Session

Saturday, June 24, 2017 - 09:00 to 11:00
Improving diagnosis in Obstructive Sleep Apnea with clinical data: a Bayesian network approach
Abstract: 
In Obstructive Sleep Apnea, respiratory effort is maintained but ventilation decreases/disappears because of the partial/total occlusion in the upper airway. It affects about 4% of men and 2% of women in the world population. The aim was to define an auxiliary diagnostic method that can support the decision to perform polysomnography (standard test), based on risk and diagnostic factors. Our sample performed polysomnography between January and May of 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes (NB) and Tree augmented Naïve Bayes (TAN), using all 39 variables or just a selection of 13. Area under the ROC curve, sensitivity, specificity, predictive values were evaluated using cross-validation. From a collected total of 241 patients, only 194 fulfill the inclusion criteria. 123 (63%) were male, with a mean age of 58 years old. 66 (34%) patients had a normal result and 128 (66%) a diagnostic of obstructive sleep apnea. The AUCs for each model were: NB39 - 72%; TAN39 - 79%; NB13 - 75% and TAN13 - 75%. The high (34%) proportion of normal results confirm the need for a pre-evaluation prior to polysomnography. The constant seeking of a validated model to screen patients with suspicion of obstructive sleep apnea in Portugal is essential, especially at the level of primary care.
Daniela Ferreira-Santos's picture
Daniela Ferreira-Santos
CINTESIS (PT)
Pedro Pereira Rodrigues's picture
Pedro Pereira Rodrigues
University of Porto (PT)