AUTHOR=Bose Sanjukta N. , Greenstein Joseph L. , Fackler James C. , Sarma Sridevi V. , Winslow Raimond L. , Bembea Melania M.
TITLE=Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit
JOURNAL=Frontiers in Pediatrics
VOLUME=9
YEAR=2021
URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2021.711104
DOI=10.3389/fped.2021.711104
ISSN=2296-2360
ABSTRACT=Objective: The objective of the study is to build models for early prediction of risk for developing multiple organ dysfunction (MOD) in pediatric intensive care unit (PICU) patients.
Design: The design of the study is a retrospective observational cohort study.
Setting: The setting of the study is at a single academic PICU at the Johns Hopkins Hospital, Baltimore, MD.
Patients: The patients included in the study were <18 years of age admitted to the PICU between July 2014 and October 2015.
Measurements and main results: Organ dysfunction labels were generated every minute from preceding 24-h time windows using the International Pediatric Sepsis Consensus Conference (IPSCC) and Proulx et al. MOD criteria. Early MOD prediction models were built using four machine learning methods: random forest, XGBoost, GLMBoost, and Lasso-GLM. An optimal threshold learned from training data was used to detect high-risk alert events (HRAs). The early prediction models from all methods achieved an area under the receiver operating characteristics curve ≥0.91 for both IPSCC and Proulx criteria. The best performance in terms of maximum F1-score was achieved with random forest (sensitivity: 0.72, positive predictive value: 0.70, F1-score: 0.71) and XGBoost (sensitivity: 0.8, positive predictive value: 0.81, F1-score: 0.81) for IPSCC and Proulx criteria, respectively. The median early warning time was 22.7 h for random forest and 37 h for XGBoost models for IPSCC and Proulx criteria, respectively. Applying spectral clustering on risk-score trajectories over 24 h following early warning provided a high-risk group with ≥0.93 positive predictive value.
Conclusions: Early predictions from risk-based patient monitoring could provide more than 22 h of lead time for MOD onset, with ≥0.93 positive predictive value for a high-risk group identified pre-MOD.