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.
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.
Authors: Robert J Farney; Brandon S Walker; Robert M Farney; Gregory L Snow; James M Walker Journal: J Clin Sleep Med Date: 2011-10-15 Impact factor: 4.062
Authors: Nathaniel A Eiseman; M Brandon Westover; Joseph E Mietus; Robert J Thomas; Matt T Bianchi Journal: J Sleep Res Date: 2011-07-14 Impact factor: 3.981
Authors: Rahel A Teferra; Brydon J B Grant; Jesse W Mindel; Tauseef A Siddiqi; Imran H Iftikhar; Fatima Ajaz; Jose P Aliling; Meena S Khan; Stephen P Hoffmann; Ulysses J Magalang Journal: Ann Am Thorac Soc Date: 2014-09
Authors: J Víctor Marcos; Roberto Hornero; Daniel Alvarez; Félix del Campo; Miguel López; Carlos Zamarrón Journal: Med Biol Eng Comput Date: 2007-10-30 Impact factor: 2.602