Early Prediction of Multiple Organ Dysfunction in the Pediatric Intensive Care Unit.

Autor:	Bose SN; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.; Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States., Greenstein JL; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States., Fackler JC; Department of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States., Sarma SV; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States., Winslow RL; Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, United States.; Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, United States., Bembea MM; Department of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in pediatrics [Front Pediatr] 2021 Aug 16; Vol. 9, pp. 711104. Date of Electronic Publication: 2021 Aug 16 (Print Publication: 2021).
DOI:	10.3389/fped.2021.711104
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Bose, Greenstein, Fackler, Sarma, Winslow and Bembea.)
Databáze:	MEDLINE
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