Network science meets respiratory medicine for OSAS phenotyping and severity prediction

PeerJ. 2017 May 9;5:e3289. doi: 10.7717/peerj.3289. eCollection 2017.

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.

Keywords: Network science; Phenotypes; Prediction score; Prediction specificity; Sleep apnea.

Grant support

This work was supported by Linde Healthcare’s Real Fund 2014, through research grant “Morpheus: A Screening and Monitoring System for Sleep Apnea Syndrome”, 11289/14.10.2014. M.U. and A.T. were also supported by a grant of the Romanian National Authority for Scientific Research and Innovation, UEFISCDI, project number 31 PED/2017: INCEPTION. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.