Multiple Machine Learning Methods Reveal Key Biomarkers of Obstructive Sleep Apnea and Continuous Positive Airway Pressure Treatment

Zhu, Jie; Sanford, Larry D.; Ren, Rong; Zhang, Ye; Tang, Xiangdong

doi:10.3389/fgene.2022.927545

Cited by 5 publications

(2 citation statements)

References 70 publications

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“…This predictive model suggested individuals at high risk for OSA showed extensive activation of immune cells and pathways and higher expression of these genes which decreased after CPAP treatment. The information provided by ML in this setting can improve the identification of people with high risk of OSA as well as insight into CPAP treatment individual benefit [ 17 ]. Another study also used ML to identify biomarkers of the presence and severity of OSA, PAI-I, tPA and sE-Selectin, and demonstrated that they reduce with CPAP treatment and therefore may provide a measure of treatment response and guide preventative cardiovascular management for those patients identified as higher risk [ 18 ].…”

Section: Main Textmentioning

confidence: 99%

The role of artificial intelligence in the treatment of obstructive sleep apnea

Brennan

Kirby

2023

Journal of Otolaryngology - Head & Neck Surgery

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Background The first-line and most common treatment for obstructive sleep apnea is nasal continuous positive airway pressure, which serves as a pneumatic splint to stabilize the upper airway and is effective when used with appropriate adherence. Continuous positive airway pressure compliance rates remain significantly low despite machine improvements and compliance intervention. Other treatment options include oral appliances, myofunctional therapy, and surgery. The aim of this project is to elucidate the role of artificial intelligence within improving the treatment of obstructive sleep apnea. Methods Related publications between 1999 and 2022 were reviewed from PubMed and Embase databases utilizing search terms “artificial intelligence,” “machine learning,” “obstructive sleep apnea,” and “treatment.” Both authors independently screened the results by title/abstract then by full text review. 126 non-duplicate articles were screened, 38 articles were included after title and abstract screen and 30 articles were included after full text review. The inclusion criteria are outline in the PICO framework and involved studies focused on artificial intelligence application in guiding and evaluating obstructive sleep apnea treatment. Non-English articles were excluded. Results The role of artificial intelligence in the treatment of OSA was categorized into the following sections: Predicting treatment outcomes of various treatment options, Improving/Evaluating treatment, and Personalizing treatment with improving understanding of underlying mechanisms of OSA. Conclusions Artificial intelligence has the capacity to improve the treatment of OSA through predicting outcomes of treatment options, evaluating the treatment the patient is currently utilizing and increasing understanding of the mechanisms that contribute to OSA disease process and physiology. Implementing AI in guiding treatment decisions allows patients to connect with treatment methods that would be most effective on an individual basis.

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Section: Main Textmentioning

confidence: 99%

The role of artificial intelligence in the treatment of obstructive sleep apnea

Brennan

Kirby

2023

Journal of Otolaryngology - Head & Neck Surgery

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“…Interest in biomarkers for OSA is increasing with early-stage research suggesting they could be beneficial in diagnostics [68,69], and could form a part of screening strategies in the future.…”

Section: Diagnosticsmentioning

confidence: 99%

Perioperative management of Obstructive Sleep Apnoea: Present themes and future directions

Chambers,

Bamber,

Singh

2023

Current Opinion in Pulmonary Medicine

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Purpose of review Obstructive sleep apnoea (OSA) is an increasingly common pathology that all those involved in perioperative care will come across. Patients with the condition present a challenge at many stages along the perioperative journey, not least because many patients living with OSA are unaware of their diagnosis. Key interventions can be made pre, intra-, and postoperatively to improve outcomes. Knowledge of screening tools, diagnostic tests, and the raft of treatment options are important for anyone caring for these patients. Recent findings Recent literature has highlighted the increasing complexity of surgical patients and significant underdiagnosis of OSA in this patient population. Work has demonstrated how and why patients with OSA are at a higher perioperative risk and that effective positive airways pressure (PAP) therapy can reduce these risks, alongside evidencing how best to optimise adherence to therapy, a key issue in OSA. Summary OSA, and particularly undiagnosed OSA, presents a huge problem in the perioperative period. Perioperative PAP reduces the risk of postoperative complications but adherence remains an issue. Bespoke perioperative pathways should be developed to identify and optimise high risk patients, although at present evidence on how best to achieve this is lacking.

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The future of big data: Remote monitoring of positive airway pressure treatment for obstructive sleep apnea – insights from adults and implications for pediatric care

Bhattacharjee,

Winck,

Gozal

2024

Pediatric Pulmonology

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The advent of large expansive datasets has generated substantial interest as a means of developing and implementing unique algorithms that facilitate more precise and personalized interventions. This methodology has permeated the realm of sleep medicine and in the care of patients with sleep disorders. One of the large repositories of information consists of adherence and physiological datasets across long periods of time as derived from patients undergoing positive airway pressure (PAP) treatment for sleep‐disordered breathing. Here, we evaluate the extant and yet scarce findings derived from big data in both adults and children receiving PAP for obstructive sleep apnea and suggest future directions towards more expansive utilization of such valuable approaches to improve therapeutic decisions and outcomes.

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Multiple Machine Learning Methods Reveal Key Biomarkers of Obstructive Sleep Apnea and Continuous Positive Airway Pressure Treatment

Cited by 5 publications

References 70 publications

The role of artificial intelligence in the treatment of obstructive sleep apnea

The role of artificial intelligence in the treatment of obstructive sleep apnea

Perioperative management of Obstructive Sleep Apnoea: Present themes and future directions

The future of big data: Remote monitoring of positive airway pressure treatment for obstructive sleep apnea – insights from adults and implications for pediatric care

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