Evidence-Based Pediatric Outcome Predictors to Guide the Allocation of Critical Care Resources in a Mass Casualty Event

• 2015

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• Philip Toltzis; Gerardo Soto-Campos; Christian Shelton; Evelyn Kuhn; Ryan Hahn; Robert Kanter
PMID: 26121100DOI: 10.1097/PCC.0000000000000481

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This article proposes a plan to help doctors decide which children should get critical care during big emergencies. It suggests dividing kids into two groups: those needing machines to breathe and those who don't. Doctors would then predict how likely each child is to survive and how long they might need care. The goal is to save more lives by giving resources to kids who can benefit the most from short-term ICU help. The study used data from many hospitals and found that machine learning could also help make these decisions accurately.

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Tags

Focus Area: Trauma

Audience: Emergency Managers

Emergency Management Stage: Mitigation

Bundles: Disaster Research Literature Review

Keywords: Critical Care; Health Care Rationing; Intensive Care Units, Pediatric; Pediatrics; Triage; Resource Allocation; Mass Casualty Incidents

Institutions: UH Rainbow Babies & Children's Hospital; Children's Wisconsin; Virginia Tech Carilion School of Medicine; Columbia University; Children's Hospital Los Angeles; Virtual PICU Systems LLC

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