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A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea.
Ye, Yanqing; Yan, Ze-Lin; Huang, Yuanshou; Li, Li; Wang, Shiming; Huang, Xiaoxing; Zhou, Jingmeng; Chen, Liyi; Ou, Chun-Quan; Chen, Huaihong.
Affiliation
  • Ye Y; Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
  • Yan ZL; Otolaryngology Department, Foshan Nan Hai District People's Hospital, Foshan, People's Republic of China.
  • Huang Y; State 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.
  • Li L; Otolaryngology Department, Foshan Nan Hai District People's Hospital, Foshan, People's Republic of China.
  • Wang S; State 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.
  • Huang X; Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
  • Zhou J; Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
  • Chen L; Department of Otorhinolaryngology-Head and Neck Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
  • Ou CQ; Yidu Cloud Technology Ltd, Beijing, People's Republic of China.
  • Chen H; State 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.
Nat Sci Sleep ; 15: 839-850, 2023.
Article in En | MEDLINE | ID: mdl-37869520
ABSTRACT

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≥ 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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Nat Sci Sleep Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Nat Sci Sleep Year: 2023 Document type: Article