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Diagnosing Asthma with and without Aerosol-Generating Procedures.

Sarah Drake1, Ran Wang1, Laura Healy1

  • 1Faculty of Biology, Medicine and Health, School of Biological Sciences, Division of Infection, Immunity & Respiratory Medicine, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom; NIHR Manchester Biomedical Research Unit and Manchester University NHS Foundation Trust, Manchester, United Kingdom.

The Journal of Allergy and Clinical Immunology. in Practice
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PubMed
Summary

A new asthma diagnostic approach using non-aerosol-generating procedures (non-AGPs) shows promise for adults, especially where access to standard tests is limited. This method aids in ruling in asthma without relying on potentially restricted procedures.

Keywords:
AsthmaCOVID-19Diagnosis

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Area of Science:

  • Pulmonology
  • Clinical Diagnostics
  • Respiratory Medicine

Background:

  • Asthma diagnosis guidelines often rely on aerosol-generating procedures (AGPs).
  • Limited access to AGPs presents challenges in primary care, low-resource settings, and during pandemics.
  • Existing diagnostic methods are not always feasible in all clinical environments.

Purpose of the Study:

  • To develop and evaluate an asthma diagnostic approach that avoids the need for AGPs.
  • To provide an alternative diagnostic strategy for individuals with suspected asthma when AGPs are inaccessible.
  • To assess the utility of non-AGP tests in confirming an asthma diagnosis.

Main Methods:

  • Recruited symptomatic adults with suspected asthma and limited smoking history.
  • Collected data on clinical history, physical exam, spirometry, home peak flow monitoring, fractional exhaled nitric oxide, and serum eosinophils.
  • Developed a diagnostic model using non-AGP data and validated its performance against expert panel diagnoses.

Main Results:

  • A diagnostic model utilizing wheeze on auscultation and blood eosinophilia demonstrated comparable discriminative ability to established guidelines.
  • The model achieved 55% sensitivity, 100% specificity, 100% positive predictive value, and 60% negative predictive value for ruling in asthma.
  • This approach reduced the reliance on spirometry-based tests by one-third.

Conclusions:

  • A proposed diagnostic algorithm using non-AGPs can be clinically valuable for ruling in asthma in adults with limited access to AGPs.
  • This pragmatic approach is not suitable for patients with low clinical probability, significant smoking history, or likely alternative diagnoses.
  • Prospective validation of this non-AGP diagnostic strategy is warranted for broader clinical application.