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Predictive value of pulmonary function parameters for sleep apnea syndrome
F Zerah-Lancner1, F Lofaso, M P d'Ortho
1Services de Physiologie-Explorations Fonctionnelles, Hôpital Henri Mondor, AP-HP, Créteil, France. francoise.zerah@hmn.ap-hop-paris.fr
American Journal of Respiratory and Critical Care Medicine
|December 9, 2000
Summary
A new predictive index using pulmonary function tests can accurately identify patients with a low risk of sleep apnea syndrome (SAS). This approach may reduce the need for costly polysomnography in obese snorers.
Area of Science:
- Pulmonary Medicine
- Sleep Medicine
- Diagnostic Innovation
Background:
- Nocturnal polysomnography is the gold standard for diagnosing sleep apnea syndrome (SAS).
- However, polysomnography is expensive and time-consuming, limiting its widespread use.
- Obese individuals who snore are at higher risk for SAS.
Purpose of the Study:
- To develop and validate a predictive index for SAS in obese snorers.
- To identify patients with a low risk of SAS who may not require polysomnography.
- To assess the utility of pulmonary function data in predicting SAS probability.
Main Methods:
- A predictive model was developed using logistic regression on pulmonary function data from 168 obese snorers.
- Key parameters included respiratory resistance (forced oscillation technique) and specific respiratory conductance.
- The model was prospectively validated in 101 similar patients.
Main Results:
- The predictive index demonstrated high accuracy, with 98% sensitivity and 97% negative predictive value (NPV) in the initial group.
- In the prospective validation, the model achieved 100% sensitivity and 100% NPV.
- The model could have obviated polysomnography in 38% of patients in the study population.
Conclusions:
- A predictive index based on pulmonary function tests is a reliable tool for assessing SAS risk in obese snorers.
- The high NPV of this index allows for the identification of patients with very low SAS risk, potentially avoiding polysomnography.
- This approach offers a cost-effective and efficient method for initial SAS screening.