The Hypoxic Burden, Clinical Implication of a New Biomarker in the Cardiovascular Management of Sleep Apnea Patients: A Systematic Review

睡眠呼吸暂停患者心血管管理中新型生物标志物的低氧负荷临床意义:系统评价

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Abstract

BACKGROUND: Obstructive sleep apnea (OSA) is a highly prevalent sleep-disordered breathing. It is associated with adverse co-morbidities, being the most scientific evidence of cardiovascular (CV) disease. Currently, OSA is measured through the apnea-hypopnea index (AHI), the total number of respiratory events per hour of sleep. However, different studies have questioned its utility in OSA management, highlighting the need to search for new parameters that better reflect the heterogeneity of the disease. Hypoxic burden (HB) has emerged as a novel biomarker that informs about the frequency, duration and depth of the desaturation related to the respiratory events. We conducted a systematic review in order to find publications about the heterogeneity of OSA measured by HB and its associations with future disease. METHODS: Systematic review was conducted using PubMed and Web of Science. The terms "sleep apne" and "hypoxic burden" were used to look for publications from the date of inception to August 15, 2023. Inclusion criteria: articles in English published in peer-reviewed journals. Exclusion criteria: (1) not available publications; (2) duplicated articles; (3) letters, editorials, and congress communications; (4) articles not including information about HB as a specific biomarker of OSA. RESULTS: 33 studies were included. The results were classified in 2 main sections: (1) HB implication in the CV sphere: HB showed to be a better predictor of CV risk in OSA patients than traditional measures such as AHI with possible clinical management implication in OSA. (2) HB response to OSA treatment: pharmacological and nonpharmacological treatments have demonstrated to be effective in improving hypoxia measured through the HB. CONCLUSIONS: HB could be a better and more effective parameter than traditional measurements in terms of diagnosis, risk prediction and therapeutic decisions in patients with OSA. This measure could be incorporated in sleep units and could play a role in OSA management, driving the clinic to a more personalized medicine.

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