The Health Survey for England reported that 25% of UK adults are obese with a 10% rise over 15 years. Consequently, clinicians are faced with a rising number of obese patients referred for bariatric and non-bariatric surgery. Previous data indicates a 50% incidence of obstructive sleep apnoea in patients with a BMI >40 kg/m2 with obesity hypoventilation syndrome present in up to a third. These patients have higher risk of peri-operative complications. A screening tool to predict hypercapnic respiratory failure (PaCO2 >6 kPa) based on simple clinic tests would be useful. Correlations were performed to determine which tests may be useful.Methods
Data from all obese patients (BMI >30 kg/m2) with evidence of sleep-disordered breathing on oximetry initiated on home ventilatory support between August 2005 and December 2010 were obtained from a discharge summary database.Results
205 patients were included for analysis. The group mean age was 54.9 (SD 14.2) years, daytime clinic oxygen saturations (SpO2clinic) 91.0% (5.8%), FEV1 1.8 l (0.96 l), FVC 2.2 l (1.11 l), weight 132.8 kg (28.5 kg), BMI 47.6 kg/m2 (9.6) and Epworth sleepiness score 8.9 (5.6). Mean daytime PaCO2 was 6.68 kPa (1.31). Significant correlations were found between PaCO2 and BMI (r=0.20; p<0.005), FEV1% predicted (r=−0.20; p<0.005), FVC% predicted (r=−0.20; p<0.005) and SpO2clinic (r=−0.52; p<0.005). Receiver operating characteristics (ROC) analysis was used to determine the utility of SpO2clinic and FVC to predict hypercapnia. The area under the curve (AUC) for SpO2clinic was 0.81 (p<0.001); a cut-off of SpO2clinic of <92% demonstrated a sensitivity of 86% and specificity of 52% in predicting hypercapnia. The AUC for FVC was found to be 0.77 (p<0.0001); a cut-off of <1.94 l demonstrated a sensitivity of 77% and specificity of 61% in detecting hypercapnia (see Abstract P276 figure 1).Conclusion
These data have significant clinical utility for clinicians involved in providing respiratory support services for obese patients undergoing bariatric and non-bariatric surgery. In particular, it could form the foundations of a screening algorithm including simple measures such as home oximetry, spirometry and clinic pulse oximetry, to identify the highest risk patients that need to be reviewed by sleep and ventilation clinicians.