Derivation and validation of a predictive model for the readmission of patients with diabetes mellitus treated in internal medicine departments

Objectives: We developed a predictive model for the hospital readmission of patients with diabetes. The objective was to identify the frail population that requires additional strategies to prevent readmissions at 90 days.

Methods: Using data collected from 1977 patients in 3 studies on the national prevalence of diabetes (2015-2017), we developed and validated a predictive model of readmission at 90 days for patients with diabetes.

Results: A total of 704 (36%) readmissions were recorded. There were no differences in the readmission rates over the course of the 3 studies. The hospitals with more than 500 beds showed significantly (p=.02) higher readmission rates than those with fewer beds. The main reasons for readmission were infectious diseases (29%), cardiovascular diseases (24) and respiratory diseases (14%). Readmissions directly related to diabetic decompensations accounted for only 2% of all readmissions. The independent variables associated with hospital readmission were patient's age, degree of comorbidity, estimated glomerular filtration rate, degree of disability, presence of previous episodes of hypoglycaemia, use of insulin in treating diabetes and the use of systemic glucocorticoids. The predictive model showed an area under the ROC curve (AUC) of 0.676 (95% confidence interval [95% CI] 0.642-0.709; p=.001) in the referral cohort. In the validation cohort, the model showed an AUC of 0.661 (95% CI 0.612-0.710; p=.001).

Conclusion: The model we developed for predicting readmissions for hospitalised patients with type 2 diabetes helps identify a subgroup of frail patients with a high risk of readmission.