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

Abstract

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.

Keywords: antifibrotics; interstitial lung disease; long COVID; machine learning; post-COVID-19.

FIGURE 1

Functional features at the 3-month follow-up. Percentage of patients with reduced FVC% predicted (A), FEV1% predicted (B), and DLCO% predicted (C) is presented.

FIGURE 2

Radiographic features at the 3-month follow-up based on Imbio Lung Texture Analysis 2.1. Percentage of patients with% extent ground glass (A) and % extent reticulation (B) above 30, between 5 and 29, as well as below 5 is presented.

FIGURE 3

Representative images from Imbio Lung Texture Analysis 2.1 of a patient with post-COVID-19-ILD (A). Notice that almost 80% of the lung parenchyma presents with reticular abnormalities that are inconspicuous (at least to that extent) to the bear eye of the operator (B,C).

FIGURE 4

Treatment algorithm for patients with post-COVID-19 interstitial lung disease (A). The proportion of patients that belongs to each group is shown in panel (B). CTPA, computed tomography pulmonary angiogram; GGO, ground glass opacities; G6PD, glucose-6-phosphate dehydrogenase; HBV, hepatitis B virus; HCV, hepatitis C virus; HIV, human immunodeficiency virus; MICO, masterful inactivity with cat-like observation; OCS, oral corticosteroids; OP, organizing pneumonia; TMP/SMX, trimethoprim-sulfamethoxazole; TST, tuberculin skin test; 6MWD: 6 min walking distance.

FIGURE 5

Patients with fibrotic-like changes and persistently decreased functional indices received antifibrotics. ANOVA repeated measures was used to investigate differences in FVC% predicted at different time points (A). Subgroup analysis of patients with 1-year follow-up is presented in panel (B).

FIGURE 6

Patients with fibrotic-like changes and persistently decreased functional indices received antifibrotics. ANOVA repeated measures was used to investigate differences in DLCO% predicted at different time points (A). Subgroup analysis of patients with 1-year follow-up is presented in panel (B).