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Journal Article

Citation

Graf WD, Cummings P, Quan L, Brutocao D. Ann. Emerg. Med. 1995; 26(3): 312-319.

Affiliation

Department of Pediatrics, Children's Hospital and Medical Center, University of Washington School of Medicine, USA.

Copyright

(Copyright © 1995, American College of Emergency Physicians, Publisher Elsevier Publishing)

DOI

unavailable

PMID

7661421

Abstract

STUDY OBJECTIVE: To predict outcome in children after near-drowning. DESIGN: Retrospective cohort study. Vegetative state and death were classified as unfavorable outcomes, whereas all other outcomes were classified as favorable. Demographic, episode-related, clinical, laboratory, and treatment variables available at the time of admission were evaluated for their usefulness in predicting outcome. SETTING: Pediatric referral hospital. PARTICIPANTS: Children admitted after submersion injury in non-icy waters. RESULTS: The study cohort comprised 194 children (median age, 2.6 years; range, 5 months to 18 years); 131 were neurologically normal at the time of discharge, 10 had some degree of neurologic impairment, 15 were in a vegetative state at the time of discharge, and 38 died. We used a combination of partitioning and logistic regression to combine variables in a prediction rule that was always correct when unfavorable outcome was predicted. The final rule predicted favorable outcome for all children who were not comatose. Among comatose children, unfavorable outcome was predicted by a combination of absent pupillary light reflex, increased initial blood glucose concentration, and male sex. This rule had a specificity of 100%--children with favorable outcomes were always predicted to do well--and a sensitivity of 65%. Therefore the rule was overly optimistic for 35% of patients with unfavorable outcomes. CONCLUSION: Pediatric submersion victims can be assigned to high or low likelihoods of unfavorable outcome with the use of four variables: comatose state, lack of pupillary light reflex, sex, and initial blood glucose concentration. This prediction rule may be useful if it can be validated in another cohort.


Language: en

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