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.