Chronic obstructive lung disease “expert system”: validation of a predictive tool for assisting diagnosis
Received 14 February 2018
Accepted for publication 5 April 2018
Published 28 May 2018 Volume 2018:13 Pages 1747—1753
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Charles Downs
Peer reviewer comments 2
Editor who approved publication: Dr Richard Russell
Fulvio Braido,1 Pierachille Santus,2 Angelo Guido Corsico,3 Fabiano Di Marco,4 Giovanni Melioli,5 Nicola Scichilone,6 Paolo Solidoro7
1Department of Internal Medicine, IRCCS San Martino di Genova University Hospital, Genoa, Italy; 2Department of Biomedical and Clinical Sciences, University of Milan, Division of Respiratory Diseases, “L. Sacco” University Hospital, ASST Fatebenefratelli-Sacco, Milan, Italy; 3Department of Internal Medicine and Therapeutics, Division of Respiratory Diseases, IRCCS Policlinico San Matteo Foundation, University of Pavia, Italy; 4Department of Health Sciences, University of Milan, San Paolo Hospital, Milan, Italy; 5Center for Precision Medicine, Asthma, and Allergy, Humanitas University, Milan, Italy; 6Department of Internal Medicine, University of Palermo, Palermo, Italy; 7Unit of Pulmonology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, Torino, Italy
Purpose: The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD).
Methods: A questionnaire and a WebFlex code were developed and validated in silico. An expert panel pilot validation on 60 cases and a clinical validation on 241 cases were performed.
Results: The developed questionnaire and code validated in silico resulted in a suitable tool to support the medical diagnosis. The clinical validation of the ES was performed in an academic setting that included six different reference centers for respiratory diseases. The results of the ES expressed as a score associated with the risk of suffering from COLD were matched and compared with the final clinical diagnoses. A set of 60 patients were evaluated by a pilot expert panel validation with the aim of calculating the sample size for the clinical validation study. The concordance analysis between these preliminary ES scores and diagnoses performed by the experts indicated that the accuracy was 94.7% when both experts and the system confirmed the COLD diagnosis and 86.3% when COLD was excluded. Based on these results, the sample size of the validation set was established in 240 patients. The clinical validation, performed on 241 patients, resulted in ES accuracy of 97.5%, with confirmed COLD diagnosis in 53.6% of the cases and excluded COLD diagnosis in 32% of the cases. In 11.2% of cases, a diagnosis of COLD was made by the experts, although the imaging results showed a potential concomitant disorder.
Conclusion: The ES presented here (COLDES) is a safe and robust supporting tool for COLD diagnosis in primary care settings.
Keywords: chronic obstructive lung diseases, expert systems, diagnosis
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