Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients
Eduardo A Trujillo Rivera1, James M Chamberlain2, Anita K Patel3
1George Washington University School of Medicine and Health Sciences, Washington, DC.
Insights
Machine learning models accurately predict future intensive care unit (ICU) needs for hospitalized children. These models show consistent performance across different prediction times, aiding in resource allocation.
Area of Science:
- Computational medicine and health informatics
- Pediatric critical care prediction modeling
Background:
- Accurate prediction of future healthcare needs is crucial for effective resource allocation in pediatric hospitals.
- Existing models may not adequately capture the dynamic nature of critical care requirements in children.
Purpose of the Study:
- To develop and compare distinct machine learning models for predicting intensive care unit (ICU) versus non-ICU care for hospitalized children.
- To assess model performance across four future time intervals: 6-12, 12-18, 18-24, and 24-30 hours.
- To validate models using an independent patient cohort and a simulated children's hospital environment.
Main Methods:
- Utilized the Health Facts database (Cerner Corporation) for predictive modeling, including patients with ICU admissions and routine care patients.
- Developed and validated models using training, validation, and testing sets derived from historical data (2009-2016).
- Employed four independent, sequential, fully connected neural networks calibrated to predict the risk of ICU care at specified future time points.
Main Results:
- Models demonstrated strong performance in the test sample, with high sensitivity, specificity, accuracy, and area under the receiver operating characteristic curves (AUROC).
- Performance remained robust in the independent 2017-2018 cohort, with sensitivity ≥ 0.545, specificity ≥ 0.972, accuracy ≥ 0.921, and AUROC ≥ 0.946.
- Comparable performance metrics were observed across simulated and real-world hospital settings, regardless of teaching status, bed capacity, or geographic location.
Conclusions:
- Machine learning models integrating physiology, therapy, and care intensity show promising performance in predicting future pediatric care needs.
- Model performance remained consistent and reliable across increasing prediction time horizons, from 6-12 hours to 24-30 hours.
- These predictive models can potentially enhance clinical decision-making and optimize resource allocation in pediatric healthcare settings.
Abstract:
Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6-12, 12-18, 18-24, and 24-30 hr) and assess these models in an independent cohort and simulated children's hospital.
Design:
Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO).
Setting:
Children hospitalized in ICUs.
Patients:
Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017-2018 cohort consisted of 80,089 children.
Interventions:
None.
Measurement And Main Results:
Initially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children's hospitals and the 2017-2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all R s > 0.98). Model performance in the 2017-2018 cohort was sensitivity greater than or equal to 0.545, specificity greater than or equal to 0.972, accuracy greater than or equal to 0.921, area under the receiver operating characteristic curves greater than or equal to 0.946, and R s greater than or equal to 0.979. Performance metrics were comparable for the simulated children's hospital and for hospitals stratified by teaching status, bed numbers, and geographic location.
Conclusions:
Machine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6-12 hours to 24-30 hours.
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