A novel algorithm to predict oxygen desaturation in sedated patients with obstructive sleep apnea utilizing polysomnography: A STROBE-compliant article. Issue 34 (21st August 2020) - Chiang, Min-Hsien; Luo, Sheng-Dean; Lin, Hsin-Ching; Hou, Shao-Yun; Ke, Ting-Yu; Chen, Chih-Chun; Hung, Kuo-Chuan; Wu, Shao-Chun
Abstract : Abstract: This retrospective study aimed at identifying the predictors of oxygen desaturation (OD) (i.e., SpO2 < 95%) in patients with obstructive sleep apnea (OSA) requiring deep sedation and developing an algorithm to predict OD. We studied 66 OSA patients undergoing propofol-induced deep sedation for drug-induced sleep endoscopy (DISE). The patients were divided into prediction (n = 35) and validation (n = 31) groups. Patient characteristics and polysomnographic parameters were analyzed with receiver operating characteristic curve and Chi-squared test to identify significant predictors of OD for developing an algorithm in the prediction group. The predictive accuracy, sensitivity, positive predictive value, and negative predictive value of the algorithm were determined in the validation group. Six polysomnographic predictors of OD were identified, including Apnea-Hypopnea Index of total sleep time (AHI-TST), AHI at the stage of rapid eye movement (AHI-REM), percentage of time with oxygen saturation <90% (mO2 < 90%), average SpO2, lowest SpO2, and desaturation index. Stepwise multiple logistic regression analysis demonstrated that low average SpO2 (<95.05%) and high AHI-REM (>16.5 events/h) were independent predictors of OD. The algorithm thus developed showed that patients with an average SpO2 < 95.05% and those with an average SpO2 ≥ 95.05% together with an AHI-REM > 16.5 events/h would be at risk of OD under sedation. The predictive accuracy, sensitivity, positive predictive value, and negative predictive value were 84%, 100%, 83%, 100%, respectively. For patients with OSA, average SpO2 and AHI-REM may enable clinicians to predict the occurrence of oxygen desaturation under deep sedation. Future large-scale studies are needed to validate the findings.