A validated clinical model to predict the need for admission and length of stay in children with acute bronchiolitis

Eur J Emerg Med. 2004 Oct;11(5):265-72. doi: 10.1097/00063110-200410000-00005.

Abstract

Objective: To develop and validate a logistic regression model to predict need for admission and length of hospital stay in children presenting to the Emergency Department with bronchiolitis.

Setting: Two children's hospitals in Dublin, Ireland.

Methods: We reviewed 118 episodes of bronchiolitis in 99 children admitted from the Emergency Department. Those discharged within 24 h by a consultant/attending paediatrician were retrospectively categorized as suitable for discharge. We then validated the model using a cohort of 182 affected infants from another paediatric Emergency Department in a bronchiolitis season 2 years later. In the validation phase actual admission, failed discharge, and age less than 2 months defined the need for admission.

Results: The model predicted admission with 91% sensitivity and 83% specificity in the validation cohort. Age [odds ratio (OR) 0.86, 95% confidence interval (CI) 0.76-0.97], dehydration (OR 2.54, 95% CI 1.34-4.82), increased work of breathing (OR 3.39, 95% CI 1.29-8.92) and initial heart rate above the 97th centile (OR 3.78, 95% CI 1.05-13.57) predicted the need for admission and a longer hospital stay.

Conclusion: We derived and validated a severity of illness model for bronchiolitis. This can be used for outcome prediction in decision support tools or severity of illness stratification in research/audit.

Publication types

  • Comparative Study
  • Validation Study

MeSH terms

  • Acute Disease
  • Adolescent
  • Age Distribution
  • Bronchiolitis / diagnosis*
  • Bronchiolitis / epidemiology*
  • Bronchiolitis / therapy
  • Child
  • Child, Hospitalized / statistics & numerical data*
  • Child, Preschool
  • Emergency Service, Hospital
  • Female
  • Humans
  • Incidence
  • Infant
  • Ireland / epidemiology
  • Length of Stay / statistics & numerical data*
  • Logistic Models
  • Male
  • Needs Assessment*
  • Patient Admission / statistics & numerical data*
  • Predictive Value of Tests
  • Prognosis
  • Retrospective Studies
  • Risk Assessment
  • Severity of Illness Index
  • Sex Distribution