Ticagrelor and Central Sleep Apnea

Revol, Bruno; Jullian‐Desayes, Ingrid; Tamisier, Renaud; Puel, Vincent; Mallaret, Michel; Joyeux‐Faure, Marie; Pépin, Jean‐Louis

doi:10.1016/j.jacc.2018.03.447

Cited by 29 publications

(19 citation statements)

References 5 publications

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“…However, the prevalence of central sleep apnoea in cardiovascular patients is high, making it difficult to attribute apnoeas to ticagrelor use, only on the basis of case reports. A safety signal for sleep apnoea syndrome and ticagrelor has recently been raised by a disproportionality analysis, with a ROR of 4.16 (95% CI, 2.87‐6.03) . However, disproportionality does not provide information on causal association between ticagrelor use and sleep apnoea.…”

Section: Casesupporting

confidence: 90%

Ticagrelor and central sleep apnoea: Impact of withdrawal and reintroduction

Puel¹,

Godard²,

Raymond³

et al. 2019

Brit J Clinical Pharma

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These are both convincing case reports that emphasise the need for clinicians to be aware of this side effect of ticagrelor and search for central apnoeas in patients complaining from dyspnoea and fatigue during treatment. | Nomenclature of targets and ligandsKey protein targets and ligands in this article are hyperlinked to corresponding entries in http://www.guidetopharmacology.org, 10 the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY.

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Section: Casesupporting

confidence: 90%

Ticagrelor and central sleep apnoea: Impact of withdrawal and reintroduction

Puel¹,

Godard²,

Raymond³

et al. 2019

Brit J Clinical Pharma

Self Cite

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“… 10 While our patient had no evidence of heart failure, emerging reports suggest that ticagrelor may itself induce central sleep apnea and Cheyne-Stokes respiration. 11 The pathophysiological explanation remains unclear. Proposed mechanisms include antagonism of microglial P2Y 12 receptors and effects on pulmonary C fibers, either as a result of increased adenosine tissue levels or because of putative P2Y 12 receptors on pulmonary C fibers.…”

Section: Discussionmentioning

confidence: 99%

“…Proposed mechanisms include antagonism of microglial P2Y 12 receptors and effects on pulmonary C fibers, either as a result of increased adenosine tissue levels or because of putative P2Y 12 receptors on pulmonary C fibers. 11 …”

Section: Discussionmentioning

confidence: 99%

Cyclical sinus bradycardia and atrioventricular block induced by ticagrelor

Waldmann

Laredo

Nigam

et al. 2018

HeartRhythm Case Reports

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“…We have used this Big Data resource to identify drugs previously unknown as generating or exacerbating CSA. By applying a ‘case‐ non‐case’ methodology to VigiBase with a disproportionality analysis, our group revealed the implication of baclofen, ticagrelor and gabapentinoids in triggering CSA and elucidated the mechanisms underlying this reaction …”

Section: Opportunities Of Big Data In Understanding Sleep Breathing Dmentioning

confidence: 99%

Big Data in sleep apnoea: Opportunities and challenges

2019

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Sleep apnoea is now regarded as a highly prevalent systemic, multimorbid, chronic disease requiring a combination of long-term home-based treatments. Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio-economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to 'Big Data' that, if used appropriately, has great potential for the benefit of health, well-being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep-disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real-life evidence-based medicine for sleep apnoea.Big Data and sleep apnoea tory, Grenoble Alpes University) for the literature search and contribution to the pharmacovigilance and environmentalRespirology (2020) 25, 486-494

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Ticagrelor and Central Sleep Apnea

Cited by 29 publications

References 5 publications

Ticagrelor and central sleep apnoea: Impact of withdrawal and reintroduction

Ticagrelor and central sleep apnoea: Impact of withdrawal and reintroduction

Cyclical sinus bradycardia and atrioventricular block induced by ticagrelor

Big Data in sleep apnoea: Opportunities and challenges

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