Sleep Disorders |

Independent Predictors for Sleep Apnea Syndrome in a Clinical Prediction Model FREE TO VIEW

Izabella Anita Toth, BS; Sorin Paralescu, MD; Stefan Mihaicuta, MD
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University of Medicine and Pharmacy, Timisoara, Romania

Chest. 2014;145(3_MeetingAbstracts):587A. doi:10.1378/chest.1824341
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SESSION TYPE: Poster Presentations

PRESENTED ON: Saturday, March 22, 2014 at 01:15 PM - 02:15 PM

PURPOSE: A cross-sectional analysis was performed and a binary logistic regression model has been developed to identify the risk factors associated with sleep apnoea syndrome (SAS) in a population group from a sleep laboratory from Timisoara, Romania.

METHODS: Between October 2005 and April 2013 we evaluated 1566 consecutive patients with suspected SAS, using the Epworth sleepiness questionnaire (ESS), anthropometric data for body mass index (BMI), neck, abdominal and hip circumference (NC, AC and HC), co morbidities and symptoms, polysomnography for apnea - hypopnea index (AHI) assessment. Binary logistic regression model was performed to identify the relationship between SAS and independent variables statistically significant in univariate analysis. Odds ratios (OR) with 95% confidence intervals (CI) were reported for the independent variables. With the area under curve (AUC) derived from the receiver-operating characteristic (ROC) curve we determined the classification ability of the model for the diagnostic of SAS.

RESULTS: In the univariate regression analysis gender, age, BMI, abdominal, neck and hip circumferences, ESS, snoring, awakenings and smoking were associated with SAS. In the multiple logistic regression analysis, BMI (OR=1.15, 95% CI 1.07 - 1.25), NC (OR=1.15, 95% CI 1.07 - 1.23), HC (OR=0.96, 95% CI 0.93 - 0.99), ESS (OR=1.08, 95% CI 1.02 - 1.15) and snoring (OR=3.04, 95% CI 1.51 - 6.10) were only independent predictors of SAS after adjustment of potential confounder variables. The model had a sensitivity of 99.66%, specificity of 9.72%, positive predicted value (PPV) of 93.14% and negative predicted value (NPV) of 77.78%. The area under the ROC curve for this model was 0.793 (95% CI 0.74 - 0.85, p<0.001).

CONCLUSIONS: We obtained a useful clinical prediction model for the identification of SAS. The binary logistic regression model could be used to divide patients into different SAS risk categories. Patients with high clinical risk of SAS could be prioritized for an overnight sleep study.

CLINICAL IMPLICATIONS: Patients with high clinical risk of SAS could be prioritized for an overnight sleep study.

DISCLOSURE: The following authors have nothing to disclose: Izabella Anita Toth, Sorin Paralescu, Stefan Mihaicuta

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