Key result
An extreme gradient boosting (XGBoost) model using early hospitalization data predicted unplanned 30-day hospital readmission with an AUROC of 0.742 on the validation dataset.
Why the study?
Existing readmission reduction solutions focus on discharge or postdischarge, while early preventive interventions during hospitalization are underexplored and early risk prediction remains challenging.
Can an early prediction model using machine learning accurately predict unplanned 30-day hospital readmission using data available within 24 hours of admission?
Observational (n=96,550)
Yes
Can an early prediction model using machine learning accurately predict unplanned 30-day hospital readmission using data available within 24 hours of admission?
Effect estimate: AUROC 0.742
An early prediction model using XGBoost can accurately predict 30-day hospital readmission using data available within the first 24 hours of hospitalization.
Early inpatient readmission prediction may enable preventive strategies; leaves open need for validation before practice change.
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.
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Zhao et al. (2019) conducted an 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 using early hospitalization data predicted unplanned 30-day hospital readmission with an AUROC of 0.742 on the validation dataset.
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