Diagnostic performance of a machine-learning algorithm (Asthma/COPD Differentiation Classification; AC/DC) tool versus primary care physicians and pulmonologists in asthma, COPD and ACO

Janwillem W.H. Kocks, Hui Cao, Björn Holzhauer, Alan Kaplan, J. Mark FitzGerald, Konstantinos Kostikas, David Price, Helen K. Reddel, Ioanna Tsiligianni, Claus F Vogelmeier, Sebastien Bostel, Paul Mastoridis* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Differential diagnosis of asthma and chronic obstructive pulmonary disease (COPD) poses a challenge in clinical practice, and their misdiagnosis results in inappropriate treatment, increased exacerbations and potentially even 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 (ACO).
Methods: The AC/DC machine learning-based diagnostic tool was developed using 12 parameters from electronic health records of >400,000 patients aged ≥35 years. An expert 98 panel of 3 pulmonologists and 4 general practitioners from 5 countries evaluated 119 patient cases from a prospective observational study and provided a confirmed diagnosis (n=116) of asthma (n=53), COPD (n=43), ACO (n=7) or other (n=13). The cases were then reviewed by 180 primary care physicians and 180 pulmonologists from 9 countries and by 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 primary care physicians (median difference, 24%; 95% posterior credible interval [CrI]: 17–29%; PPdiagnostic 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 diagnosis of asthma and COPD in patients aged ≥35 years and has the potential to support physicians in the diagnosis of these conditions in clinical practice.
Original languageEnglish
JournalThe Journal of Allergy and Clinical Immunology: In Practice
Publication statusAccepted/In press - 11 Jan 2023

Keywords

  • Asthma
  • COPD
  • Differential diagnosis
  • machine learning
  • AC/DC tool
  • asthma-COPD overlap
  • primary care physician
  • Pulmonologist
  • Accuracy

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