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Individual risk assessment tool for school-age asthma prediction in UK birth cohort

Ran Wang1, Angela Simpson1, Adnan Custovic2

  • 1Division of Infection Immunity and Respiratory Medicine, School of Biological Sciences, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, The University of Manchester, Manchester, UK.

Clinical and Experimental Allergy : Journal of the British Society for Allergy and Clinical Immunology
|November 18, 2018
PubMed

Insights

A new asthma prediction tool (MAAS APT) identifies children at high risk for school-age asthma using simple factors identified at age 3. This tool aids in early asthma risk assessment for children.

Area of Science:

  • Pediatric Pulmonology
  • Epidemiology
  • Clinical Prediction Modeling

Background:

  • Existing asthma prediction tools have limitations in clinical utility due to moderate positive and high negative likelihood ratios.
  • There is a need for a simple, clinically applicable asthma prediction tool for use in population-based birth cohorts.

Purpose of the Study:

  • To develop and validate a straightforward asthma prediction tool for use in clinical and research settings.
  • To identify key risk factors for predicting school-age asthma in children from a birth cohort.

Main Methods:

  • Utilized data from the Manchester Asthma and Allergy Study (MAAS) birth cohort, including preschool wheeze, questionnaires, and skin prick testing (SPT).
  • Developed the Manchester Asthma and Allergy Study Asthma Prediction Tool (MAAS APT) using logistic regression on 3-year-old data to predict asthma at school-age (8/11 years).
  • Defined school-age asthma based on physician diagnosis, wheeze, or frequent wheezing attacks.

Main Results:

  • The MAAS APT identified 5 significant risk factors: wheeze after exercise, wheeze causing breathlessness, cough on exertion, current eczema, and SPT sensitization.
  • Children scoring ≥3 on the MAAS APT (maximum score 5) had a high risk of school-age asthma (positive predictive value >75%, +LR 6.3).
  • Children with a score of 0 had a very low risk (LR 0.2), indicating good discriminative ability.

Conclusions:

  • The MAAS APT is a simple and effective tool for predicting school-age asthma.
  • This prediction tool can be readily implemented in both clinical practice and research environments for early asthma risk assessment.
Abstract

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