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Predicting Intensive Care Readmission Among Hospitalized Children
Insights
Machine learning models for pediatric intensive care unit (PICU) readmissions showed limited generalizability. Locally derived models had modest performance, suggesting potential for provider decision-making if prospectively validated.
Area of Science:
- Pediatric critical care medicine
- Machine learning applications in healthcare
- Patient safety and quality improvement
Background:
- Pediatric intensive care unit (PICU) readmissions are linked to worse patient outcomes.
- Predicting PICU readmissions can enable timely interventions to improve care.
- Developing accurate predictive models is crucial for patient safety.
Purpose of the Study:
- To develop and validate machine learning models for predicting PICU readmission risk at the time of patient transfer.
- To assess the generalizability of these models across different clinical sites.
Main Methods:
- Retrospective observational cohort study of 35,601 children admitted to three quaternary care PICUs (2012-2019).
- Developed and validated four models (logistic regression, elastic net, random forest, XGBoost) using vital signs, patient characteristics, and lab results.
- Primary outcome: unplanned PICU readmission within 48 hours of transfer.
Main Results:
- Internal validation showed consistent model performance (AUC 0.70-0.73) across sites.
- External validation revealed a significant decrease in performance (AUC 0.60-0.69).
- Key predictive variables varied by site, indicating limited generalizability.
Conclusions:
- Machine learning models for PICU readmission prediction exhibit limited generalizability.
- Locally derived models showed modest performance and may aid decision-making if prospectively validated.
- Models developed externally are unlikely to perform well in predicting PICU readmissions.
Objective:
Readmissions to the PICU are associated with increased morbidity and mortality. A prediction model that can identify children at risk of readmission at the time of transfer can allow providers to intervene and potentially improve patient outcomes. The objective of this study was to derive and validate machine learning models to predict PICU readmission at the time of transfer.
Design:
Retrospective observational cohort study.
Setting:
Three quaternary care PICUs in the city of Chicago.
Patients:
All children admitted to the PICU between 2012 and 2019.
Measurements:
The primary outcome was unplanned readmission to the PICU within 48 hours of transfer to the inpatient ward. Predictor variables included vital signs, patient characteristics, and laboratory results. We developed and externally validated four models to predict PICU readmission: logistic regression, elastic net, random forest, and XGBoost.
Main Results:
This study included 35,601 patients, with readmission rates ranging from 2.2 - 3.7% by site. The performance of models during internal validation was consistent at the three sites, with the area under the receiver operating characteristic (AUC) values between 0.70 and 0.73 and no difference across the four models. Model performance decreased significantly during external validation (AUCs of 0.60 - 0.69). The variables most important to the prediction differed at each site.
Conclusion:
Machine learning models for predicting readmissions to the PICU have limited generalizability. Locally derived models demonstrated modest performance in our study and could potentially inform provider decision-making if prospectively validated. Externally developed models are unlikely to perform well at predicting PICU readmissions.
