Sleep questionnaires in screening for obstructive sleep apnoea

Introduction: Awareness of the high prevalence of obstructive sleep apnoea (OSA) coupled with the dramatic proportion of undiagnosed individuals has been motivating research in the past two decades for elaborating sleep questionnaires that could help in early detection of OSA. This work aims to assess the predictive value of a subset of features that are used in the STOP-BANG questionnaire (BMI, age, gender, neck size) through a rigorous statistical analysis. Methods: A clinical database of 856 individuals referred to the sleep clinic for polysomnography was used to estimate the predictive value of the individual and combined demographic features in identifying OSA individuals. Four of the eight STOP-BANG questionnaire features were available in this database. These features were combined using a logistic regression model. The data were divided into train (80%) and test set (20%). Results: Results on the test set were 83.3% sensitivity, 45.8% specificity, 62.2% positive predictive value, 71.7% negative predictive value. The area under the curve (AUC) had a mean of 0.717. The results showed that combining the available subset of STOP-BANG questionnaire features gave similar performance to the full STOP-BANG questionnaire as reported in a number of studies. This highlights that combining features using a machine learning framework may improve the STOP-BANG questionnaire prediction. Alternatively, it may indicate that there are redundant or uninformative questions in the STOP-BANG questionnaire.