A Novel Clinical Tool to Detect Severe Obstructive Sleep Apnea

Area of Science:

• Medical research
• Clinical diagnostics
• Sleep medicine

Background:

• Obstructive sleep apnea (OSA) is a serious condition with significant health risks.
• Current screening tools for severe OSA lack optimal accuracy and feasibility.
• Effective screening is crucial for timely patient management and improved outcomes.

Purpose of the Study:

• To develop and validate a feasible clinical predictive model for identifying severe OSA patients.
• To create a user-friendly tool for physicians to screen for severe OSA.

Main Methods:

• Retrospective cohort study of 1920 adults undergoing polysomnography.
• Multivariate logistic regression model constructed using demography, symptoms, and hematological data.
• Nomogram developed for severe OSA identification and compared with the Stop-Bang Questionnaire (SBQ).

Main Results:

• Severe OSA associated with male sex, BMI ≥ 28, hypertension, choking, sleepiness, apnea, elevated white blood cells, hemoglobin, and triglycerides.
• The developed model achieved an AUC of 0.76 (95% CI: 0.74-0.78).
• The novel model demonstrated significantly higher accuracy than the SBQ (AUC 0.78 vs 0.66, P = 0.002).

Conclusions:

• A novel clinical model and nomogram were developed for screening severe OSA in inpatients.
• The tool utilizes common clinical data for ease of use and visual interpretation.
• This model offers a practical solution for physicians to identify patients with severe OSA.