HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:
Guest Access | Sign In via User Name/Password

This Article Full Text Free Full Text (PDF) Free Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Add to My Personal Article Archive Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (12) Citing Articles via Google Scholar Google Scholar Articles by Kirby, S. D. Articles by Ferguson, K. A. Search for Related Content PubMed PubMed Citation Articles by Kirby, S. D. Articles by Ferguson, K. A.

Neural Network Prediction of Obstructive Sleep Apnea From Clinical Criteria^*

^* From the Departments of Otolaryngology (Drs. Kirby and George) and Medicine (Drs. Danter, Ferguson, and Francovic), The University of Western Ontario, London, Ontario, Canada.

Correspondence to: Kathleen A. Ferguson BSc, MD, FCCP, University Campus, London Health Sciences Centre, 339 Windermere Rd, London, Ontario, Canada N6A 5A5; e-mail: kafergus{at}julian.uwo.ca

Study objectives: Clinical prediction models for the diagnosis of obstructive sleep apnea (OSA) have lacked the accuracy necessary to confidently replace polysomnography (PSG). Artificial neural networks are computer programs that can be trained to predict outcomes based on experience. This study was conducted to test the hypothesis that a generalized regression neural network (GRNN) could accurately classify patients with OSA from clinical data.

Study design: Retrospective review.

Setting: Regional sleep referral center.

Patients: Randomly selected records of patients referred for possible OSA.

Measurements: The neural network was trained using 23 clinical variables from 255 patients, and the predictive performance was evaluated using 150 other patients.

Results: The prevalence of OSA in this series of 405 patients (293 men and 112 women) was 69%. The trained GRNN had an accuracy of 91.3% (95% confidence interval [CI], 86.8 to 95.8). The sensitivity was 98.9% for having OSA (95% CI, 96.7 to 100), and the specificity was 80% (95% CI, 70 to 90). The positive predictive value that the patient would have OSA was 88.1% (95% CI, 81.8 to 94.4), whereas the negative predictive value that the patient would not have OSA (if so classified) was 98% (95% CI, 94 to 100).

Conclusions: Appropriately trained GRNN has the ability to accurately rule in OSA from clinical data, and GRNN did not misclassify patients with moderate to severe OSA. In this study, use of the neural network could have reduced the number of PSG studies performed. Prospective validation of the neural network for the diagnosis of OSA is now required.

Key Words: artificial neural networks • clinical prediction models • obstructive sleep apnea • screening

This article has been cited by other articles:

				L. K. Brown Cephalometric Measurements and Sleep Apnea Hypopnea Syndrome Chest, September 1, 2002; 122(3): 765 - 768. [Full Text] [PDF]

				N. Roche, B. Herer, C. Roig, and G. Huchon Prospective Testing of Two Models Based on Clinical and Oximetric Variables for Prediction of Obstructive Sleep Apnea Chest, March 1, 2002; 121(3): 747 - 752. [Abstract] [Full Text] [PDF]