In Annals of surgery ; h5-index 104.0
OBJECTIVE : We used machine learning to identify the highest impact components of emergency department (ED) pediatric readiness for predicting in-hospital survival among children cared for in US trauma centers.
SUMMARY BACKGROUND DATA : Emergency department (ED) pediatric readiness is associated with improved short- and long-term survival among injured children and part of the national verification criteria for US trauma centers. However, the components of ED pediatric readiness most predictive of survival are unknown.
METHODS : This was a retrospective cohort study of injured children <18 years treated in 458 trauma centers from 1/1/2012 through 12/31/2017, matched to the 2013 National ED Pediatric Readiness Assessment and the American Hospital Association survey. We used machine learning to analyze 265 potential predictors of survival, including 152 ED readiness variables, 29 patient variables, and 84 ED- and hospital-level variables. The primary outcome was in-hospital survival.
RESULTS : There were 274,756 injured children, including 4,585 (1.7%) who died. Nine ED pediatric readiness components were associated with the greatest increase in survival: policy for mental health care (+8.8% change in survival), policy for patient assessment (+7.5%), specific respiratory equipment (+7.2%), policy for reduced-dose radiation imaging (+7.0%), physician competency evaluations (+4.9%), recording weight in kilograms (+3.2%), life support courses for nursing (+1.0% to 2.5%), and policy on pediatric triage (+2.5%). There was a 268% improvement in survival when the five highest impact components were combined.
CONCLUSION : ED pediatric readiness components related to specific policies, personnel, and equipment were the strongest predictors of pediatric survival and worked synergistically when combined.
Newgard Craig D, Babcock Sean R, Song Xubo, Remick Katherine E, Gausche-Hill Marianne, Lin Amber, Malveau Susan, Mann N Clay, Nathens Avery B, Cook Jennifer N B, Jenkins Peter C, Burd Randall S, Hewes Hilary A, Glass Nina E, Jensen Aaron R, Fallat Mary E, Ames Stefanie G, Salvi Apoorva, McConnell K John, Ford Rachel, Auerbach Marc, Bailey Jessica, Riddick Tyne A, Xin Haichang, Kuppermann Nathan
2022-Nov-01
