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Integration of Single-Center Data-Driven Vital Sign Parameters into a Modified Pediatric Early Warning System
Catherine E Ross1, Iliana J Harrysson, Veena V Goel
11Division of Medicine Critical Care, Department of Medicine, Boston Children's Hospital, Boston, MA. 2Department of Pediatrics, Santa Clara Valley Medical Center, San Jose, CA. 3Division of Pediatric Hospital Medicine, Department of Pediatrics, Palo Alto Medical Foundation, Sutter Health, Palo Alto, CA. 4Biomedical Informatics, Stanford University School of Medicine, Stanford, CA. 5Statistical Unit, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. 6Division of Critical Care Medicine, Department of Pediatrics, University of California, San Francisco, San Francisco, CA. 7Division of Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA. 8Department of Clinical Informatics, Stanford Children's Health, Stanford, CA. 9Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA.
Objectives:
Pediatric early warning systems using expert-derived vital sign parameters demonstrate limited sensitivity and specificity in identifying deterioration. We hypothesized that modified tools using data-driven vital sign parameters would improve the performance of a validated tool.
Design:
Retrospective case control.
Setting:
Quaternary-care children's hospital.
Patients:
Hospitalized, noncritically ill patients less than 18 years old. Cases were defined as patients who experienced an emergent transfer to an ICU or out-of-ICU cardiac arrest. Controls were patients who never required intensive care. Cases and controls were split into training and testing groups.
Interventions:
The Bedside Pediatric Early Warning System was modified by integrating data-driven heart rate and respiratory rate parameters (modified Bedside Pediatric Early Warning System 1 and 2). Modified Bedside Pediatric Early Warning System 1 used the 10th and 90th percentiles as normal parameters, whereas modified Bedside Pediatric Early Warning System 2 used fifth and 95th percentiles.
Measurements And Main Results:
The training set consisted of 358 case events and 1,830 controls; the testing set had 331 case events and 1,215 controls. In the sensitivity analysis, 207 of the 331 testing set cases (62.5%) were predicted by the original tool versus 206 (62.2%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 191 (57.7%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. For specificity, 1,005 of the 1,215 testing set control patients (82.7%) were identified by original Bedside Pediatric Early Warning System versus 1,013 (83.1%; p = 0.54) with modified Bedside Pediatric Early Warning System 1 and 1,055 (86.8%; p < 0.001) with modified Bedside Pediatric Early Warning System 2. There was no net gain in sensitivity and specificity using either of the modified Bedside Pediatric Early Warning System tools.
Conclusions:
Integration of data-driven vital sign parameters into a validated pediatric early warning system did not significantly impact sensitivity or specificity, and all the tools showed lower than desired sensitivity and specificity at a single cutoff point. Future work is needed to develop an objective tool that can more accurately predict pediatric decompensation.
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