C73-07 Ventilation and Perfusion Abnormalities in Individuals With Dyspnea Due to Post-Acute Sequelae of Sars-Cov-2

Rationale Post-Acute Sequelae of SARS-CoV-2 (PASC) affects an estimated 10% of those recovering from COVID-19, with dyspnea being one of the common symptoms affecting up to 41% of those with PASC. Despite extensive physiologic testing and imaging, the etiology of dyspnea in many individuals with PASC remains unclear. The purpose of this study is to describe ventilation and perfusion abnormalities seen on computed tomography imaging utilizing functional respiratory imaging. Methods Participants 18 years of age or older reporting persistent symptoms of dyspnea, fatigue, or exercise intolerance at least 3 months after their primary SARS-CoV-2 infection and healthy controls were enrolled. For each participant, symptom surveys, quality of life scores, pulmonary function testing (PFTs), six-minute walk test (6MWT), and non-contrast CT chest images were obtained. CT images were analyzed using CT:VQ software (4D Medical, Australia), which estimates ventilation and perfusion parameters in lung tissue by analyzing mapped CT-derived Hounsfield units within paired non-contrast inspiratory and expiratory CT images. Variables were analyzed using the Mann Whitney U test and results were reported as median and interquartile range. Results 7 individuals with PASC and dyspnea were compared to 18 individuals with no dyspnea. Those with dyspnea were older and had a lower perception of their own current overall health as assessed by the EQ-5D-5L VAS (Table 1). Baseline PFTs and 6MW distance were normal and were not significantly different between the 2 groups. Despite unremarkable baseline inspiratory CT Chest imaging, perfusion heterogeneity and perfusion defect percent were significantly higher in the dyspnea compared to the non-dyspnea cohort. Conclusion Individuals with PASC and dyspnea demonstrate normal conventional CT chest imaging and pulmonary function testing, but may have underlying perfusion abnormalities that could be detected with non-contrast chest imaging using CT:VQ software. Additional study and further analyses are needed to confirm these findings and to determine their clinical significance. This abstract is funded by: 4D Medical