Yang Tang,1 Zhengyu Zhang,2 Yue Yu,3 Yuxin He,4 Yuan Yuan,5 Xin Wu,6 Qian Xu,7 Jianhua Niu,8 Xiaoxin Wu,9 Juntao Tan10 

1Department of Cardiology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People’s Republic of China; 2Medical Records Department, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People’s Republic of China; 3Senior Bioinformatician Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA; 4Department of Medical Administration, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People’s Republic of China; 5Medical Records Department, Women and Children’s Hospital of Chongqing Medical University, Chongqing, 401147, People’s Republic of China; 6Department of Gastrointestinal Surgery, Third Affiliated Hospital of Chongqing Medical University, Chongqing, 401120, People’s Republic of China; 7Library, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 8Department of Critical Care, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People’s Republic of China; 9State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, People’s Republic of China; 10College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China

Correspondence: Xiaoxin Wu, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qing Chun Road, Hangzhou, Zhejiang, 310003, People’s Republic of China, Tel +8615988112032, Email xiaoxinwu@zju.edu.cn Juntao Tan, College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, People’s Republic of China, Tel +8618375753171, Email tanjuntao@hospital.cqmu.edu.cn

Objective: Older patients with type 2 diabetes mellitus (T2DM) often face severe health challenges. This study aims to develop and validate a predictive model for estimating in-hospital death risk in this population.
Methods: Clinical data of 17,421 patients with T2DM aged ≥ 65 years admitted to six hospitals in southwest China were collected retrospectively. Model performance was assessed through area under the receiver operating characteristic curve (AUROC) analysis and calibration plots. Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC).
Results: The overall in-hospital death rate was 3.19% (556 cases). Eleven independent predictors were identified: age, gender, history of surgery, Charlson Comorbidity Index score, coronary heart disease, chronic obstructive pulmonary disease, serum levels of creatinine, albumin, glycated hemoglobin, nutritional support drug use, and antibiotic drug use. The multivariable model demonstrated robust predictive accuracy with AUROC values of 0.873 (95% CI: 0.857– 0.889) in training set, 0.830 (0.797– 0.864) in internal validation set, and 0.834 (0.757– 0.911) in external validation set. Bootstrap validation (n=1,000 resamples) confirmed adequate calibration. DCA and CIC analyses revealed substantial clinical net benefit across threshold probabilities. An interactive web-based calculator was implemented for clinical application (https://cqykdxtjt.shinyapps.io/in_hospital_death/).
Conclusion: The prediction model developed in this study demonstrated robust discrimination, calibration, and clinical utility. It can assist healthcare professionals in identifying high-risk older patients with T2DM, facilitating early prevention, detection, and intervention, thereby reducing the risk of in-hospital death in this vulnerable population.

Keywords: diabetes mellitus, type 2, hospital mortality, aged, predictive models