Key result
Multifactorial prediction model shows acceptable discrimination for 30-day acute readmissions in older patients with ~0.70 AUC.
Why the study?
Knowledge is sparse regarding how different social factors can contribute to predicting acute readmission in older patients.
Observational (n=770)
No
Effect estimate: AUC 0.70
A comprehensive prediction model incorporating social factors such as educational level alongside clinical variables achieved acceptable discrimination for predicting 30-day acute readmissions in older medical patients.
Supports incorporating social factors into readmission models for older patients; hypothesis-generating and requires prospective validation before clinical use.
Background: Readmission rate is one way to measure quality of care for older patients. Knowledge is sparse on how different social factors can contribute to predict readmission. We aimed to develop and internally validate a comprehensive model for prediction of acute 30-day readmission among older medical patients using various social factors along with demographic, organisational and health-related factors. Methods: We performed an observational prospective study based on a group of 770 medical patients aged 65 years or older, who were consecutively screened for readmission risk factors at an acute care university hospital during the period from February to September 2012. Data on outcome and candidate predictors were obtained from clinical screening and administrative registers. We used multiple logistic regression analyses with backward selection of predictors. Measures of model performance and performed internal validation were calculated. Results: Twenty percent of patients were readmitted within 30 days from index discharge. The final model showed that low educational level, along with male gender, contact with emergency doctor, specific diagnosis, higher Charlson Comorbidity Index score, longer hospital stay, cognitive problems, and medical treatment for thyroid disease, acid-related disorders, and glaucoma, predicted acute 30-day readmission. Area under the receiver operating characteristic curve (0.70) indicated acceptable discriminative ability of the model. Calibration slope was 0.98 and calibration intercept was 0.01. In internal validation analysis, both discrimination and calibration measures were stable. Conclusions: We developed a model for prediction of readmission among older medical patients. The model showed that social factors in the form of educational level along with demographic, organisational and health-related factors contributed to prediction of acute 30-day readmissions among older medical patients.
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Lehn et al. (2019) conducted an observational in Older medical patients at risk of readmission (n=770). Prediction model including social, demographic, organisational, and health-related factors was evaluated on Acute all-cause readmission within 30 days (AUC 0.70). A prediction model incorporating social, demographic, and health-related factors predicted 30-day acute readmission among older medical patients with acceptable discriminative ability (AUC 0.70).