Cluster analysis to identify long COVID phenotypes using ¹²⁹Xe magnetic resonance imaging: a multicentre evaluation

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.

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.

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.

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.