| Literature DB >> 36217078 |
Félix Del Campo1,2,3, C Ainhoa Arroyo1, Carlos Zamarrón4, Daniel Álvarez5,6,7.
Abstract
Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.Entities:
Keywords: Artificial intelligence; Comorbidities; Decision support system; Diagnosis; Home sleep apnea testing; Machine learning; Obstructive sleep apnea; Polysomnography; Respiratory event; Screening; Sleep staging
Mesh:
Year: 2022 PMID: 36217078 DOI: 10.1007/978-3-031-06413-5_4
Source DB: PubMed Journal: Adv Exp Med Biol ISSN: 0065-2598 Impact factor: 3.650