» Articles » PMID: 12684304

Predicting Sleep Apnea and Excessive Day Sleepiness in the Severely Obese: Indicators for Polysomnography

Overview
Journal Chest
Publisher Elsevier
Specialty Pulmonary Medicine
Date 2003 Apr 10
PMID 12684304
Citations 70
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Obstructive sleep apnea (OSA) is common in severely obese subjects (body mass index [BMI] > 35). Overnight polysomnography (OPS) is the "gold standard" method of evaluating this condition; however, it is time-consuming, inconvenient, and expensive. Selection of patients for OPS would be enhanced if we could better predict those likely to have clinically significant OSA.

Study Objective: To look for clinical and biochemical predictors of OSA in symptomatic patients presenting for obesity surgery.

Design And Patients: Symptoms suggestive of OSA were sought in a structured interview. We report OPS results of 99 consecutive subjects in whom OSA was clinically suspected. Predictors of apnea-hypopnea index (AHI) were sought from an extensive preoperative data collection. Multivariate linear and logistic analysis was used to identify independent predictors of AHI.

Results: Symptoms were poor predictors of AHI, with observed sleep apnea the only positive predictor. Four clinical and two biochemical factors independently predicted AHI: observed sleep apnea, male sex, higher BMI, age, fasting insulin, and glycosylated hemoglobin A(Ic) (r(2) = 0.42). Neck circumference (the best single measure) could replace BMI and sex in the analysis (r(2) = 0.43). With cutoffs selected, a simple scoring system using these six factors provides a method of predicting those with moderate or severe OSA. A score > or = 3 provides a sensitivity and specificity of 89% and 81%, and 96% and 71% for AHIs of > or = 15 and > or = 30, respectively. None of the 31 subjects with scores of 0 or 1 were found to have an AHI > or = 15.

Conclusion: We explore sleep disturbance and report a simple method of predicting OSA in severely obese symptomatic subjects. This should assist in limiting the use of OPS to those with greater risk and provide a method of assessing risk in those not presenting primarily with a sleep problem.

Citing Articles

Analysis of Possible Predictors of Moderate and Severe Obstructive Sleep Apnea in Obese Patients.

Matarredona-Quiles S, Carrasco-Llatas M, Martinez-Ruiz de Apodaca P, Diez-Ares J, Gonzalez-Turienzo E, Dalmau-Galofre J Indian J Otolaryngol Head Neck Surg. 2024; 76(6):5126-5132.

PMID: 39559156 PMC: 11569310. DOI: 10.1007/s12070-024-04908-0.


Risk prediction for Obstructive Sleep Apnea prognostic in Obese patients referred for bariatric surgery.

Hora A, Napolis L, Villaca D, Dos Santos R, Galvao T, Togeiro S J Bras Pneumol. 2022; 48(6):e20210360.

PMID: 36477170 PMC: 9720887. DOI: 10.36416/1806-3756/e20210360.


Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.

Ferreira-Santos D, Amorim P, Silva Martins T, Monteiro-Soares M, Rodrigues P J Med Internet Res. 2022; 24(9):e39452.

PMID: 36178720 PMC: 9568812. DOI: 10.2196/39452.


Effects of six weeks high-intensity interval training and resistance training in adults with obesity and sleep related breathing disorders.

Longlalerng K, Nakeaw A, Charawae A, Reantong P, Prangyim U, Jeenduang N Sleep Sci. 2021; 14(Spec 1):41-48.

PMID: 34917272 PMC: 8663736. DOI: 10.5935/1984-0063.20200076.


Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model.

Zhang J, Tang Z, Gao J, Lin L, Liu Z, Wu H Comput Intell Neurosci. 2021; 2021:5594733.

PMID: 33859679 PMC: 8009718. DOI: 10.1155/2021/5594733.