Background Obstructive sleep apnoea (OSA) has been previously reported as a major risk factor for perioperative adverse events. 1 Identifying patients with undiagnosed OSA can potentially have an impact on co morbidities and hospitalisation costs.2
Aim To validate a previously reported screening tool for surgical patients suspected of having OSA.
Method A prospective study was performed in a university hospital between 1st Dec 2013 and 1st June 2014. An easy to use screening tool (STOP BANG) has been addressed to all patients prior to overnight oximetry sleep study during chest clinic assessment. The STOP BANG questionnaire incorporated 8 questions related to Snoring, Tiredness, Observed apnoeas, high blood Pressure, BMI >30 kg/m2, Age >50, Neck size >15” and male Gender. Each affirmative answer was marked with 1 point. OSA was defined as dip rate ≥ 10 events per hour associated with an oxygen desaturation ≥ 4% below baseline value.
Results A total of 102 patients have been included, 57 males (55.8%) and average age 50.8 ± 14 years. 52 patients (50.9%) have been diagnosed with OSA out of which 29 patients (28.4%) had severe OSA (defined as dip rate ≥30 events per hour).
Using logistic regression analysis, a STOP BANG score of ≥ 3 had a sensitivity of 94.2% and specificity 72% with a positive predictive value of 77.8% and a negative predictive value of 92.3% in detecting OSA patients.
Conclusion We have identified a high incidence of OSA of 50.9% in our sleep study population. We have validated STOP BANG questionnaire to be a useful predictor of OSA with a sensitivity of 94.2% and specificity of 72%. This can be used during pre anaesthetic assessment indicating the requirement of chest clinic referral for sleep study at a score of ≥3.
Kaw, R, Chung, F, Pasupuleti, et al. Meta-analysis of the association between obstructive sleep apnoea and postoperative outcome. Br J Anaesth, 2012;109, 6:897-906
Mokhlesi, B, Hovda, MD, Vekhter, B, et al. Sleep-disordered breathing and postoperative outcomes after elective surgery: analysis of the nationwide inpatient sample. Chest, 2013;144, 3:903-14