Predictive Model for In-Hospital Death in Older Patients with Type 2 Diabetes Mellitus: A Multicenter Retrospective Study in Southwest China
Notice bibliographique
Résumé
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 ChinaCorrespondence: 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.cnObjective: 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
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».