The European respiratory journal · 2024
Cluster analysis to identify long COVID phenotypes using 129Xe magnetic resonance imaging: a multicentre evaluation.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Eddy Rachel L, Mummy David, Zhang Shuo, Dai Haoran, Bechtel Aryil, Schmidt Alexandra, Frizzell Bradie, Gerayeli Firoozeh V, Leipsic Jonathon A, Leung Janice M, Driehuys Bastiaan, Que Loretta G, Castro Mario, Sin Don D, Niedbalski Peter J
Аннотация
BACKGROUND: Long COVID impacts ∼10% of people diagnosed with coronavirus disease 2019 (COVID-19), yet the pathophysiology driving ongoing symptoms is poorly understood. We hypothesised that 129Xe 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 129Xe MRI ∼14 months post-acute infection across three centres. Long COVID was defined as persistent dyspnoea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully recovered. 129Xe MRI ventilation defect percent (VDP) and membrane-to-gas (Mem/Gas), red blood cell-to-membrane (RBC/Mem) and red blood cell-to-gas (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 (mean±sd age 40±16 years), 34 fully recovered (42±14 years) and 73 long COVID (49±13 years). RBC/Mem (p=0.03) and forced expiratory volume in 1 s (FEV1) (p=0.04) were different between long COVID and COVID-negative; FEV1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster 1 was the youngest with normal MRI and mild gas trapping; Cluster 2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster 3 had mildly increased Mem/Gas with normal PFTs; and Cluster 4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern.
CONCLUSIONS: We identified four 129Xe MRI long COVID phenotypes with distinct characteristics. 129Xe MRI can dissect pathophysiological heterogeneity of long COVID to enable personalised patient care.
METHODS: COVID-negative controls and participants who previously tested positive for COVID-19 underwent 129Xe MRI ∼14 months post-acute infection across three centres. Long COVID was defined as persistent dyspnoea, chest tightness, cough, fatigue, nausea and/or loss of taste/smell at MRI; participants reporting no symptoms were considered fully recovered. 129Xe MRI ventilation defect percent (VDP) and membrane-to-gas (Mem/Gas), red blood cell-to-membrane (RBC/Mem) and red blood cell-to-gas (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 (mean±sd age 40±16 years), 34 fully recovered (42±14 years) and 73 long COVID (49±13 years). RBC/Mem (p=0.03) and forced expiratory volume in 1 s (FEV1) (p=0.04) were different between long COVID and COVID-negative; FEV1 and all other pulmonary function tests (PFTs) were within normal ranges. Four unique long COVID clusters were identified compared with recovered and COVID-negative. Cluster 1 was the youngest with normal MRI and mild gas trapping; Cluster 2 was the oldest, characterised by reduced RBC/Mem but normal PFTs; Cluster 3 had mildly increased Mem/Gas with normal PFTs; and Cluster 4 had markedly increased Mem/Gas with concomitant reduction in RBC/Mem and restrictive PFT pattern.
CONCLUSIONS: We identified four 129Xe MRI long COVID phenotypes with distinct characteristics. 129Xe MRI can dissect pathophysiological heterogeneity of long COVID to enable personalised patient care.