A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea

• Published in: Nature and science of sleep 2023-10, Vol.15, p.839-850
• Main Authors: Ye, Yanqing, Yan, Ze-Lin, Huang, Yuanshou, Li, Li, Wang, Shiming, Huang, Xiaoxing, Zhou, Jingmeng, Chen, Liyi, Ou, Chun-Quan, Chen, Huaihong
• Format: Article
• Language: English
• Subjects: Blood cell count; clinical prediction model; Glycosylated hemoglobin; Health aspects; Hypertension; Medical screening; nomogram; Original Research; Prognosis; severe obstructive sleep apnea; Sleep apnea syndromes; Triglycerides
• ISSN: 1179-1608; 1179-1608
• DOI: 10.2147/NSS.S418093

Purpose: Obstructive sleep apnea (OSA) is a disease with high morbidity and is associated with adverse health outcomes. Screening potential severe OSA 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 OSA patients. Patients and Methods: We performed a retrospective cohort study including 1920 adults with overnight polysomnography among which 979 cases were diagnosed with severe OSA. 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 OSA. Moreover, we compared the performance of our model with the most commonly used screening tool, Stop-Bang Questionnaire (SBQ), among patients who completed the questionnaires. Results: Severe OSA was associated with male, BMI[greater than or equal to] 28 kg/[m.sup.2], high blood pressure, choke, sleepiness, apnea, white blood cell count [greater than or equal to]9.5 x [10.sup.9]/L, hemoglobin [greater than or equal to]175g/L, triglycerides [greater than or equal to]1.7 mmol/L. The AUC of the final model was 0.76 (95% CI: 0.74-0.78), with sensitivity and specificity under the optimal threshold selected by maximizing Youden Index of 73% and 66%. Among patients having the information of SBQ, the AUC of our model was statistically significantly greater than that of SBQ (0.78 vs 0.66, P = 0.002). Conclusion: Based on common clinical examination of admission, we develop a novel model and a nomogram for identifying severe OSA from inpatient with suspected OSA, which provides physicians with a visual and easy-to-use tool for screening severe OSA. Plain Language Summary: Question: How to build a more efficient screening model for severe OSA using some common variables for community physicians or non-sleep physicians? Findings: It was found that severe OSA was associated with male, BMI[greater than or equal to] 28 kg/[m.sup.2], high blood pressure, choke, sleepiness, apnea, white blood cell count[greater than or equal to]9.5 x [10.sup.9]/L, hemoglobin [greater than or equal to]175g/L, and triglycerides [greater than or equal to]1.7 mmol/L. The nomogram based on these variables was developed and validated. It seemed that our model outperformed the SBQ. Meaning: A clinicall