Abstract
Vital signs are frequently used in pediatric prehospital assessments and guide protocol utilization. Common pediatric vital sign classification criteria identify >80% of children in the prehospital setting as having abnormal vital signs, though few receive lifesaving interventions (LSIs). We sought to identify data-driven thresholds for abnormal vital signs by evaluating their association with prehospital LSIs. We evaluated prehospital care records for children (<18 years) transported to the hospital during 2022 from a large, national repository of emergency medical services (EMS) patient encounters. Predictors of interest were heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), and pulse oximetry. HR, RR, and SBP were converted to Z-scores using age-based distributional models. Our outcome was potential LSIs, defined as performance of selected respiratory procedures, resuscitative interventions, or medication administrations. Using cut point analysis, we identified higher specificity (maximal specificity with a minimum of 25% sensitivity) and higher sensitivity (maximal sensitivity with a minimum of 25% specificity) ranges for each vital sign and evaluated measures of diagnostic accuracy. We included 987,515 children (median age 10 years, IQR 2-15 years). An LSI occurred in 4.3% (2.1% with respiratory procedures, 1.2% with resuscitative interventions, and 2.0% with medication administration). HR, RR, and SBP demonstrated a U-shaped association with LSIs. Specificities ranged from 84.1% to 93.7% for higher specificity criteria, with RR demonstrating the best performance (sensitivity 84.6%, specificity 27.0%). Sensitivities ranged from 62.3% to 84.4% for higher sensitivity criteria. Cut points for pediatric vital signs were associated with LSIs. Specific age-adjusted ranges can identify children at higher and lower risk for receipt of LSI. These ranges may be combined with other objective measures to improve the assessment of children in the prehospital setting, assist in optimizing protocol utilization, improve transport decision making, and guide destination selection.
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