Diagnosing Asthma with and without Aerosol-Generating Procedures

Abstract

Asthma diagnostic guidelines require procedures with aerosol-generating potential (aerosol-generating procedures [AGPs]) to guide decision making. Restricted access to AGPs poses significant challenges in primary care and resource-poor countries, further amplified during the coronavirus disease 2019 pandemic. To establish an approach to asthma diagnosis that does not require AGPs. Symptomatic yet untreated (beyond as-required bronchodilator use) adults with clinician-suspected asthma and maximum 10 pack year smoking history were recruited. Clinical history, physical examination, spirometry with bronchodilator reversibility, home peak flow monitoring, and bronchial challenges were performed, and fractional exhaled nitric oxide and serum eosinophils measured. Tests were then repeated following treatment with inhaled corticosteroids before an asthma diagnosis was confirmed or refuted by an expert panel. A total of 65 adults (mean age, 34.8 ± 12.2 years) were recruited. Five were excluded as "unclassifiable," because of borderline results or missing data. Of the remainder, 36 were diagnosed with asthma and 24 were not. Using data from non-AGPs only (wheeze on auscultation and blood eosinophilia) and home peak flow variability, a "rule-in" diagnostic model provided comparable discriminative ability to the application of established guidelines. Clinical suspicion of asthma together with at least 1 positive non-AGP test result provided a sensitivity of 55%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 60%. Application of this model reduced the need for spirometry-based tests by one-third. The proposed diagnostic algorithm may be clinically useful in "ruling-in" asthma in adults when access to AGPs is limited. This algorithm is not suitable for those with low clinical probability, with a significant smoking history, or where alternative diagnoses are more likely. This pragmatic approach to asthma diagnosis merits prospective validation.