang,1 Li Peng,3,* Min Sun1,*
1Department of Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Endocrinology, Nanjing Gaochun People’s Hospital, Nanjing, People’s Republic of China; 3Department of Endocrinology, The Fourth Affiliated Hospital with Nanjing Medical University, Nanjing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Min Sun, Department of Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, People’s Republic of China, Email drsunm@njmu.edu.cn Li Peng, Department of Endocrinology, The Fourth Affiliated Hospital with Nanjing Medical University, Nanpu Road 298, Nanjing, 211800, People’s Republic of China, Email penglee@njmu.edu.cn
Background: Heart failure (HF) is a severe and common complication of type 2 diabetes mellitus (T2DM), associated with increased morbidity and mortality. Although the biomarker NT-proBNP, at a cut-off value of 125 pg/mL, has demonstrated satisfactory discriminatory power for predicting HF risk in T2DM patients, its measurement remains inaccessible in most primary healthcare settings in China. This study aimed to develop and externally validate a machine learning-based nomogram for predicting the risk of elevated NT-proBNP (≥ 125 pg/mL) as a surrogate for HF risk in patients with T2DM.
Methods: We retrospectively enrolled 564 T2DM patients as the development cohort and 302 from two external centers as the validation cohort. After feature selection via least absolute shrinkage and selection operator regression, five machine learning models were constructed and evaluated using 10-fold cross-validation. The optimal model was presented as a static nomogram and further deployed as an online web application for clinical use.
Results: Six key predictors were identified: estimated glomerular filtration rate, age, serum albumin, hemoglobin, urine albumin-to-creatinine ratio, and the binary indicator of age ≥ 65 years. Interpretability analysis using SHapley Additive exPlanations revealed estimated glomerular filtration rate as the most influential feature. The final machine learning-based nomogram achieved AUCs of 0.806 (95% CI: 0.767– 0.845) in training and 0.861 (95% CI: 0.813– 0.908) in external validation, with good calibration and clinical utility. Furthermore, the nomogram scores showed a significant positive correlation with established TRS-HFDM risk strata, supporting its clinical relevance.
Conclusion: We developed and validated an interpretable machine learning-based nomogram that effectively predicts the risk of elevated NT-proBNP in T2DM patients using six routine clinical variables. This tool demonstrates robust performance and generalizability, offering a practical and accessible solution for HF risk stratification in resource-limited primary care settings in China.
Keywords: type 2 diabetes mellitus, N-terminal pro-B-type natriuretic peptide, heart failure, prediction model, machine learning
1Department of Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, People’s Republic of China; 2Department of Endocrinology, Nanjing Gaochun People’s Hospital, Nanjing, People’s Republic of China; 3Department of Endocrinology, The Fourth Affiliated Hospital with Nanjing Medical University, Nanjing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Min Sun, Department of Endocrinology, The First Affiliated Hospital with Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, People’s Republic of China, Email drsunm@njmu.edu.cn Li Peng, Department of Endocrinology, The Fourth Affiliated Hospital with Nanjing Medical University, Nanpu Road 298, Nanjing, 211800, People’s Republic of China, Email penglee@njmu.edu.cn
Background: Heart failure (HF) is a severe and common complication of type 2 diabetes mellitus (T2DM), associated with increased morbidity and mortality. Although the biomarker NT-proBNP, at a cut-off value of 125 pg/mL, has demonstrated satisfactory discriminatory power for predicting HF risk in T2DM patients, its measurement remains inaccessible in most primary healthcare settings in China. This study aimed to develop and externally validate a machine learning-based nomogram for predicting the risk of elevated NT-proBNP (≥ 125 pg/mL) as a surrogate for HF risk in patients with T2DM.
Methods: We retrospectively enrolled 564 T2DM patients as the development cohort and 302 from two external centers as the validation cohort. After feature selection via least absolute shrinkage and selection operator regression, five machine learning models were constructed and evaluated using 10-fold cross-validation. The optimal model was presented as a static nomogram and further deployed as an online web application for clinical use.
Results: Six key predictors were identified: estimated glomerular filtration rate, age, serum albumin, hemoglobin, urine albumin-to-creatinine ratio, and the binary indicator of age ≥ 65 years. Interpretability analysis using SHapley Additive exPlanations revealed estimated glomerular filtration rate as the most influential feature. The final machine learning-based nomogram achieved AUCs of 0.806 (95% CI: 0.767– 0.845) in training and 0.861 (95% CI: 0.813– 0.908) in external validation, with good calibration and clinical utility. Furthermore, the nomogram scores showed a significant positive correlation with established TRS-HFDM risk strata, supporting its clinical relevance.
Conclusion: We developed and validated an interpretable machine learning-based nomogram that effectively predicts the risk of elevated NT-proBNP in T2DM patients using six routine clinical variables. This tool demonstrates robust performance and generalizability, offering a practical and accessible solution for HF risk stratification in resource-limited primary care settings in China.
Keywords: type 2 diabetes mellitus, N-terminal pro-B-type natriuretic peptide, heart failure, prediction model, machine learning