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Evaluation of the modified asthma predictive index in high-risk preschool children

  • Timothy S. Chang
  • , Robert F. Lemanske
  • , Theresa W. Guilbert
  • , James E. Gern
  • , Michael H. Coen
  • , Michael D. Evans
  • , Ronald E. Gangnon
  • , C. David Page
  • , Daniel J. Jackson

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Prediction of subsequent school-age asthma during the preschool years has proven challenging. Objective: To confirm in a post hoc analysis the predictive ability of the modified Asthma Predictive Index (mAPI) in a high-risk cohort and a theoretical unselected population. We also tested a potential mAPI modification with a 2-wheezing episode requirement (m2API) in the same populations. Methods: Subjects (n = 289) with a family history of allergy and/or asthma were used to predict asthma at age 6, 8, and 11 years with the use of characteristics collected during the first 3 years of life. The mAPI and the m2API were tested for predictive value. Results: For the mAPI and m2API, school-age asthma prediction improved from 1 to 3 years of age. The mAPI had high predictive value after a positive test (positive likelihood ratio ranging from 4.9 to 55) for asthma development at years 6, 8, and 11. Lowering the number of wheezing episodes to 2 (m2API) lowered the predictive value after a positive test (positive likelihood ratio ranging from 1.91 to 13.1) without meaningfully improving the predictive value of a negative test. Posttest probabilities for a positive mAPI reached 72% and 90% in unselected and high-risk populations, respectively. Conclusions: In a high-risk cohort, a positive mAPI greatly increased future asthma probability (eg, 30% pretest probability to 90% posttest probability) and is a preferred predictive test to the m2API. With its more favorable positive posttest probability, the mAPI can aid clinical decision making in assessing future asthma risk for preschool-age children.

Original languageEnglish (US)
Pages (from-to)152-156
Number of pages5
JournalJournal of Allergy and Clinical Immunology: In Practice
Volume1
Issue number2
DOIs
StatePublished - Mar 2013
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Asthma
  • Asthma predictive index
  • Children
  • Modified asthma predictive index
  • Wheezing

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