Nomogram Predicting 90-Day Readmission in Patients with Diabetes: A Prospective Study
Ziyan Dong, Wen Xie, Liuqing Yang, Yue Zhang, Jie Li School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People’s Republic of ChinaCorrespondence: Jie Li, School of Nursing, Tongji Medical College, 13 hangkong Road, Qiaokou District, Wuhan...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Dove Medical Press
2025-01-01
|
| Series: | Diabetes, Metabolic Syndrome and Obesity |
| Subjects: | |
| Online Access: | https://www.dovepress.com/nomogram-predicting-90-day-readmission-in-patients-with-diabetes-a-pro-peer-reviewed-fulltext-article-DMSO |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Ziyan Dong, Wen Xie, Liuqing Yang, Yue Zhang, Jie Li School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, People’s Republic of ChinaCorrespondence: Jie Li, School of Nursing, Tongji Medical College, 13 hangkong Road, Qiaokou District, Wuhan, Hubei Province, 430030, People’s Republic of China, Tel +86-189-7109-7091, Email Lijie@hust.edu.cnPurpose: Readmission within a period time of discharge is common and costly. Diabetic patients are at risk of readmission because of comorbidities and complications. It is crucial to monitor patients with diabetes with risk factors for readmission and provide them with target suggestions. We aim to develop a nomogram to predict the risk of readmission within 90 days of discharge in diabetic patients.Patients and Methods: This is a prospective observational survey. A total of 784 adult patients with diabetes recruited in two tertiary hospitals in central China were randomly assigned to a training set or a validation set at a ratio of 7:3. Depression, anxiety, self-care, physical activity, and sedentary behavior were assessed during hospitalization. A 90-day follow-up was conducted after discharge. Multivariate logistic regression was employed to develop a nomogram, which was validated with the use of a validation set. The AUC, calibration plot, and clinical decision curve were used to assess the discrimination, calibration, and clinical usefulness of the nomogram, respectively.Results: In this study, the 90-day readmission rate in our study population was 18.6%. Predictors in the final nomogram were previous admissions within 1 year of the index admission, self-care scores, anxiety scores, physical activity, and complicating with lower extremity vasculopathy. The AUC values of the predictive model and the validation set were 0.905 (95% CI=0.874– 0.936) and 0.882 (95% CI=0.816– 0.947). Hosmer–Lemeshow test values were p = 0.604 and p = 0.308 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. Decision curve analysis indicated that the nomogram improved the clinical net benefit within a probability threshold of 0.02– 0.96.Conclusion: The nomogram constructed in this study was a convenient tool to evaluate the risk of 90-day readmission in patients with diabetes and contributed to clinicians screening the high-risk populations.Keywords: readmission, rehospitalization, diabetes, nomogram, prediction model, diabetes mellitus |
|---|---|
| ISSN: | 1178-7007 |