[Year:2019] [Month:October-December] [Volume:14] [Number:4] [Pages:6] [Pages No:61 - 66]
Introduction: A number of screening questionnaires and clinical screening models have been developed to identify patients with obstructive sleep apnea syndrome (OSAS). These questionnaires lack reliability, and their predictability varies. Hence, it is difficult to predict or rule out OSAS on one questionnaire alone. The combination of two or more questionnaires might be helpful in ruling out OSAS. Objectives: (1) To determine the sensitivity, specificity, and predictive values of combination of two or more sleep questionnaires out of three established sleep questionnaires, i.e., Epworth sleepiness scale (ESS), perioperative sleep apnea prediction score (PSAP), STOP-Bang, in predicting OSAS and correlation with severity of OSAS. (2) To compare and correlate ESS, PSAP, and STOP-Bang individually with apnea–hypopnea index (AHI) obtained by polysomnography (PSG). Materials and methods: It was a prospective observational study conducted in a tertiary care center from January 2018 to August 2019 involving 250 cases of suspected OSAS. All the participants were interviewed for the three questionnaires followed by diagnostic type I PSG. Results: Comparing the individual questionnaires, ESS had higher sensitivity but low specificity, whereas PSAP had higher specificity. Perioperative sleep apnea prediction [area under curve (AUC) = 0.743 for any OSAS and 0.722 for moderate-to-severe OSAS] had a better prediction for OSAS. For predicting any OSAS, the combination of STOP-Bang + ESS + PSAP had a sensitivity of 95.76, specificity of 24.59%, and high negative predictive value (NPV) of 65.22%. For predicting moderate-to-severe OSAS, the combination of STOP-Bang + ESS + PSAP had a sensitivity of 92.59, specificity of 36.06%, and high NPV of 61.11%. Conclusion: The combination of questionnaires especially STOP-Bang, ESS, and PSAP improves the sensitivity of detection up to 95%, and when all of them are negative, OSAS is ruled out with around 65% confidence. So, using this combination can help us to identify high-risk patients and prioritize them for PSG so that they can get early treatment.