Development of a Minimally Invasive Screening Tool to Identify Obese Pediatric Population at Risk of Obstructive Sleep Apnea/Hypopnea Syndrome

Obstructive sleep apnea syndrome is a reduction of the airflow during sleep which not
only produces a reduction in sleep quality but also has major health consequences. The prevalence
in the obese pediatric population can surpass 50%, and polysomnography is the current gold
standard method for its diagnosis. Unfortunately, it is expensive, disturbing and time-consuming
for experienced professionals. The objective is to develop a patient-friendly screening tool for the
obese pediatric population to identify those children at higher risk of su ering from this syndrome.
Three supervised learning classifier algorithms (i.e., logistic regression, support vector machine and
AdaBoost) common in the field of machine learning were trained and tested on two very di erent
datasets where oxygen saturation raw signal was recorded. The first dataset was the Childhood
Adenotonsillectomy Trial (CHAT) consisting of 453 individuals, with ages between 5 and 9 years old
and one-third of the patients being obese. Cross-validation was performed on the second dataset
from an obesity assessment consult at the Pediatric Department of the Hospital General Universitario
of Valencia. A total of 27 patients were recruited between 5 and 17 years old; 42% were girls and 63%
were obese. The performance of each algorithm was evaluated based on key performance indicators
(e.g., area under the curve, accuracy, recall, specificity and positive predicted value). The logistic
regression algorithm outperformed (accuracy = 0.79, specificity = 0.96, area under the curve = 0.9,
recall = 0.62 and positive predictive value = 0.94) the support vector machine and the AdaBoost
algorithm when trained with the CHAT datasets. Cross-validation tests, using the Hospital General
de Valencia (HG) dataset, confirmed the higher performance of the logistic regression algorithm
in comparison with the others. In addition, only a minor loss of performance (accuracy = 0.75,
specificity = 0.88, area under the curve = 0.85, recall = 0.62 and positive predictive value = 0.83) was
observed despite the di erences between the datasets. The proposed minimally invasive screening
tool has shown promising performance when it comes to identifying children at risk of su ering
obstructive sleep apnea syndrome. Moreover, it is ideal to be implemented in an outpatient consult
in primary and secondary care.