Developing a risk stratification model for predicting future health care use in asthmatic children

Ann Allergy Asthma Immunol. 2016 Jan;116(1):26-30. doi: 10.1016/j.anai.2015.10.014. Epub 2015 Nov 6.

Abstract

Background: Previous studies have stratified pediatric asthma patients for risk of future exacerbation and/or health care use, but most incorporate multiple clinical parameters.

Objective: To determine whether historical acute care visits (ACVs) alone could predict risk of future health care use.

Methods: Children seen for asthma in an outpatient visit during a 3-year period were identified. The number of ACVs in the 12 months before and after the outpatient visit was determined. Logistic regression models were used to determine the odds of a future ACV. Models were adjusted for age, sex, race, and insurance status.

Results: Of 28,047 outpatient visits, 21,099 (75.2%) had no historical ACVs. The probability of a future ACV increased from 30% with one historical ACV to 87% with 5 or more historical ACVs. Outpatient visits with one historical ACV had significantly higher odds of a future ACV compared with those with no historical ACVs (adjusted odds ratio [OR], 3.60; 95% confidence interval [CI], 3.14-4.12; P < .001). The OR increased with each additional historical ACV to an adjusted OR of 58.71 (95% CI, 24.34-141.61; P < .001) with 5 or more historical ACVs. Outpatient visits with 5 or more historical ACVs represented only 1.1% of the study sample but accounted for a higher mean number of future ACVs.

Conclusion: The historical count of ACVs was predictive of future ACVs. A significant increase in the probability of future ACVs was observed with each additional historical visit, effectively stratifying risk by the historical visit count. Notably, a small group of patients accounted for a disproportionate number of future ACVs.

MeSH terms

  • Adolescent
  • Ambulatory Care / statistics & numerical data*
  • Ambulatory Care / trends
  • Asthma / epidemiology*
  • Child
  • Child, Preschool
  • Female
  • Humans
  • Male
  • Missouri / epidemiology
  • Models, Theoretical*
  • Primary Health Care
  • Regression Analysis
  • Reproducibility of Results
  • Risk