An extreme gradient boosting (XGBoost) model predicted unplanned 30-day hospital readmission using early hospitalization data with an AUROC of 0.742 on the validation dataset.
Observational (n=96,550)
Yes
An XGBoost machine learning model using data available within 24 hours of admission achieved an AUC of 0.742 for predicting unplanned 30-day hospital readmission.
Effect estimate: AUROC 0.742
BACKGROUND: Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. OBJECTIVE: The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. METHODS: We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. RESULTS: We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. CONCLUSIONS: The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.
Zhao et al. (Wed,) conducted a observational in unplanned 30-day hospital readmission (n=96,550). Early prediction model (XGBoost) was evaluated on Unplanned 30-day hospital readmission (AUROC 0.742). An extreme gradient boosting (XGBoost) model predicted unplanned 30-day hospital readmission using early hospitalization data with an AUROC of 0.742 on the validation dataset.
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