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

Abstract

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 suffering 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 different 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 differences between the datasets. The proposed minimally invasive screening tool has shown promising performance when it comes to identifying children at risk of suffering obstructive sleep apnea syndrome. Moreover, it is ideal to be implemented in an outpatient consult in primary and secondary care.

Development of a New Detection Algorithm to Identify Acute Coronary Syndrome Using Electrochemical Biosensors for Real-World Long-Term Monitoring

“…Additional indicators commonly used to evaluate diagnostic tests were also calculated [ 22 , 23 , 24 ]. The LR+ and LR−measure the influence of a result on the probability while the DOR estimates the discriminative ability of the test.…”

“…Figure 2 schematically illustrates the methodology used in this manuscript. The performance of the developed logistic regression model was evaluated making use of the confusion matrix and a broad number of key performance indicators (e.g., accuracy, specificity, recall or sensitivity, precision or positive predictive value (PPV), area under the curve (AUC), negative predictive value (NPV), F-Score, Youden index (YI), likelihood ratio positive (LR+), likelihood ratio negative (LR−) and diagnostic odds ratio (DOR)) [ 22 , 23 , 24 ].…”

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

“…Only three exceptions were found to the use of a third real or simulated subgroup due to hyperparameter tunning. Two of them were the studies of Calderon et al 23 and Bertoni et al, 18 whose corresponding AdaBoost and SVM models usually require hyperparameter tuning to reach an optimum performance (e.g., penalty parameters such as learning rate or C, respectively). In contrast, Xu et al 26 applied the same exact MLP than the one previously internally validated by Hornero et al 16 …”

“…In contrast, machine‐learning techniques have elicited increasingly growing interest due to their prominent impact in a wide range of healthcare processes 14 . Indeed, promising results have also been reported in studies involving machine‐learning approaches that facilitate automated OSA diagnosis using pediatric recordings 15–33 . However, a substantial level of skepticism remains among the sleep specialists and clinical practitioners alike, regarding the clinical use of these automatic tools 34 …”

“…14 Indeed, promising results have also been reported in studies involving machine-learning approaches that facilitate automated OSA diagnosis using pediatric recordings. [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] However, a substantial level of skepticism remains among the sleep specialists and clinical practitioners alike, regarding the clinical use of these automatic tools. 34 There is little doubt that availability of a reliable, automated, and simplified alternative to PSG would improve OSA diagnosis in children from several different perspectives.…”