A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea

Abstract

Yanqing Ye,1,2,* Ze-Lin Yan,3,* Yuanshou Huang,2,* Li Li,3 Shiming Wang,1 Xiaoxing Huang,1 Jingmeng Zhou,1 Liyi Chen,4 Chun-Quan Ou,3 Huaihong Chen1 1Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 2Otolaryngology Department, Foshan Nan Hai District People’s Hospital, Foshan, People’s Republic of China; 3State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, People’s Republic of China; 4Yidu Cloud Technology Ltd, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaihong Chen, Department of Otorhinolaryngology Head and Neck Surgery, Nan fang Hospital, Southern Medical University, 1838 Guangzhou Avenue, Bai-Yun District, Guangzhou, People’s Republic of China, Email [email protected] Chun-Quan Ou, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, 1838 Guangzhou Avenue, Bai-Yun District, Guangzhou, People’s Republic of China, Email [email protected]: 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≥ 28 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 0.76 (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≥ 28 kg/m2, high blood pressure, choke, sleepiness, apnea, white blood cell count ≥ 9.5× 109/L, hemoglobin ≥ 175g/L, and triglycerides ≥ 1.7 mmol/L. The nomogram based on these variables was developed and validated. It seemed that our model outperformed the SBQ.Meaning: A clinically easy-to-use nomogram was provided for the screening of severe OSA in both non-sleep departments and community hospitals.Keywords: severe obstructive sleep apnea, clinical prediction model, nomogram

Keywords

• severe obstructive sleep apnea
• clinical prediction model
• nomogram