In Frontiers in medicine
INTRODUCTION : Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).
METHODS : In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.
RESULTS : Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.
CONCLUSION : Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.
Karampitsakos Theodoros, Sotiropoulou Vasilina, Katsaras Matthaios, Tsiri Panagiota, Georgakopoulou Vasiliki E, Papanikolaou Ilias C, Bibaki Eleni, Tomos Ioannis, Lambiri Irini, Papaioannou Ourania, Zarkadi Eirini, Antonakis Emmanouil, Pandi Aggeliki, Malakounidou Elli, Sampsonas Fotios, Makrodimitri Sotiria, Chrysikos Serafeim, Hillas Georgios, Dimakou Katerina, Tzanakis Nikolaos, Sipsas Nikolaos V, Antoniou Katerina, Tzouvelekis Argyris
antifibrotics, interstitial lung disease, long COVID, machine learning, post-COVID-19