Quantitative Chest Computed Tomography and Machine Learning for Subphenotyping Small Airways Disease in Long COVID.

PURPOSE: To investigate imaging phenotypes in posthospitalized COVID-19 patients by integrating quantitative CT (QCT) and machine learning (ML), with a focus on small airway disease (SAD) and its correlation with plethysmography.

MATERIALS AND

METHODS

In this single-center cross-sectional retrospective study, a subanalysis of a larger prospective cohort, 257 adult survivors from the initial COVID-19 peak (mean age, 56±13 y; 49% male) were evaluated. Patients were admitted to a quaternary hospital between March 30 and August 31, 2020 (median length of stay: 16 [8-26] d) and underwent plethysmography along with volumetric inspiratory and expiratory chest CT 6 to 12 months after hospitalization. QCT parameters were derived using AI-Rad Companion Chest CT (Siemens Healthineers).

RESULTS

Hierarchical clustering of QCT parameters identified 4 phenotypes among survivors, named "SAD," "intermediate," "younger fibrotic," and "older fibrotic," based on clinical and imaging characteristics. The SAD cluster (n=37, 14%) showed higher residual volume (RV) and RV/total lung capacity (TLC) ratios as well as lower FEF 25-75 /forced vital capacity (FVC) on plethysmography. The older fibrotic cluster (n=42, 16%) had the lowest TLC and FVC values. The younger fibrotic cluster (n=79, 31%) demonstrated lower RV and RV/TLC ratios and higher FEF 25-75 than the other phenotypes. The intermediate cluster (n=99, 39%) exhibited characteristics that were intermediate between those of SAD and fibrotic phenotypes.

CONCLUSION

The integration of inspiratory and expiratory chest CT with quantitative analysis and ML enables the identification of distinct imaging phenotypes in long COVID patients, including a unique SAD cluster strongly associated with specific pulmonary function abnormalities.