A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea Hypopnea Syndrome

Background: Obstructive sleep apnea hypopnea syndrome (OSAHS) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSAHS patients will improve the quality of patient management and prognosis, while the accuracy and feasibility of existing screening tools are not so satisfactory. The purpose of this study is to develop and validate a well-feasible clinical predictive model for screening potential severe OSAHS patients. Methods: We performed a retrospective cohort study including 1,920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSAHS. Based on demography, symptoms, and hematological data, a multivariate logistic regression model was constructed and cross-validated and then a nomogram was developed to identify severe OSAHS. Moreover, we compared the performance of our model with two most commonly used screening tools, Epworth sleepiness scale (ESS) and Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires.Results: Severe OSAHS was associated with male, BMI≥ 27 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥ 9.5×109/L, hemoglobin ≥ 175g/L, triglycerides ≥ 1.7 mmol/L. The AUC of the final model was 0.75 (95% CI: 0.67-0.82), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 71% and 68%. Among patients having the information of ESS or SBQ, the AUC of our model was statistically significantly greater than that of ESS (0.74 vs 0.58, P=0.006) and not less than that of SBQ (0.78 vs 0.66, P=0.073). Conclusion: Based on common clinical examination on admission, we develop a novel model and a nomogram for identifying severe OSAHS from inpatient with suspected OSAHS, which provides physicians with a visual and easy-to-use tool for screening severe OSAHS.