Sleep apnea prediction in a Swedish cohort : Can the STOP-Bang questionnaire be improved?

Sammanfattning: Obstructive sleep apnea (OSA) is defined as more than five breathing pauses per hour of sleep, an apnea-hypopnea index (AHI) > 5. STOP-Bang is a questionnaire that predicts the risk of sleep apnea based on risk factors, like snoring, hypertension, and throat circumference greater than 40 cm. Many individuals with OSA are undiagnosed and patients with sleep apnea have an increased risk of complications after surgery. Therefore, it is important to identify these patients. This thesis aims to create prediction models that predict the degree of sleep apnea, defined as no sleep apnea to mild sleep apnea (AHI < 15) or moderate to severe sleep apnea (AHI ≥ 15), by using different methods. The methods are Random Forests, logistic regression, and linear discriminant analysis (LDA). Beyond these three methods, the STOP-Bang questionnaire, a weighted STOP-Bang, and a modified STOP-Bang are used to predict the degree of sleep apnea. In the modified STOP-Bang, the same feature variables are used as in STOP-Bang. But the categorical feature variables are divided in a different way, and the modified STOP-Bang gives more weight to some of the feature variables. STOP-Bang models where some other feature variables are used were made to see if the prediction accuracy would be improved, SCAPIS STOP-Bang. The prediction precision is also compared for all models depending on gender. Accuracy, specificity, and sensitivity were compared for the models. For the models using the STOP-Bang feature variables, the models with the highest area under the curve (AUC), with confidence interval in parenthesis, were the LDA and the logistic regression models with an AUC of 0.81 (0.78, 0.84). The confidence intervals for the AUC, sensitivity, and accuracy were overlapping for all the models. The SCAPIS STOP-Bang model did not achieve a better prediction accuracy. For all the models, the accuracy was higher for females than for males. But also here, all the confidence intervals were overlapping.