WEIGHTING OF AHI SCORES AND SYMPTOMS IN THE DIAGNOSIS OF SLEEP APNEA BY SLEEP SPECIALISTS
Method: Data of AHI scores and clinical diagnoses from 269 patients participating in the SIESTA trial were used. A composite binary variable was created summarizing symptoms of daytime sleepiness and fatigue. ROC curve analysis and logistic regression models were used for assessing how well AHI scores alone can discriminate patients and predict a diagnosis of OSA, and how much the discriminatory power increases when additional information on symptoms is considered, using methods including the Area Under the Curve (AUC), optimal cutoff and Wald χ2 tests for the model covariates. In addition, classification of patients using the recommended guidelines was compared against the actual clinical diagnosis.
Result: The AHI alone was able to discriminate the diagnosed patients with high precision (AUC=0.9702), with an optimal cutoff of 5.2. When added to the logistic regression model as covariate, the composite symptom variable created a marginal improvement (AUC=0.9721), but without being statistically significant (p=0.267). The classification based on the guidelines gave a very high positive predictive value (0.99) but an unsatisfactory negative predictive value (0.73), due to a significant number (n=17) of false negatives. Interestingly, all of these false negatives had AHI < 5 and only 4 of them had signs of daytime sleepiness. This indicates that sleep specialists assign a positive diagnosis almost always to patients with AHI ≥ 5, but occasionally also to patients with AHI < 5, which is against the recommended guidelines.
Conclusion: Our analysis has shown that an AHI score ≥5 alone predicts very accurately the clinical diagnosis of OSA by sleep specialists. However, it also demonstrates that some patients are diagnosed as having OSA using criteria other than the AHI and symptoms of daytime sleepiness. Additional investigation is needed.