Abstract
Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS). By running targeted network clustering algorithms, we identify eight patient phenotypes and corroborate them with the co-morbidity types. Also, by employing machine learning on the uncovered phenotypes, we derive a classification tree and introduce a computational framework which render the Sleep Apnea Syndrome Score (SASScore); our OSAS score is implemented as an easy-to-use, web-based computer program which requires less than one minute for processing one individual. Our evaluation, performed on a distinct validation database with 231 consecutive patients, reveals that OSAS prediction with SASScore has a significant specificity improvement (an increase of 234%) for only 8.2% sensitivity decrease in comparison with the state-of-the-art score STOP-BANG. The fact that SASScore has bigger specificity makes it appropriate for OSAS screening and risk prediction in big, general populations.
Highlights
Obstructive Sleep Apnea Syndrome (OSAS) is a serious clinical disorder caused by abnormal breathing pauses that occur during sleep; this results in sleep fragmentation and excessive daytime somnolence (Simon & Collop, 2012; Fischer et al, 2012; Lévy et al, 2014)
The Apnea Patients Network (APN) representation resulted from our clustering methodology is presented in Fig. 6, where the distinct colors correspond to distinct modularity classes, and the well-defined topological clusters are explained
The result of applying our methodology on non-OSAS database (NAD) patients is presented in Fig. 7, where the colors correspond to distinct modularity classes; at the same time, topological communities rendered with the energy-model layout Force Atlas 2 are indicated and explained
Summary
Distributed under Creative Commons CC-BY 4.0 OPEN ACCESSObstructive Sleep Apnea Syndrome (OSAS) is a serious clinical disorder caused by abnormal breathing pauses that occur during sleep; this results in sleep fragmentation and excessive daytime somnolence (Simon & Collop, 2012; Fischer et al, 2012; Lévy et al, 2014). If not properly diagnosed and treated, OSAS increases the morbidity and perioperative risks (Memtsoudis, Besculides & Mazumdar, 2013; McNicholas, Bonsignore & of EU Cost Action B26, 2007; Rossi, Stradling & Kohler, 2013; Utriainen et al, 2013; Sánchez-de-la Torre, Campos-Rodriguez & Barbé, 2013). When it remains undetected, OSAS rapidly creates serious cardiovascular, respiratory and nutritional problems (McNicholas, Bonsignore & of EU Cost Action B26, 2007; Yaggi et al, 2005; Bakker, Montesi & Malhotra, 2013). It is essential that OSAS be detected at an early stage, which can only be achieved through preventive actions such as extensive population screening (Pelletier-Fleury et al, 2004)
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