Literature DB >> 12684304

Predicting sleep apnea and excessive day sleepiness in the severely obese: indicators for polysomnography.

John B Dixon1, Linda M Schachter, Paul E O'Brien.   

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.

Entities:  

Mesh:

Year:  2003        PMID: 12684304     DOI: 10.1378/chest.123.4.1134

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  67 in total

1.  Comparison of Commonly Used Questionnaires to Identify Obstructive Sleep Apnea in a High-Risk Population.

Authors:  Kirk Kee; John Dixon; Jonathan Shaw; Elena Vulikh; Markus Schlaich; David M Kaye; Paul Zimmet; Matthew T Naughton
Journal:  J Clin Sleep Med       Date:  2018-12-15       Impact factor: 4.062

2.  The assessment, diagnosis, and treatment of excessive sleepiness: practical considerations for the psychiatrist.

Authors:  Dewey McWhirter; Charles Bae; Kumaraswamy Budur
Journal:  Psychiatry (Edgmont)       Date:  2007-09

3.  A novel approach to prediction of mild obstructive sleep disordered breathing in a population-based sample: the Sleep Heart Health Study.

Authors:  Brian Caffo; Marie Diener-West; Naresh M Punjabi; Jonathan Samet
Journal:  Sleep       Date:  2010-12       Impact factor: 5.849

Review 4.  Lateral teleradiography of the head as a diagnostic tool used to predict obstructive sleep apnea.

Authors:  Juste Armalaite; Kristina Lopatiene
Journal:  Dentomaxillofac Radiol       Date:  2015-08-03       Impact factor: 2.419

5.  The STOP-Bang equivalent model and prediction of severity of obstructive sleep apnea: relation to polysomnographic measurements of the apnea/hypopnea index.

Authors:  Robert J Farney; Brandon S Walker; Robert M Farney; Gregory L Snow; James M Walker
Journal:  J Clin Sleep Med       Date:  2011-10-15       Impact factor: 4.062

6.  Prediction of obstructive sleep apnea syndrome in a large Greek population.

Authors:  Izolde Bouloukaki; Fotis Kapsimalis; Charalampos Mermigkis; Meir Kryger; Nikos Tzanakis; Panagiotis Panagou; Violeta Moniaki; Eleni M Vlachaki; Georgios Varouchakis; Nikolaos M Siafakas; Sophia E Schiza
Journal:  Sleep Breath       Date:  2010-09-25       Impact factor: 2.816

Review 7.  Challenges in pulmonary risk assessment and perioperative management in bariatric surgery patients.

Authors:  Roop Kaw; Loutfi Aboussouan; Dennis Auckley; Charles Bae; David Gugliotti; Paul Grant; Wael Jaber; Philip Schauer; Daniel Sessler
Journal:  Obes Surg       Date:  2007-11-16       Impact factor: 4.129

8.  Outpatient laparoscopic adjustable gastric banding in super-obese patients.

Authors:  Kevin F Montgomery; Brad M Watkins; Jessie H Ahroni; Robert Michaelson; Ronald E Abrams; Marc D Erlitz; James E Scurlock
Journal:  Obes Surg       Date:  2007-06       Impact factor: 4.129

9.  Clinical practice guidelines for the perioperative nutritional, metabolic, and nonsurgical support of the bariatric surgery patient--2013 update: cosponsored by American Association of Clinical Endocrinologists, The Obesity Society, and American Society for Metabolic & Bariatric Surgery.

Authors:  Jeffrey I Mechanick; Adrienne Youdim; Daniel B Jones; W Timothy Garvey; Daniel L Hurley; M Molly McMahon; Leslie J Heinberg; Robert Kushner; Ted D Adams; Scott Shikora; John B Dixon; Stacy Brethauer
Journal:  Obesity (Silver Spring)       Date:  2013-03       Impact factor: 5.002

10.  Clinical practice guidelines for the perioperative nutritional, metabolic, and nonsurgical support of the bariatric surgery patient--2013 update: cosponsored by American Association of Clinical Endocrinologists, the Obesity Society, and American Society for Metabolic & Bariatric Surgery.

Authors:  Jeffrey I Mechanick; Adrienne Youdim; Daniel B Jones; W Timothy Garvey; Daniel L Hurley; M Molly McMahon; Leslie J Heinberg; Robert Kushner; Ted D Adams; Scott Shikora; John B Dixon; Stacy Brethauer
Journal:  Endocr Pract       Date:  2013 Mar-Apr       Impact factor: 3.443

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.