Can We Avoid Unnecessary Polysomnographies in the Diagnosis of Obstructive Sleep Apnea? A Bayesian Network Decision Support Tool

Leite, Liliana; Costa-Santos, Cristina; Rodrigues, Pedro Pereira

doi:10.1109/cbms.2014.30

Cited by 5 publications

(2 citation statements)

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“…In this clinical outcome, diagnostic models need to have a high sensitivity, as false negatives should be avoided, to prevent excluding patients with moderate or severe OSA from performing polysomnography (PSG) [24,34], the standard test for OSA final diagnosis. This study aimed to define auxiliary diagnostic methods that can support the decision to perform PSG, based on risk and diagnostic factors by means of interactive models or risk matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Recommendations from the American Academy of Sleep Medicine state that OSA is present when AHI ≥ 5. It can be classified as mild (AHI: 5-15), moderate (AHI: [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], or severe (AHI ≥ 30) [6,7,13,22]. Approximately 30% of the general public is affected by a significant sleep problem, often of long standing [39].…”

Section: Introductionmentioning

confidence: 99%

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A clinical risk matrix for obstructive sleep apnea using Bayesian network approaches

Ferreira-Santos

Rodrigues

2018

Int J Data Sci Anal

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In obstructive sleep apnea, respiratory effort is maintained but ventilation decreases/disappears due to upper-airway partial/total occlusion. This condition affects about 4% of men and 2% of women worldwide. This study aimed to define an auxiliary diagnostic method that can support the decision to perform polysomnography, based on risk and diagnostic factors. Our sample performed polysomnography between January and May 2015. Two Bayesian classifiers were used to build the models: Naïve Bayes and Tree Augmented Naïve Bayes, using 38 variables identified by literature review or just a selection of 6. Area under the ROC curve, sensitivity, specificity and predictive values were evaluated using leave-one-out and cross-validation techniques. From a total of 241 patients, only 194 fulfilled the inclusion criteria, 123 (63%) were male, with a mean age of 58 years, 66 (34%) patients had a normal result and 128 (66%) a diagnosis of obstructive sleep apnea. The cross-validated AUCs for each model were: NB38: 69.2%; TAN38: 69.0%; NB6: 74.6% and TAN6: 63.6%. Regarding risk matrix, female gender presented a starting rate of 8%, comparing to 20% in male gender, almost 3 times higher. The high (34%) proportion of normal results confirms the need for a pre-evaluation prior to polysomnography, making the search for a validated model to screen patients with suspicion of obstructive sleep apnea essential, especially at primary care level.

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Section: Introductionmentioning

confidence: 99%