Philip Toltzis1, Gerardo Soto-Campos, Christian R Shelton, Evelyn M Kuhn, Ryan Hahn, Robert K Kanter, Randall C Wetzel. 1. 1Division of Critical Care, Department of Pediatrics, Rainbow Babies and Children's Hospital, Cleveland, OH. 2Virtual PICU Systems LLC, Los Angeles, CA. 3National Outcomes Center, Children's Hospital of Wisconsin, Milwaukee, Wisconsin. 4Pediatric Critical Care Medicine, Department of Pediatrics, Virginia Tech Carilion School of Medicine, Roanoke, VA. 5National Center for Disaster Preparedness, Columbia University, New York, NY. 6Department of Anesthesiology Critical Care Medicine, Children's Hospital of Los Angeles, Los Angeles, CA.
Abstract
OBJECTIVE: ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children. DESIGN: A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process. SETTING: One hundred ten American PICUs SUBJECTS: : One hundred fifty thousand records from the Virtual PICU database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm. CONCLUSION: An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.
OBJECTIVE: ICU resources may be overwhelmed by a mass casualty event, triggering a conversion to Crisis Standards of Care in which critical care support is diverted away from patients least likely to benefit, with the goal of improving population survival. We aimed to devise a Crisis Standards of Care triage allocation scheme specifically for children. DESIGN: A triage scheme is proposed in which patients would be divided into those requiring mechanical ventilation at PICU presentation and those not, and then each group would be evaluated for probability of death and for predicted duration of resource consumption, specifically, duration of PICU length of stay and mechanical ventilation. Children will be excluded from PICU admission if their mortality or resource utilization is predicted to exceed predetermined levels ("high risk"), or if they have a low likelihood of requiring ICU support ("low risk"). Children entered into the Virtual PICU Performance Systems database were employed to develop prediction equations to assign children to the exclusion categories using logistic and linear regression. Machine Learning provided an alternative strategy to develop a triage scheme independent from this process. SETTING: One hundred ten American PICUs SUBJECTS: : One hundred fifty thousand records from the Virtual PICU database. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The prediction equations for probability of death had an area under the receiver operating characteristic curve more than 0.87. The prediction equation for belonging to the low-risk category had lower discrimination. R for the prediction equations for PICU length of stay and days of mechanical ventilation ranged from 0.10 to 0.18. Machine learning recommended initially dividing children into those mechanically ventilated versus those not and had strong predictive power for mortality, thus independently verifying the triage sequence and broadly verifying the algorithm. CONCLUSION: An evidence-based predictive tool for children is presented to guide resource allocation during Crisis Standards of Care, potentially improving population outcomes by selecting patients likely to benefit from short-duration ICU interventions.
Authors: Elizabeth Y Killien; Brianna Mills; Nicole A Errett; Vicki Sakata; Monica S Vavilala; Frederick P Rivara; Niranjan Kissoon; Mary A King Journal: Pediatr Crit Care Med Date: 2020-08 Impact factor: 3.971