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Cardiovascular Disease and Adiposity · Journal article
Reviews in Cardiovascular Medicine · July 28, 2026
A consensus or society position rather than new primary data.
This narrative review examines the mechanistic links between sleep-disordered breathing and cardiovascular disease and proposes artificial intelligence as a tool to improve phenotyping, risk stratification, and treatment personalization. The source acknowledges that despite strong mechanistic rationale, clinical trials of SDB treatment have produced neutral or adverse cardiovascular outcomes, highlighting a evidence–practice gap that AI may help address; however, the review itself offers no new empirical validation.
Narrative review. Patients with cardiovascular disease and sleep-disordered breathing.
Sleep-disordered breathing is highly prevalent among patients with cardiovascular disease and contributes to hypertension, coronary artery disease, arrhythmias, heart failure, and cerebrovascular events. Clinical trials of SDB treatment have yielded neutral or adverse cardiovascular outcomes despite strong mechanistic links involving intermittent hypoxemia, sympathetic activation, intrathoracic pressure fluctuations, and systemic inflammation. Artificial intelligence applications proposed include automated detection, cardiovascular risk prediction via machine-learning models, and AI-guided therapy personalization.
Clinical trials of SDB treatment have yielded neutral or adverse cardiovascular outcomes despite strong mechanistic links involving intermittent hypoxemia, sympathetic activation, intrathoracic pressure fluctuations, and systemic inflammation.
Clinicians should recognize that mechanistic understanding of SDB does not yet translate reliably into improved cardiovascular outcomes, and that AI-based tools for phenotyping and treatment selection remain promising but require prospective validation before clinical integration.
A narrative review synthesizing mechanistic understanding and emerging AI applications for sleep-disordered breathing in cardiovascular disease, offering conceptual framework rather than new empirical evidence.
As stated by the source record.
Clinicians should recognize that mechanistic understanding of SDB does not yet translate reliably into improved cardiovascular outcomes, and that AI-based tools for phenotyping and treatment selection remain promising but require prospective validation before clinical integration.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Sleep-disordered breathing (SDB), which includes both obstructive and central sleep apnea, is highly prevalent among patients with cardiovascular disease (CVD). Moreover, SDB contributes significantly to the development and progression of hypertension, coronary artery disease, arrhythmias, heart failure, and various cardiovascular and cerebrovascular events. However, despite strong mechanistic links involving intermittent hypoxemia, sympathetic activation, intrathoracic pressure fluctuations, and systemic inflammation, clinical trials of SDB treatment have yielded in neutral or even adverse cardiovascular outcomes. These results underscore the need for refined phenotyping, risk stratification, and personalized management. Artificial intelligence (AI) has emerged as a promising tool to address these challenges. In this review, we evaluate the mechanistic pathways through which SDB affects cardiovascular health and critically examine AI-based methods to enhance screening, outcome prediction, and treatment optimization. Applications include automated detection using clinical and biosignal data, cardiovascular risk prediction through machine-learning models based on sleep parameters, and AI-guided therapy personalization. Furthermore, we emphasize translational relevance by comparing model performance, identifying high-risk phenotypes, and exploring the potential for integration into clinical workflows. AI-enabled tools may help bridge the gap between pathophysiological understanding and improved outcomes by facilitating earlier diagnosis, tailored interventions, and proactive monitoring. Future studies should focus on prospective validation, regulatory pathways, and equitable deployment across populations.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.