STUDY OBJECTIVE: We sought to create a clinical decision rule, on the basis of variables available to out-of-hospital personnel, that could be used to accurately predict severe injury in pediatric patients involved in motor vehicle crashes as occupants. METHODS: We analyzed the National Automotive Sampling System database, a national probability sample, using pediatric patients up to 15 years old (occupants only) involved in motor vehicle crashes from January 1993 to December 1999. The National Automotive Sampling System database includes patients from regions throughout the country, weighted to represent a nationwide sample. Twelve out-of-hospital variables were used in classification and regression tree analysis to create a decision rule separating children with severe injuries (Injury Severity Score [ISS] > or =16) from those with minor injuries (ISS < 16). Misclassification costs and complexity parameters were selected to yield a decision tree with appropriate sensitivity and specificity for the identification of severely injured patients, while also being simple and practical for out-of-hospital use. Probability weights were used throughout the analysis to account for the sampling design and sampling weights. RESULTS: Using a sample size of 8,392 children, we constructed a decision rule using 3 out-of-hospital variables (Glasgow Coma Scale score, passenger space intrusion > or =6 in [> or =15 cm], and restraint use) to predict those patients with an ISS of 16 or more. Internal cross-validation was used to determine the sensitivity and specificity, yielding values of 92% and 73%, respectively, for the prediction of patients with an ISS of 16 or more. CONCLUSION: Out-of-hospital variables available to field personnel could be used to effectively triage pediatric motor vehicle crash patients using the decision rule developed here. Prospective trials would be needed to test this decision rule in actual use.
STUDY OBJECTIVE: We sought to create a clinical decision rule, on the basis of variables available to out-of-hospital personnel, that could be used to accurately predict severe injury in pediatric patients involved in motor vehicle crashes as occupants. METHODS: We analyzed the National Automotive Sampling System database, a national probability sample, using pediatric patients up to 15 years old (occupants only) involved in motor vehicle crashes from January 1993 to December 1999. The National Automotive Sampling System database includes patients from regions throughout the country, weighted to represent a nationwide sample. Twelve out-of-hospital variables were used in classification and regression tree analysis to create a decision rule separating children with severe injuries (Injury Severity Score [ISS] > or =16) from those with minor injuries (ISS < 16). Misclassification costs and complexity parameters were selected to yield a decision tree with appropriate sensitivity and specificity for the identification of severely injured patients, while also being simple and practical for out-of-hospital use. Probability weights were used throughout the analysis to account for the sampling design and sampling weights. RESULTS: Using a sample size of 8,392 children, we constructed a decision rule using 3 out-of-hospital variables (Glasgow Coma Scale score, passenger space intrusion > or =6 in [> or =15 cm], and restraint use) to predict those patients with an ISS of 16 or more. Internal cross-validation was used to determine the sensitivity and specificity, yielding values of 92% and 73%, respectively, for the prediction of patients with an ISS of 16 or more. CONCLUSION: Out-of-hospital variables available to field personnel could be used to effectively triage pediatric motor vehicle crashpatients using the decision rule developed here. Prospective trials would be needed to test this decision rule in actual use.
Authors: Etienne St-Louis; Jade Séguin; Daniel Roizblatt; Dan Leon Deckelbaum; Robert Baird; Tarek Razek Journal: Pediatr Surg Int Date: 2016-11-21 Impact factor: 1.827
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Authors: Momar Ndao; Terry W Spithill; Rebecca Caffrey; Hongshan Li; Vladimir N Podust; Regis Perichon; Cynthia Santamaria; Alberto Ache; Mark Duncan; Malcolm R Powell; Brian J Ward Journal: J Clin Microbiol Date: 2010-01-13 Impact factor: 5.948
Authors: R Andrew Taylor; Joseph R Pare; Arjun K Venkatesh; Hani Mowafi; Edward R Melnick; William Fleischman; M Kennedy Hall Journal: Acad Emerg Med Date: 2016-02-13 Impact factor: 3.451
Authors: Craig D Newgard; Peter E Fischer; Mark Gestring; Holly N Michaels; Gregory J Jurkovich; E Brooke Lerner; Mary E Fallat; Theodore R Delbridge; Joshua B Brown; Eileen M Bulger Journal: J Trauma Acute Care Surg Date: 2022-04-27 Impact factor: 3.697