Chest
Clinical InvestigationsSleep and BreathingNeural Network Prediction of Obstructive Sleep Apnea From Clinical Criteria
Section snippets
Materials and Methods
The clinical data were collected from a retrospective review of randomly selected patients who presented to the London Health Sciences Center Sleep Clinic for assessment of possible OSA and who went on to have PSG. Greater than 95% of patients referred for possible OSA have PSG performed in their work-up. Reasons for not going on to PSG included a clinical diagnosis other than OSA (eg, insomnia or sleep-related laryngospasm) and patient refusal. Excluded from the study were patients < 16 years
Results
The prevalence of OSA (defined as AHI ≥ 10) in this series was 69% (53% in women and 76% in men). The demographic and clinical features of the study population are outlined in Table 1. There were more men than women in the data set, and on average the patients were middle aged and overweight. The prevalence of OSA in the training set and the test set were 72% and 65%, respectively (p > 0.05). There were small differences between the training set and the test set. The subjects in the test set
Discussion
We have shown in this retrospective study of a population of OSA patients that a trained GRNN can accurately diagnose the presence of OSA (defined as AHI ≥ 10/h). To our knowledge, this study is the first use of a neural network to predict the presence or absence of OSA. The prevalence of OSA in this population was 69%, with OSA more common in men than in women. In the overall group, the presence of OSA was associated with a history of witnessed apneas, observed choking, and increased smoking
Conclusion
This study has shown that an optimally trained GRNN can accurately rule out OSA in a referral setting where the prevalence of OSA is high (ie, 69%). Also, the neural network did not miss important cases of OSA. This neural network was developed from readily available clinical data found in most patient charts. Approximately 32% of all PSG studies could have been avoided by using this classification tool. Training with increased numbers of subjects can be anticipated to only improve network
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Presented in part at the Meeting of the Eastern Section of the American Laryngological, Rhinological and Otological Society Inc., New York, NY, January, 1998.