Evaluation of a prediction model for sleep apnea in patients submitted to polysomnography.

[Article in English, Portuguese]


Júlia Kubitschek Hospital, Fundação Hospitalar do Estado de Minas Gerais-FHEMIG, Hospital Foundation of the state of Minas Gerais-Belo Horizonte, Brazil. silviomusman@yahoo.com.br



To test a prediction model for sleep apnea based on clinical and sociodemographic variables in a population suspected of having sleep disorders and submitted to .


We included 323 consecutive patients submitted to polysomnography because of the clinical suspicion of having sleep disorders. We used a questionnaire with sociodemographic questions and the Epworth sleepiness scale. Blood pressure, weight, height, and SpO2 were measured. Multiple linear regression was used in order to create a prediction model for sleep apnea, the apnea-hypopnea index (AHI) being the dependent variable. Multinomial logistic regression was used in order to identify factors independently associated with the severity of apnea (mild, moderate, or severe) in comparison with the absence of apnea.


The prevalence of sleep apnea in the study population was 71.2%. Sleep apnea was more prevalent in men than in women (81.2% vs. 56.8%; p < 0.001). The multiple linear regression model, using log AHI as the dependent variable, was composed of the following independent variables: neck circumference, witnessed apnea, age, BMI, and allergic rhinitis. The best-fit linear regression model explained 39% of the AHI variation. In the multinomial logistic regression, mild apnea was associated with BMI and neck circumference, whereas severe apnea was associated with age, BMI, neck circumference, and witnessed apnea.


Although the use of clinical prediction models for sleep apnea does not replace polysomnography as a tool for its diagnosis, they can optimize the process of deciding when polysomnography is indicated and increase the chance of obtaining positive polysomnography findings.

J Bras Pneumol. 2011 Feb;37(1):75-84.

Randy Clare

Randy Clare

Randy Clare brings to The Sleep and Respiratory Scholar more than 25 years of extensive knowledge and experience in the sleep and pulmonary function field. He has held numerous management positions throughout his career and has demonstrated a unique view of the alternate care diagnostic and therapy model. He is considered by many an expert in the use of a Sleep Bruxism Monitor in a dental office. Mr. Clare's extensive sleep industry experience assists Sleepandrespiratoryscholar in providing current, relevant, data-proven information on sleep diagnostics and sleep therapies that are effective for the treatment of sleep disorders. Mr Clare is a senior brand manager for Glidewell Dental Laboratory his focus is on dental treatment for sleep disordered breathing.

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