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Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
Published on: December 6, 2016
Advances in cardiovascular risk assessment indicators in obstructive sleep apnea
Tong Feng1, Yuanyuan Liu1, Yubing Yue1
1Department of Respiratory and Critical Care Medicine, Deyang People's Hospital, Deyang, China.
None:
Obstructive sleep apnea (OSA) is a highly prevalent sleep-related breathing disorder that is closely associated with cardiovascular morbidity and mortality. Although the apnea-hypopnea index (AHI) remains the conventional metric for diagnosing and grading OSA severity, it inadequately reflects the heterogeneous pathophysiological burden of the disease and has limited predictive value for cardiovascular outcomes. This review summarizes recent advances in cardiovascular risk assessment indicators in OSA, with a focus on mechanisms of OSA-related cardiovascular injury, limitations of traditional metrics, emerging physiological indices, circulating biomarkers, phenotype-based stratification, and risk evaluation in patients with specific cardiovascular comorbidities. Intermittent hypoxia, sleep fragmentation, and intrathoracic pressure swings jointly contribute to sympathetic activation, oxidative stress, systemic inflammation, endothelial dysfunction, hypercoagulability, metabolic dysregulation, and cardiac remodeling. Novel physiological indicators, including hypoxic burden, heart rate response, pulse wave amplitude drop index, and respiratory effort-related metrics, may better quantify OSA-related cardiovascular stress than AHI alone. In parallel, biomarkers reflecting inflammation, endothelial dysfunction, coagulation activation, myocardial injury, and microRNA-mediated regulation show potential for identifying high-risk subgroups. Phenotype-based approaches, such as classifications integrating symptoms, hypoxic patterns, and cardiovascular risk, provide a promising framework for precision management. Future research should standardize these novel indicators, validate their associations with hard cardiovascular outcomes, and develop multimodal prediction models to guide individualized therapeutic strategies.
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