Background: The differential diagnosis of asthma and chronic obstructive pulmonary disease (COPD) poses a challenge in clinical practice and its misdiagnosis results in inappropriate treatment, increased exacerbations, and potentially death.
Objective: To investigate the diagnostic accuracy of the Asthma/COPD Differentiation Classification (AC/DC) tool compared with primary care physicians and pulmonologists in asthma, COPD, and asthma-COPD overlap.
Methods: The AC/DC machine learning-based diagnostic tool was developed using 12 parameters from electronic health records of more than 400,000 patients aged 35 years and older. An expert panel of three pulmonologists and four general practitioners from five countries evaluated 119 patient cases from a prospective observational study and provided a confirmed diagnosis (n = 116) of asthma (n = 53), COPD (n = 43), asthma-COPD overlap (n = 7), or other (n = 13). Cases were then reviewed by 180 primary care physicians and 180 pulmonologists from nine countries and by the AC/DC tool, and diagnostic accuracies were compared with reference to the expert panel diagnoses.
Results: Average diagnostic accuracy of the AC/DC tool was superior to that of primary care physicians (median difference, 24%; 95% posterior credible interval: 17% to 29%; P < .0001) and was noninferior and superior (median difference, 12%; 95% posterior credible interval: 6% to 17%; P < .0001 for noninferiority and P = .0006 for superiority) to that of pulmonologists. Average diagnostic accuracies were 73%, 50%, and 61% by AC/DC tool, primary care physicians, and pulmonologists versus expert panel diagnosis, respectively. Conclusion: The AC/DC tool demonstrated superior diagnostic accuracy compared with primary care physicians and pulmonologists in the diagnosis of asthma and COPD in patients aged 35 years and greater and has the potential to support physicians in the diagnosis of these conditions in clinical practice.
|Number of pages||15|
|Journal||The Journal of Allergy and Clinical Immunology: In Practice|
|Early online date||28 Jan 2023|
|Publication status||Published - May 2023|
The study was funded by Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States.
The studies were funded by Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States. Under the direction of authors, Rabi Panigrahy, Preethi B and Ian Wright (professional medical writers; Novartis) assisted in the preparation of this article in accordance with the third edition of Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3)
Data Availability StatementNovartis is committed to sharing access to patient-level data and supporting documents from eligible studies with qualified external researchers. These requests are reviewed and approved by an independent review panel on the basis of scientific merit. All data provided are anonymized to respect the privacy of patients who have participated in the trial in line with applicable laws and regulations. The authors contributed to the preparation of the manuscript draft, along with critical review and approval of manuscript for submission to the journal. All authors contributed to intellectual content of the manuscript and approved for publication. Under the direction of authors Rabi Panigrahy, Preethi B and Ian Wright (professional medical writers; Novartis) assisted in the preparation of this article in accordance with the third edition of Good Publication Practice guidelines (http://www.ismpp.org/gpp3).
- Differential diagnosis
- machine learning
- AC/DC tool
- asthma-COPD overlap
- primary care physician