Improving diagnosis in Obstructive Sleep Apnea with clinical data: a Bayesian network approach

Daniela Ferreira-Santos1, Pedro Pereira Rodrigues1,2

1CINTESIS, 2MEDCIDS-FMUP

Introduction: 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.

Aim: 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.

Methods: Our sample performed polysomnography between January and May 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.

Results: 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%.

Discussion: The high (34%) proportion of normal results confirm the need for a pre-evaluation prior to polysomnography.

Conclusion: The constant seeking of a validated model to screen patients with suspicion of obstructive sleep apnea is essential, especially at the level of primary care.

keywords: obstructive sleep apnea; risk factors; diagnosis; Bayesian network; clinical model; sensitivity and specificity

Presentation: Improving diagnosis in Obstructive Sleep Apnea with clinical data: a Bayesian network approach