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Predictive Model for In-Hospital Death in Older Patients with Type 2 Diabetes Mellitus: A Multicenter Retrospective
Yang Tang1, Zhengyu Zhang2, Yue Yu3
1Department of Cardiology, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People's Republic of China.
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.
