AUTHOR=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 

TITLE=Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

JOURNAL=Frontiers in Medicine

VOLUME=9

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.1083264

DOI=10.3389/fmed.2022.1083264

ISSN=2296-858X

ABSTRACT=<sec><title>Introduction</title><p>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).</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>Two hundred thirty-two (<italic>n</italic> = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and &lt;60 in 74.2% (<italic>n</italic> = 172), 21.1% (<italic>n</italic> = 49), and 4.7% (<italic>n</italic> = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and &lt;60 in 69.4% (<italic>n</italic> = 161), 15.5% (<italic>n</italic> = 36), and 15.1% (<italic>n</italic> = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (<italic>n</italic> = 10), between 5 and 29% in 48.7% of patients (<italic>n</italic> = 113) and &lt;5% in 47.0% of patients (<italic>n</italic> = 109). The extent of reticulation was ≥30%, 5–29% and &lt;5% in 1.3% (<italic>n</italic> = 3), 24.1% (<italic>n</italic> = 56), and 74.6% (<italic>n</italic> = 173) of the cohort, respectively. Patients (<italic>n</italic> = 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 (<italic>p</italic> = 0.01) and +14.6 (<italic>p</italic> = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.</p></sec><sec><title>Conclusion</title><p>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.</p></sec>