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DC Field | Value | Language |
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dc.contributor.author | Bourdin A. | - |
dc.contributor.author | Bardin P. | - |
dc.contributor.author | Chanez P. | - |
dc.date.accessioned | 2024-10-16T01:56:20Z | - |
dc.date.available | 2024-10-16T01:56:20Z | - |
dc.date.copyright | 2024 | - |
dc.date.issued | 2024-09-30 | en |
dc.identifier.citation | Expert Review of Respiratory Medicine. 18(8) (pp 561-567), 2024. Date of Publication: 2024. | - |
dc.identifier.uri | https://repository.monashhealth.org/monashhealthjspui/handle/1/52570 | - |
dc.description.abstract | Introduction: There are no validated decision-making algorithms concerning severe asthma (SA) management. Future risks are crucial factors and can be derived from SA trajectories. Areas covered: The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted. Expert opinion: Asthma severity is currently defined a priori, thereby precluding a role for early interventions aiming to prevent outcomes such as exacerbations (systemic corticosteroids exposure) and lung function decline. Asthma 'at-risk' might represent the ultimate paradigm but merits longitudinal studies considering modern interventions. Real exacerbations, severe airway hyperresponsiveness, excessive T2-related biomarkers, noxious environments and patient behaviors, harms of OCS and high-doses inhaled corticosteroids (ICS), and low adherence-to-effectiveness ratios of ICS-containing inhalers are predictors of future risks. New tools such as imaging, genetic, and epigenetic signatures should be used. Logical and numerical artificial intelligence may be used to generate a consistent risk score. A pragmatic definition of response to treatments will allow development of a validated and applicable algorithm. Biologics have the best potential to minimize the risks, but cost remains an issue. We propose a simplified six-step algorithm for decision-making that is ultimately aiming to achieve asthma remission.Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group. | - |
dc.publisher | Taylor and Francis Ltd. | - |
dc.relation.ispartof | Expert Review of Respiratory Medicine | - |
dc.subject.mesh | artificial intelligence | - |
dc.subject.mesh | drug megadose | - |
dc.subject.mesh | epigenetics | - |
dc.subject.mesh | genetics | - |
dc.subject.mesh | lung function | - |
dc.subject.mesh | respiratory tract allergy | - |
dc.subject.mesh | severe asthma | - |
dc.title | Imagining the severe asthma decision trees of the future. | - |
dc.type | Article | - |
dc.identifier.affiliation | Respiratory and Sleep Medicine | - |
dc.type.studyortrial | Review article (e.g. literature review, narrative review) | - |
dc.identifier.doi | http://monash.idm.oclc.org/login?url=https://dx.doi.org/10.1080/17476348.2024.2390987 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.pubmedid | 39120156 [https://www.ncbi.nlm.nih.gov/pubmed/?term=39120156] | - |
dc.identifier.institution | (Bourdin) Departement de Pneumologie et Addictologie, PhyMedExp, University of Montpellier, INSERM U1046, CNRS UMR 9214, Montpellier, France | - |
dc.identifier.institution | (Bardin) Monash Lung and Sleep Allergy Immunology, Monash Hospital, Monash Health and University, Hudson Institute, Melbourne, VIC, Australia | - |
dc.identifier.institution | (Chanez) APHM, Clinique des bronches allergies et sommeil, Marseille, France | - |
dc.identifier.institution | (Chanez) Aix Marseille Univ, INSERM U1263, INRA 1260 (C2VN), Marseille, France | - |
dc.identifier.affiliationmh | (Bardin) Monash Lung and Sleep Allergy Immunology, Monash Hospital, Monash Health and University, Hudson Institute, Melbourne, VIC, Australia | - |
item.openairetype | Article | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | No Fulltext | - |
crisitem.author.dept | Respiratory and Sleep Medicine | - |
Appears in Collections: | Articles |
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