Network science meets respiratory medicine for OSAS phenotyping and severity prediction

PeerJ

article

Stefan Mihaicuta¹  Mihai Udrescu²  Alexandru Topirceanu²  Lucretia Udrescu³ 

[1] Department of Pulmonology, Victor Babes University of Medicine and Pharmacy;Department of Computer and Information Technology, University Politehnica of Timisoara;Faculty of Pharmacy, Victor Babes University of Medicine and Pharmacy

关键词: Network science;    Sleep apnea;    Phenotypes;    Prediction score;    Prediction specificity;   

DOI  :  10.7717/peerj.3289

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY