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

Abstract

Long COVID impacts ∼10% of people diagnosed with 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 129XeMRI ∼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. 129XeMRI 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. 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 FEV1 (p=0.04) were different between long- 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. 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. We identified four 129XeMRI long COVID phenotypes with distinct characteristics. 129XeMRI can dissect pathophysiologic heterogeneity of long COVID to enable personalised patient care.