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

2020 ◽  
Vol 7 (4) ◽  
pp. 131
Author(s):  
José Miguel Calderón ◽  
Julio Álvarez-Pitti ◽  
Irene Cuenca ◽  
Francisco Ponce ◽  
Pau Redon
Keyword(s):  
Obstructive Sleep Apnea ◽  
Logistic Regression ◽  
Sleep Apnea ◽  
Screening Tool ◽  
Pediatric Population ◽  
Sleep Apnea Syndrome ◽  
Area Under The Curve ◽  
Support Vector ◽  
Obstructive Sleep ◽  
Logistic Regression Algorithm

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.

scholarly journals Validation of the Spanish Version of the STOP-Bang Questionnaire: Usefulness as a Screening Tool for Obstructive Sleep Apnea in Adults

2020 ◽  
pp. 014556132093233
Author(s):  
Beatriz Delgado-Vargas ◽  
Leticia Acle-Cervera ◽  
Gianmarco Narciso López
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Screening Tool ◽  
Sleep Apnea Syndrome ◽  
Spanish Version ◽  
Obstructive Sleep ◽  
Significant Difference ◽  
Good Internal Consistency ◽  
The Mean ◽  
Apnea Syndrome

Objectives: Obstructive sleep apnea syndrome (OSAS) is an increasing health problem, the diagnosis of which is generally delayed due to long waiting lists for the tests used to identify it. Therefore, tools that help on classifying patients at higher risk of suffering this syndrome have been developed. Methods: One hundred ninety-three consecutive patients, with and without OSAS, filled in the Spanish version of the STOP-Bang questionnaire in Hospital Universitario de Torrejón (Spain). Polysomnographies were performed to diagnose the presence and severity of the OSAS. Statistics analysis of the demographic characteristics of the sample and the questionnaire results was performed. Results: Most patients were male (73%) and the mean age was 50.4 years (ranging from 19-77 years). Cronbach α coefficient in the sample was 0.8072. A statistically significant difference was noted in the questionnaire scores between patients with OSAS and those without the syndrome. Conclusions: The Spanish version of the STOP-Bang questionnaire possess a good internal consistency that allows us to rely on it as a screening tool for patients with OSAS. In our sample, a difference in the questionnaire score was appreciated between patients with and without the syndrome, which strongly supports the utility of the questionnaire for its purpose.

scholarly journals Cardiorespiratory Polygraphy for Detection of Obstructive Sleep Apnea in Patients With Atrial Fibrillation

2021 ◽  
Vol 8 ◽  
Author(s):  
Michiel Delesie ◽  
Lieselotte Knaepen ◽  
Johan Verbraecken ◽  
Karolien Weytjens ◽  
Paul Dendale ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Screening Tool ◽  
Area Under The Curve ◽  
Screening Tools ◽  
Apnea Hypopnea Index ◽  
Automated Algorithm ◽  
Obstructive Sleep ◽  
At Home

Background: Obstructive sleep apnea (OSA) is a modifiable risk factor of atrial fibrillation (AF) but is underdiagnosed in these patients due to absence of good OSA screening pathways. Polysomnography (PSG) is the gold standard for diagnosing OSA but too resource-intensive as a screening tool. We explored whether cardiorespiratory polygraphy (PG) devices using an automated algorithm for Apnea-Hypopnea Index (AHI) determination can meet the requirements of a good screening tool in AF patients.Methods: This prospective study validated the performance of three PGs [ApneaLink Air (ALA), SOMNOtouch RESP (STR) and SpiderSAS (SpS)] in consecutive AF patients who were referred for PSG evaluation. Patients wore one of the three PGs simultaneously with PSG, and a different PG during each of three consecutive nights at home. Severity of OSA was classified according to the AHI during PSG (<5 = no OSA, 5–14 = mild, 15–30 = moderate, >30 = severe).Results: Of the 100 included AF patients, PSG diagnosed at least moderate in 69% and severe OSA in 33%. Successful PG execution at home was obtained in 79.1, 80.2 and 86.8% of patients with the ALA, STR and SpS, respectively. For the detection of clinically relevant OSA (AHI ≥ 15), an area under the curve of 0.802, 0.772 and 0.803 was calculated for the ALA, STR and SpS, respectively.Conclusions: This study indicates that home-worn PGs with an automated AHI algorithm can be used as OSA screening tools in AF patients. Based on an appropriate AHI cut-off value for each PG, the device can guide referral for definite PSG diagnosis.

scholarly journals Comparison of support vector machine based on genetic algorithm with logistic regression to diagnose obstructive sleep apnea

2018 ◽  
Vol 23 (1) ◽  
pp. 65 ◽  
Author(s):  
Nader Salari ◽  
Zohreh Manoochehri ◽  
Mansour Rezaei ◽  
Habibolah Khazaie ◽  
Sara Manoochehri ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Obstructive Sleep Apnea ◽  
Logistic Regression ◽  
Sleep Apnea ◽  
Support Vector ◽  
Obstructive Sleep

scholarly journals Application of machine learning to predict obstructive sleep apnea syndrome severity

2019 ◽  
Vol 26 (1) ◽  
pp. 298-317 ◽  
Author(s):  
Corrado Mencar ◽  
Crescenzio Gallo ◽  
Marco Mantero ◽  
Paolo Tarsia ◽  
Giovanna E Carpagnano ◽  
...  
Keyword(s):  
Machine Learning ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep Apnea Syndrome ◽  
Sleep Apnea Syndrome ◽  
Support Vector ◽  
Apnea Hypopnea Index ◽  
Machine Learning Methods ◽  
Obstructive Sleep ◽  
Apnea Syndrome

Introduction: Obstructive sleep apnea syndrome has become an important public health concern. Polysomnography is traditionally considered an established and effective diagnostic tool providing information on the severity of obstructive sleep apnea syndrome and the degree of sleep fragmentation. However, the numerous steps in the polysomnography test to diagnose obstructive sleep apnea syndrome are costly and time consuming. This study aimed to test the efficacy and clinical applicability of different machine learning methods based on demographic information and questionnaire data to predict obstructive sleep apnea syndrome severity. Materials and methods: We collected data about demographic characteristics, spirometry values, gas exchange (PaO2, PaCO2) and symptoms (Epworth Sleepiness Scale, snoring, etc.) of 313 patients with previous diagnosis of obstructive sleep apnea syndrome. After principal component analysis, we selected 19 variables which were used for further preprocessing and to eventually train seven types of classification models and five types of regression models to evaluate the prediction ability of obstructive sleep apnea syndrome severity, represented either by class or by apnea–hypopnea index. All models are trained with an increasing number of features and the results are validated through stratified 10-fold cross validation. Results: Comparative results show the superiority of support vector machine and random forest models for classification, while support vector machine and linear regression are better suited to predict apnea–hypopnea index. Also, a limited number of features are enough to achieve the maximum predictive accuracy. The best average classification accuracy on test sets is 44.7 percent, with the same average sensitivity (recall). In only 5.7 percent of cases, a severe obstructive sleep apnea syndrome (class 4) is misclassified as mild (class 2). Regression results show a minimum achieved root mean squared error of 22.17. Conclusion: The problem of predicting apnea–hypopnea index or severity classes for obstructive sleep apnea syndrome is very difficult when using only data collected prior to polysomnography test. The results achieved with the available data suggest the use of machine learning methods as tools for providing patients with a priority level for polysomnography test, but they still cannot be used for automated diagnosis.

The NoSAS score: A new and simple screening tool for obstructive sleep apnea syndrome in depressive disorder

2018 ◽  
Vol 227 ◽  
pp. 136-140 ◽  
Author(s):  
Kelly Guichard ◽  
Helena Marti-Soler ◽  
Jean-Arthur Micoulaud-Franchi ◽  
Pierre Philip ◽  
Pedro Marques-Vidal ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Depressive Disorder ◽  
Obstructive Sleep Apnea Syndrome ◽  
Screening Tool ◽  
Sleep Apnea Syndrome ◽  
Obstructive Sleep ◽  
Apnea Syndrome ◽  
Simple Screening
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