Cluster Analysis to Identify Long COVID Phenotypes Using 129 Xe Magnetic Resonance Imaging: A Multi-centre Evaluation.

Autor:	Eddy RL; Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, Canada.; Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, Canada., Mummy D; Department of Radiology, Duke University, Durham, NC, USA., Zhang S; Department of Radiology, Duke University, Durham, NC, USA., Dai H; Department of Medical Physics, Duke University, Durham, NC, USA., Bechtel A; Department of Radiology, Duke University, Durham, NC, USA., Schmidt A; Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, Canada., Frizzell B; Division of Pulmonary and Critical Care Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA., Gerayeli FV; Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, Canada., Leipsic JA; Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, Canada.; Department of Radiology, University of British Columbia, Vancouver, Canada., Leung JM; Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, Canada.; Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, Canada., Driehuys B; Department of Radiology, Duke University, Durham, NC, USA.; Department of Medical Physics, Duke University, Durham, NC, USA.; Department of Biomedical Engineering, Duke University, Durham, NC, USA., Que LG; Division of Pulmonary, Department of Medicine, Duke University, Durham, NC, USA., Castro M; Division of Pulmonary and Critical Care Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA., Sin DD; Centre for Heart Lung Innovation, St. Paul's Hospital, University of British Columbia, Vancouver, Canada.; Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, Canada., Niedbalski PJ; Division of Pulmonary and Critical Care Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA pniedbalski@kumc.edu.
Jazyk:	angličtina
Zdroj:	The European respiratory journal [Eur Respir J] 2024 Feb 08. Date of Electronic Publication: 2024 Feb 08.
DOI:	10.1183/13993003.02301-2023
Abstrakt:	Background: Long COVID impacts ∼10% of people diagnosed with COVID-19, yet the pathophysiology driving ongoing symptoms is poorly understood. We hypothesised that 129 Xe magnetic resonance imaging (MRI) could identify unique pulmonary phenotypic subgroups of long COVID, therefore we evaluated ventilation and gas exchange measurements with cluster analysis to generate imaging-based phenotypes. Methods: COVID-negative controls and participants who previously tested positive for COVID-19 underwent 129 XeMRI ∼14-months post-acute infection across three centres. Long COVID was defined as persistent dyspnea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully-recovered. 129 XeMRI ventilation defect percent (VDP) and membrane (Mem)/Gas, red blood cell (RBC)/Mem and RBC/Gas ratios were used in k-means clustering for long COVID, and measurements were compared using ANOVA with post-hoc Bonferroni correction. Results: We evaluated 135 participants across three centres: 28 COVID-negative (40±16yrs), 34 fully-recovered (42±14yrs) and 73 long COVID (49±13yrs). RBC/Mem (p=0.03) and FEV 1 (p=0.04) were different between long- and COVID-negative; FEV 1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster1 was the youngest with normal MRI and mild gas-trapping; Cluster2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster3 had mildly increased Mem/Gas with normal PFTs; and Cluster4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern. Conclusion: We identified four 129 XeMRI long COVID phenotypes with distinct characteristics. 129 XeMRI can dissect pathophysiologic heterogeneity of long COVID to enable personalised patient care. Competing Interests: Conflicts of interest: Rachel Eddy reports grants from Michael Smith Health Research BC, Canadian Respiratory Research Network and Natural Sciences and Engineering Research Council Canada, consulting fees from VIDA Diagnostics Inc., payment or honoraria for lectures from Thorasys Thoracic Medical Systems Inc., support for attending meetings and/or travel from Canadian Institutes of Health Research – Institute of Circulatory and Respiratory Health, and a leadership or fiduciary role for the Xenon MRI Clinical Trials Consortium (Steering Committee Member). Conflicts of interest: David Mummy reports consultancy fees from Polarean Imaging plc. Conflicts of interest: Firoozeh Gerayeli reports grants from MITACS Accelerate. Conflicts of interest: Jonathon Leipsic reports grants from GE Healthcare, consultancy fees and support for attending meetings and/or travel from Heartflow, and owns stock/stock options in Heartflow. Conflicts of interest: Janice Leung reports grants from Canadian Institutes of Health Research and BC Lung Foundation, payment or honoraria for lectures, presentations, manuscript writing or educational events from BC Lung Foundation, participation on a data safety monitoring board or advisory board for Enhance Quality Safety, and Patient experience in Chronic Obstructive Pulmonary Disorder (EQuiP COPD), and leadership or fiduciary roles for Canadian Respiratory Research Network and CanCOLD Study. Conflicts of interest: Bastiaan Driehuys reports grants from Translating Duke Health (Duke Internal Award), royalties or licenses from Polarean Imaging, a leadership or fiduciary role with Polarean Imaging, and owns stock/stock options in Polarean Imaging. Conflicts of interest: Loretta G. Que reports grants from Translating Duke Health (Duke Internal Award), and a leadership or fiduciary role as member of XeMRI Consortium. Conflicts of interest: Mario Castro reports grants from NIH, ALA, PCORI, AstraZeneca, Gala Therapeutics, Genentech, GSK, Novartis, Pulmatrix, Sanofi-Aventis, Shionogi and Theravance, consulting fees from Genentech, Teva, Sanofi-Aventis, Merck, Novartis, Arrowhead Pharmaceuticals, Allakos, Amgen, OM Pharma, Pfizer, Pioneering Medicines and GSK, payment or honoraria for lectures, presentations, manuscript writing or educational events from Amgen, AstraZeneca, Genentech, Regeneron, Sanofi-Aventis and Teva, and owns stock/stock options in Aer Therapeutics. Conflicts of interest: Don D. Sin reports payment or honoraria for lectures, presentations, manuscript writing or educational events from GSK, AZ and BI, and participation on a data safety monitoring board or advisory board for NHLBI. Conflicts of interest: Peter Niedbalski reports grants from Scleroderma Foundation (new investigator grant) and American Heart Association (CDA 930177), and consultancy fees, payment or honoraria for lectures, and support for attending meetings and/or travel from Polarean Imaging Plc. Conflicts of interest: The remaining authors have no potential conflicts of interest to disclose. (Copyright ©The authors 2024.)
Databáze:	MEDLINE
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