111614
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
用于预测糖尿病合并缺血性脑卒中患者 1 年内非计划再入院的可解释机器学习模型：炎症与代谢的协同视角
Authors Hu Y, Zhang Y, Lin P, Hu X, Zhu Y, Yan P, Fei F, Wang Q, Yao X, Ren J
Received 5 June 2025
Accepted for publication 14 November 2025
Published 27 November 2025 Volume 2025:20 Pages 2163—2175
DOI https://doi.org/10.2147/CIA.S544949
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Maddalena Illario
Yue Hu,1,* Yunhong Zhang,2,* Peichong Lin,3,4,* Xinye Hu,5 Yiping Zhu,6 Peiling Yan,7 Fan Fei,8 Qiuyan Wang,9 Xuelin Yao,10 Jingjing Ren1
1Department of General Practice, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 2Department of General Practice, Dali Bai Autonomous Prefecture People’s Hospital, Dali, People’s Republic of China; 3Department of General Practice, Jianqiao Street Community Health Service Center, Hangzhou, People’s Republic of China; 4Department of General Practice, Xiasi Town Central Health Center, Jiange County, Guangyuan, People’s Republic of China; 5Department of General Practice, Quxi Street Community Health Service Center, Wenzhou, People’s Republic of China; 6Department of General Practice, Zhuangqiao Sub-District Community Health Service Center, Ningbo, People’s Republic of China; 7Department of General Practice, Zhuangshi Street Community Health Service Center, Ningbo, People’s Republic of China; 8Department of General Practice, Sanshipu Town Central Health Center, Luan, People’s Republic of China; 9Department of General Practice, Kangqiao Street Community Health Service Center, Hangzhou, People’s Republic of China; 10Department of General Practice, Qixing Street Community Health Service Center, Jiaxing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jingjing Ren, Email 3204092@zju.edu.cn
Background: To develop and validate an interpretable machine learning (ML) model integrating inflammatory and metabolic biomarkers for predicting the risk of 1-year unplanned readmission in patients with ischemic stroke (IS) and type 2 diabetes mellitus (T2DM).
Methods: This retrospective study included IS patients with comorbid T2DM who were hospitalized between June 2022 and December 2023. A total of 49 clinical variables were extracted. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. The dataset was randomly divided into a training set (70%) and a validation set (30%). Seven widely used ML algorithms were applied to construct predictive models, and model performance was evaluated using a validation set. No external validation was performed in this study. The best-performing model was further interpreted using Shapley Additive Explanations (SHAP), and a dynamic nomogram was developed for individualized risk assessment.
Results: A total of 833 patients were included, with a 1-year unplanned readmission rate of 34.3%. LASSO regression identified nine key variables: age, neutrophil-to-lymphocyte ratio (NLR), homocysteine (HCY), glycated hemoglobin A1c (HbA1c), triglyceride-glucose (TyG) index, metformin use, and the presence of hyperlipidemia, pulmonary infection, and renal insufficiency. The random forest model demonstrated the best overall performance (area under the curve [AUC] = 0.78, F1 score = 0.70). SHAP analysis indicated that NLR, HCY, HbA1c, and TyG index were the most influential predictors, suggesting that chronic inflammation and metabolic dysregulation play pivotal roles in readmission risk.
Conclusion: The ML model based on inflammatory and metabolic biomarkers effectively predicts 1-year unplanned readmission risk in IS patients with T2DM, with good interpretability and clinical potential. The dynamic nomogram enables real-time, individualized risk prediction to support early identification of high-risk patients, tailored follow-up, and targeted allocation of healthcare resources.
Keywords: ischemic stroke, type 2 diabetes mellitus, unplanned readmission, machine learning, inflammatory biomarkers, metabolic biomarkers
1Department of General Practice, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China; 2Department of General Practice, Dali Bai Autonomous Prefecture People’s Hospital, Dali, People’s Republic of China; 3Department of General Practice, Jianqiao Street Community Health Service Center, Hangzhou, People’s Republic of China; 4Department of General Practice, Xiasi Town Central Health Center, Jiange County, Guangyuan, People’s Republic of China; 5Department of General Practice, Quxi Street Community Health Service Center, Wenzhou, People’s Republic of China; 6Department of General Practice, Zhuangqiao Sub-District Community Health Service Center, Ningbo, People’s Republic of China; 7Department of General Practice, Zhuangshi Street Community Health Service Center, Ningbo, People’s Republic of China; 8Department of General Practice, Sanshipu Town Central Health Center, Luan, People’s Republic of China; 9Department of General Practice, Kangqiao Street Community Health Service Center, Hangzhou, People’s Republic of China; 10Department of General Practice, Qixing Street Community Health Service Center, Jiaxing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jingjing Ren, Email 3204092@zju.edu.cn
Background: To develop and validate an interpretable machine learning (ML) model integrating inflammatory and metabolic biomarkers for predicting the risk of 1-year unplanned readmission in patients with ischemic stroke (IS) and type 2 diabetes mellitus (T2DM).
Methods: This retrospective study included IS patients with comorbid T2DM who were hospitalized between June 2022 and December 2023. A total of 49 clinical variables were extracted. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. The dataset was randomly divided into a training set (70%) and a validation set (30%). Seven widely used ML algorithms were applied to construct predictive models, and model performance was evaluated using a validation set. No external validation was performed in this study. The best-performing model was further interpreted using Shapley Additive Explanations (SHAP), and a dynamic nomogram was developed for individualized risk assessment.
Results: A total of 833 patients were included, with a 1-year unplanned readmission rate of 34.3%. LASSO regression identified nine key variables: age, neutrophil-to-lymphocyte ratio (NLR), homocysteine (HCY), glycated hemoglobin A1c (HbA1c), triglyceride-glucose (TyG) index, metformin use, and the presence of hyperlipidemia, pulmonary infection, and renal insufficiency. The random forest model demonstrated the best overall performance (area under the curve [AUC] = 0.78, F1 score = 0.70). SHAP analysis indicated that NLR, HCY, HbA1c, and TyG index were the most influential predictors, suggesting that chronic inflammation and metabolic dysregulation play pivotal roles in readmission risk.
Conclusion: The ML model based on inflammatory and metabolic biomarkers effectively predicts 1-year unplanned readmission risk in IS patients with T2DM, with good interpretability and clinical potential. The dynamic nomogram enables real-time, individualized risk prediction to support early identification of high-risk patients, tailored follow-up, and targeted allocation of healthcare resources.
Keywords: ischemic stroke, type 2 diabetes mellitus, unplanned readmission, machine learning, inflammatory biomarkers, metabolic biomarkers