ICU predictive factors of fibrotic changes following
COVID-19 related ARDS: a RECOVIDS substudy

Matthieu Demeyere, Isabelle Fournel, Amadou-Khalilou Sow, Stéphanie Gélinotte, Martine Nyunga, Anissa Berraies, Marie Labruyère, Alexandre Ampere, Bertrand Sauneuf, Cédric Daubin, et al.

► To cite this version:

Matthieu Demeyere, Isabelle Fournel, Amadou-Khalilou Sow, Stéphanie Gélinotte, Martine Nyunga, et al. ICU predictive factors of fibrotic changes following COVID-19 related ARDS: a RECOVIDS substudy. Annals of Intensive Care, 2025, 15 (1), pp.177. 10.1186/s13613-025-01577-2. hal-05354192

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# ICU predictive factors of fibrotic changes following COVID-19 related ARDS: a RECOVIDS substudy

Matthieu Demeyere¹, Isabelle Fournel², Amadou-Khalilou Sow², Stéphanie Gélinotte³, Martine Nyunga⁴, Anissa Berraies⁵, Marie Labruyère⁶,⁷,⁸, Alexandre Ampere⁹, Bertrand Sauneuf¹⁰, Cédric Daubin¹¹, Agathe Delbove¹², Julio Badie¹³, Pierre Bulpa¹⁴ ID, David Delacour¹⁵, Clotilde Lefevre¹, Saad Nseir¹⁶,¹⁷, Elise Artaud-Macari¹⁸, Michel Ramakers¹⁹, Vanessa Bironneau²⁰, Hugues Georges²¹, Walid Oulehri²², Arnaud-Felix Miailhe²³,²⁴, Nicolas Delberghe²⁵, Béatrice La Combe²⁶, Elise Redureau²⁷, Caroline Clarot²⁸, Nicholas Sedillot²⁹, Thierry Dugernier³⁰, David Schnell³¹, Laurie Lagache³², Charlotte Salmon Gandonniere³³, Julien Maizel³⁴, Thierry Vanderlinden³⁵, Gaël Bourdin³⁶, Mélanie Adda³⁷, Gaëtan Plantefeve³⁸, Gaëtan Beduneau³⁹ ID, Marjolaine Georges⁴⁰, Jean-Pierre Quenot⁶,⁷,⁸, Pierre-Louis Declercq³* ID and RECOVIDS Study Group and the CRICS-TRIGGERSEP and BOREAL Research Networks

**Abstract**

**Background** Pulmonary fibrotic changes (FC) following COVID-19-related ARDS represent a significant concern due to the potential respiratory complications. The identification of early predictive factors for FC and the development of predictive tools are needed to optimize patient management and outcomes.

**Methods** This observational prospective multicentre study is a substudy of the RECOVIDS study and included 32 centres in France and Belgium. COVID-19 ARDS survivors were included if they met the Berlin ARDS criteria or if they received high flow oxygen therapy (flow ≥ 50 L/min and $FiO_2$ ≥ 50%). Exclusion criteria were non-attendance at follow-up 6 ± 1 months after ICU discharge, lack of baseline or follow-up chest CT, and history of interstitial lung disease. The primary endpoint was presence of FC at follow-up CT. The secondary outcome was to identify predominant radiological patterns.

**Results** Among 555 patients included in the RECOVIDS study, 440 were analysed, of whom 162 (36.8%) had FC at follow-up. Predictive factors for FC included older age, body mass index < 30, Charlson comorbidity index ≥ 1, invasive mechanical ventilation, early signs of FC, and greater lung involvement on baseline CT. The nomogram for predicting pulmonary FC yielded an AUC of 80.6% (95%CI (76.4–84.8)). Late organizing pneumonia was the most common pattern overall and 30 (18.5%) of the 162 patients with FC presented mainly anterior fibrosis compatible with post ventilatory changes.

Full list of author information is available at the end of the article

<figure><img src="image_3.png" /></figure>

<footnote>*Correspondence:
Pierre-Louis Declercq
pdeclercq@ch-dieppe.fr</footnote>
**Conclusion** In this large cohort of COVID-19 ARDS survivors, 36.8% exhibited FC at 6 months post-ICU discharge. The key predictors identified here could guide therapeutic and follow-up strategies.

**Keywords** ARDS, COVID-19, Fibrosis, Computed tomography, Risk factors

## Introduction

The SARS-CoV-2 pandemic, necessitate the exploration and management of late consequences of acute infection, particularly respiratory sequelae. Among the most concerning is pulmonary fibrosis, which can develop following viral infection [1], and carries a risk of clinical impact, including respiratory disability and impaired quality of life (QoL), somewhat similar to idiopathic pulmonary fibrosis (IPF) [2]. The possibility of fibrosing lung injury post-COVID-19 has been documented [3], yet the course of such injury remains unclear. Its occurrence appears to be associated with age and the severity of the initial respiratory insult, especially in case of acute respiratory distress syndrome (ARDS) [4]. The pulmonary and systemic inflammatory response following SARS-CoV-2 aggression is more pronounced in patients who develop ARDS potentially sustaining an inflammatory and/or autoimmune process that could lead to interstitial lung lesions [5]. Additionally, ARDS fibrotic sequelae were described long before COVID-19 era [6]. Acute COVID-19 infection and IPF share common cellular and molecular features, raising concerns about a similar progressive potential in some patients [7]. Early identification in the intensive care unit (ICU) of patients at highest risk of fibrotic changes (FC) could help optimize their management and follow-up. We therefore aimed to identify predictors of post-COVID-19 pulmonary fibrosis, based on data from the initial chest computed tomography (CT) and ICU stay in a cohort of COVID-19 ARDS survivors.

## Methods

### Study design

This prospective observational multicentre study is a sub-study of the RECOVIDS study, performed in 30 centres in France and 2 in Belgium. The protocol and results of the main study have previously been published [8, 9]. The RECOVIDS study was approved by the ethics committee "Comité de Protection des Personnes Sud Méditerranée II" on 10/07/2020 under the number 2020-A02014-35 for France, and by the ethics committee of Namur on 11/12/2020 under the number B0392020000073 for Belgium. Oral informed consent was obtained from all patients.

All adult patients admitted to ICU due to SARS-CoV-2 infection confirmed by polymerase chain reaction, in any of the participating centres, were eligible. To be included, patients had to have undergone baseline CT at the initial phase of management; have ARDS diagnosed according to the Berlin 2012 definition [10] or have received high

flow nasal oxygen (HFOT) with a flow of at least 50L/ minute with $FiO_2$ > 50% and a $PaO_2/FiO_2$ ratio ≤ 200. The non-inclusion criteria of the main study [9] are presented in the additional file (eMethods 1).

Specific exclusion criteria for this substudy were: failure to attend follow-up CT, or a history of interstitial lung disease (ILD). Additionally, patients in whom baseline CT was performed more than 21 days after ICU admission, or in whom the follow-up CT showed FC but was done less than 4 months after baseline CT, or patients with CT scan of insufficient quality to assess the primary outcome were excluded from the statistical analysis as protocol violations.

## Procedures

Patients were screened at ICU discharge and during hospitalization in post-ICU units. The follow-up, including a follow-up CT, was scheduled at 6 ± 1 months after ICU discharge as previously described [9].

Following anonymization, radiological data were transferred to the coordinating centre on a secure server for central interpretation.

## Outcomes and chest CT analysis

The primary outcome was presence of FC, defined as interstitial lesions with volume loss and/or architectural distortion and/or traction bronchiectasis, and/or honey-combing at follow-up CT. FC at baseline, termed later in the text "early fibrotic signs" and follow-up, termed "FC", were independently assessed by two thoracic radiologists, both actively involved in ILD multidisciplinary meetings. Discrepancies were collaboratively resolved during a concurrent third review session. For the purposes of the present study, radiological features on baseline CT at the acute phase were described using the same terminology applied in fibrotic ILD and follow-up CT evaluation, allowing systematic tracking of changes. However, the authors acknowledge that the use of fibrotic descriptors—particularly traction bronchiectasis—in the acute setting should be avoided as such features may be reversible and not indicative of established fibrosis. Moreover seemingly pre-existing subpleural fibrotic interstitial lung abnormalities on baseline CT were not considered as FC on baseline [11], or at follow-up, unless fibrotic progression was observed after reviewing both CTs. Quality was rated as follows: 1-no artefact; 2-mild artefact; 3-moderate artefact (<10%), 4-severe artefact (>10%), 5-any artefact precluding a conclusion as to the presence of FC. CT scans rated 5 were excluded from the statistical analysis.
The main secondary outcomes of radiological signs rated at baseline and follow-up CT are listed in additional file (eMethods 2). The extent of lesions was quantified using a visual method through an average score (0–100%) and an automated method using the CT Pneumonia Analysis application on Syngo.Via (Siemens Healthineers, Erlangen, Germany), see additional file (eMethods 3).

The main pattern on follow-up CT was categorized as one of the following: complete resolution (confirmed using minimal intensity projection), minimal lesions < 5% of any area (isolated reticulation, pleural bands), residual ground glass opacities (GGO), late organizing pneumonia (OP) with or without distortion, anterior fibrosis, and any other types of fibrotic pattern [12].

The non-radiological secondary outcomes were the clinical and functional respiratory function, assessed using the modified Medical Research Council dyspnea scale (mMRC) [13], pulmonary function tests (PFT) and the 6-min walk test (6MWT) [14]. Quality of life (QoL) was evaluated using the Visual Simplified Respiratory Questionnaire (VRSQ) [15] and Short Form Health Survey modified 36 (SF-36) [16].

## Statistical analysis

All analyses were performed using R (version 4.3.0; R Core Team, 2023). After assessing normality using the Shapiro-Wilk test, continuous variables were summarized as means with standard deviation (SD) or as medians with interquartile ranges (IQR) for normally and non-normally distribution respectively. Group differences for continuous variables were tested using Student's t-test for normally distributed data and the Wilcoxon test for non-normally distributed data. Categorical variables are presented as counts and proportions, and were compared using the Fisher exact or Chi-square test, as appropriate. Lung involvement was presented as mean percentage of the total lung or pulmonary region, considered as continuous variables and expressed as median (IQR). Missing data were reported in the tables, by reporting subjects with available data as [N0] but excluded from statistical analyses.

Variables associated with FC at follow-up were identified using the strategy below. Initially, candidate variables (additional file, Table S1) with a p-value < 0.15 by univariable analysis were included in a multivariable logistic regression model, excluding those with few events or redundancy (full model). A comparison of area under the curve (AUC) was performed to evaluate the diagnostic performance of automated versus visual quantification of lung involvement on baseline CT. The best variable was chosen for inclusion as the lung extent parameter in the multivariable model (eFigure 1).

Collinearity between adjustment variables was verified by calculating the variance inflation factor (VIF); all

values were < 5, indicating absence of multicollinearity. Backward stepwise selection was applied to obtain final model, with model improvements assessed by the Akaike Information Criterion (AIC).

The Box-Tidwell test was used to check the non-linearity of continuous variables. Model calibration was assessed with a visual calibration plot and the Hosmer-Lemeshow test. Results are presented as odds ratios (ORs) and 95% confidence intervals (CIs).

Internal validation of the model was performed using cross-validation: the population was randomly split into a 80% training set and a 20% test set [17]. Model performance was evaluated using the area under the curve (AUC) metric, ranging from 50% (no discrimination) to 100% (perfect discrimination).

A nomogram based on the final model was constructed using the entire dataset [18]. Internal validation of the nomogram's predictive accuracy was conducted using 1000 bootstrap replications [18].

## Results

### Study population

The RECOVIDS study included a total of 555 adult patients between September 2020 and June 2021 (513 in France, 42 in Belgium). Among these, 94 had specific exclusion criteria for the present substudy. An additional 21 patients had protocol violations, leaving a total of 440 patients for the present analysis (Fig. 1).

Of these 440 patients, 162 (36.8%) had FC at follow-up. These patients were significantly older, more often men, less often obese, more frequently with Charlson score ≥ 1, and had higher median Simplified Acute Physiology Score II (SAPS II), compared to patients without FC (Table 1). Patients with FC at follow-up more often had early fibrotic signs at baseline (46% vs 29%; p < 0.001), as well as more extensive lung involvement, as assessed by visual analysis (51.8% [38.9–69.8] vs 37.5% [23.7–52.9]); p < 0.001) or with CT Pneumonia analysis (57.3% [41.7–69.8] vs 44.3 [28.8, 61.1]); p < 0.001) (Table 2). Detailed lung involvement by pulmonary region, or according to the interval between the initial CT scan and ICU admission in the FC group is described in the additional file (Table S2 and Fig. S2 respectively).

### Primary outcome

By multivariate analysis, the odds of FC at follow-up were significantly higher in older patients, those with a body mass index (BMI) below 30 kg/m², a Charlson comorbidity score of one or higher at ICU admission, those who required invasive mechanical ventilation (IMV) during the ICU stay, and those who exhibited at least 2 early fibrotic signs and greater lung involvement at baseline CT (Fig. 2) and additional file (Table S3).
<figure><img src="image_4.png" /><figcaption>**Fig. 1** Flow chart of the study population</figcaption></figure>

The nomogram incorporating these factors (Fig. 3) yielded a robust prediction of FC at six months post-ICU discharge, with an AUC of 80.6% (95% CI: 76.4–84.8) for the entire cohort. Specifically, the AUC was 81.2 and 79.8 for the training and the test set respectively (additional file, Fig. S3 and Table S4).

**Secondary outcomes**

Regarding follow-up CT evaluation (Table 3), the median interval from baseline CT was 6.5 months (6.1–7.2), the most frequent radiological pattern was late OP with or

without distortion, observed in 206 patients (47.2%). In the FC group, visual lung involvement was higher (13.7% [8.1–24.1] vs. 2.5% [1.1–6.3]; p < 0.001). The visual quantification of lung lesions decreased with increasing time elapsed since ICU discharge (p = 0.004), whereas the prevalence of FC did not vary significantly (additional file, Figs. S4 and S5, respectively). Patients in the FC group at 6 months post-ICU discharge more frequently presented shortness of breath according to the mMRC and had significantly lower SpO₂ at rest and after exertion (additional file, Tables S5 and S6). They also had significantly
<table><caption>**Table 1** Patient characteristics and management at admission and during ICU stay according to fibrotic changes</caption><thead><tr><th></th><th>All patients, N=440</th><th>No fibrotic changes N=278 (63.2)</th><th>Fibrotic changes N=162 (36.8)</th><th>P-value</th></tr></thead><tbody><tr><td>Age, mean (SD)</td><td>63.2 (11)</td><td>61.2 (11.8)</td><td>66.7 (8.6)</td><td>&lt;0.001</td></tr><tr><td>Sexe, female</td><td>134 (30.5%)</td><td>97 (34.9%)</td><td>37 (22.8%)</td><td>&lt;0.008</td></tr><tr><td>COVID-19 epidemic wave</td><td></td><td></td><td></td><td>0.362</td></tr><tr><td>First wave</td><td>148 (33.6%)</td><td>87 (31.3%)</td><td>61 (37.7%)</td><td></td></tr><tr><td>Second wave</td><td>239 (54.3%)</td><td>155 (55.8%)</td><td>84 (51.9%)</td><td></td></tr><tr><td>Third wave</td><td>53 (12.1%)</td><td>36 (12.9%)</td><td>17 (10.5%)</td><td></td></tr><tr><td>Pre-existing conditions</td><td></td><td></td><td></td><td></td></tr><tr><td>Alcohol</td><td></td><td></td><td></td><td>0.653</td></tr><tr><td>Never or former</td><td>178/399 (44.6%)</td><td>111/242 (45.9%)</td><td>67/157 (42.7%)</td><td></td></tr><tr><td>Up to one drink per day</td><td>172/399 (43.1%)</td><td>104/242 (43%)</td><td>68/157 (43.3%)</td><td></td></tr><tr><td>At least two drinks per day</td><td>49/399 (12.3%)</td><td>27/242 (11.1%)</td><td>22/157 (14%)</td><td></td></tr><tr><td>Tobacco</td><td></td><td></td><td></td><td>0.778</td></tr><tr><td>Never</td><td>206/419 (49.2%)</td><td>132/261 (50.6%)</td><td>74/158 (46.8%)</td><td></td></tr><tr><td>Current</td><td>8/419 (1.9%)</td><td>5/261 (1.9%)</td><td>3/158 (1.9%)</td><td></td></tr><tr><td>Former</td><td>205/419 (48.9%)</td><td>124/261 (47.5%)</td><td>81/158 (51.3%)</td><td></td></tr><tr><td>Body Mass Index, kg/m<sup>2</sup></td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>&lt;30</td><td>248/436 (56.9%)</td><td>139/275 (50.5%)</td><td>109/161 (67.7%)</td><td></td></tr><tr><td>30- &lt;40</td><td>157/436 (36%)</td><td>110/275 (40%)</td><td>47/161 (29.2%)</td><td></td></tr><tr><td>≥40</td><td>31/436 (7.1%)</td><td>26/275 (9.5%)</td><td>5/161 (3.1%)</td><td></td></tr><tr><td>Hypertension</td><td>218 (49.5%)</td><td>129 (46.4%)</td><td>89 (54.9%)</td><td>0.084</td></tr><tr><td>Chronic Obstructive Pulmonary Disease</td><td>15 (3.4%)</td><td>10 (3.6%)</td><td>5 (3.1%)</td><td>0.776</td></tr><tr><td>Asthma</td><td>26 (5.9%)</td><td>17 (6.1%)</td><td>9 (5.6%)</td><td>0.810</td></tr><tr><td>Diabetes mellitus</td><td>116 (26.4%)</td><td>68 (24.5%)</td><td>48 (29.6%)</td><td>0.235</td></tr><tr><td>Mild to severe chronic renal failure ≥ 265 µmol/L</td><td>7/438 (1.6%)</td><td>5/277 (1.8%)</td><td>2/161 (1.2%)</td><td>0.999</td></tr><tr><td>Congestive heart failure</td><td>13 (3%)</td><td>8 (2.9%)</td><td>5 (3.1%)</td><td>0.999</td></tr><tr><td>Solid tumor with or without metastases within 5 years</td><td>24 (5.5%)</td><td>14 (5%)</td><td>10 (6.2%)</td><td>0.613</td></tr><tr><td>Hematological malignancy</td><td>14 (3.2%)</td><td>7 (2.5%)</td><td>7 (4.3%)</td><td>0.299</td></tr><tr><td>Charlson comorbidity index ≥ 1</td><td>193/438 (44.1%)</td><td>108 (38.8%)</td><td>85/160 (53.1%)</td><td>0.004</td></tr><tr><td>Simplified Acute Physiology Score II</td><td>34.5(28, 43) [438]</td><td>32 (26, 42) [276]</td><td>36.5 (30, 45.7) [162]</td><td>&lt;0.001</td></tr><tr><td>Sequential Organ Failure Assessment score</td><td>4 (2.75, 6)</td><td>4 (3, 5.75)</td><td>4 (2.2, 7)</td><td>0.068</td></tr><tr><td>C-reactive Protein maximum blood level, mg/L</td><td>152.2 (99,218.2) [306]</td><td>148 (94.2, 213.5) [201]</td><td>162.9 (109, 225) [105]</td><td>0.111</td></tr><tr><td>Most invasive respiratory support</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>IMV</td><td>261 (59.3%)</td><td>135 (48.6%)</td><td>126 (77.8%)</td><td></td></tr><tr><td>NIV</td><td>38 (8.6%)</td><td>28 (10%)</td><td>10 (6.2%)</td><td></td></tr><tr><td>HFOT</td><td>141 (32.1%)</td><td>115 (41.4%)</td><td>26 (16%)</td><td></td></tr><tr><td>Most severe P/F ratio</td><td></td><td></td><td></td><td>0.079</td></tr><tr><td>[200; 300]</td><td>17/433 (3.9%)</td><td>10/272 (3.7%)</td><td>7/161 (4.3%)</td><td></td></tr><tr><td>[100; 200]</td><td>178/433 (41.1%)</td><td>123/272 (45.2%)</td><td>55/161 (34.2%)</td><td></td></tr><tr><td>&lt;100</td><td>238/433 (55%)</td><td>139/272 (51.1%)</td><td>99/161 (61.3%)</td><td></td></tr><tr><td>Patients receiving IMV</td><td></td><td></td><td></td><td></td></tr><tr><td>Duration, days</td><td>17 (9, 26) [251]</td><td>13.3 (8,19) [133]</td><td>21 (13.3, 33) [118]</td><td>&lt;0.001</td></tr><tr><td>Minimum of Static Compliance of the Respiratory System, ml per cm of water</td><td>26.5 (16.3, 33.7) [150]</td><td>26 (14, 35) [77]</td><td>27 (20, 33) [73]</td><td>0.891</td></tr><tr><td>PEEP max, cm of water</td><td>14 (12, 15) [244]</td><td>14 (11.7, 15) [124]</td><td>14 (12, 16) [120]</td><td>0.460</td></tr><tr><td>Maximum of Plateau Pressure, cm of water</td><td>29 (25, 32) [216]</td><td>28 (25, 32) [109]</td><td>30 (26, 33) [107]</td><td>0.064</td></tr><tr><td>Prone Positioning</td><td>175/261 (67%)</td><td>86/135 (63.7%)</td><td>89/126 (70.6%)</td><td>0.234</td></tr><tr><td>Neuromuscular blockade</td><td>231/261 (88.5%)</td><td>115/135 (85.2%)</td><td>116/126 (92.1%)</td><td>0.082</td></tr><tr><td>Inhaled nitric oxide</td><td>36/259 (13.9%)</td><td>12/133 (9%)</td><td>24/126 (19%)</td><td>0.020</td></tr><tr><td>VV ECMO</td><td>15/260 (5.8%)</td><td>7/134 (5.2%)</td><td>8/126 (6.3%)</td><td>0.697</td></tr><tr><td>VV ECMO duration, mean (SD), days</td><td>11.3 (7.6) [15]</td><td>11.6 (9.6) [7]</td><td>11.1 (6.2) [8]</td><td>0.999</td></tr><tr><td>IMV weaning</td><td></td><td></td><td></td><td></td></tr><tr><td>Tracheal re-intubation after weaning failure</td><td>42/249 (16.9%)</td><td>19/130 (14.6%)</td><td>23/119 (19.3%)</td><td>0.321</td></tr><tr><td>Tracheotomy</td><td>47/261 (18%)</td><td>13/135 (9.6%)</td><td>34/126 (27%)</td><td>&lt;0.001</td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th></th><th>All patients, N = 440</th><th>No fibrotic changes N = 278 (63.2)</th><th>Fibrotic changes N = 162 (36.8)</th><th>P-value</th></tr></thead><tbody><tr><td>Tracheotomy duration, days</td><td>16.5 (10, 31.2) [44]</td><td>14 (10, 16) [12]</td><td>19 (11.5, 35) [32]</td><td>0.097</td></tr><tr><td>NIV as most invasive respiratory support</td><td></td><td></td><td></td><td></td></tr><tr><td>PEEP max, cm of water</td><td>8 (6.5, 10) [31]</td><td>8 (6,10) [23]</td><td>9 (8, 10) [8]</td><td>0.200</td></tr><tr><td>FiO<sub>2</sub> max, %</td><td>70 (60, 84) [36]</td><td>70 (60, 88) [26]</td><td>75 (60, 81.5) [10]</td><td>0.693</td></tr><tr><td>NIV duration, days</td><td>4.5 (2.5, 6.7) [34]</td><td>4 (2, 6) [26]</td><td>5.5(4, 7) [88]</td><td>0.335</td></tr><tr><td>HFOT as most invasive respiratory support</td><td></td><td></td><td></td><td></td></tr><tr><td>Flow max, L/min</td><td>50 (50, 60) [131]</td><td>50 (50, 60) [109]</td><td>50 (50, 60) [22]</td><td>0.553</td></tr><tr><td>FiO<sub>2</sub> max, %</td><td>70 (60, 88.7) [134]</td><td>70 (60, 88.3) [110]</td><td>70 (60, 86.3) [24]</td><td>0.384</td></tr><tr><td>HFOT duration, days</td><td>6 (4, 8) [135]</td><td>6 (4, 8) [110]</td><td>7 (5, 11) [25]</td><td>0.028</td></tr><tr><td>Other organ support</td><td></td><td></td><td></td><td></td></tr><tr><td>Vasopressors</td><td>199 (45.2%)</td><td>97 (34.9%)</td><td>102 (63%)</td><td>&lt;0.001</td></tr><tr><td>Vasopressor duration, days</td><td>5 (3,11) [195]</td><td>4 (2,7) [94]</td><td>8 (3,15) [101]</td><td>&lt;0.001</td></tr><tr><td>Inotropic agent</td><td>14 (3.2%)</td><td>5 (1.8%)</td><td>9 (5.6%)</td><td>0.030</td></tr><tr><td>Renal replacement therapy</td><td>25/439 (5.7%)</td><td>10 (3.6%)</td><td>15/161 (9.3%)</td><td>0.013</td></tr><tr><td>Specific treatments for COVID-19</td><td></td><td></td><td></td><td></td></tr><tr><td>Antiviral therapies</td><td>94 (21.4%)</td><td>51 (18.3%)</td><td>43 (26.5%)</td><td>0.043</td></tr><tr><td>Immunomodulatory therapies</td><td>25 (5.7%)</td><td>10 (3.6%)</td><td>15 (9.3%)</td><td>0.013</td></tr><tr><td>Corticosteroids</td><td>260 (59.1%)</td><td>163 (58.6%)</td><td>97 (59.9%)</td><td>0.798</td></tr><tr><td>Time from ICU admission to hospital discharge, days</td><td>23 (14, 45)</td><td>19 (13, 31)</td><td>41 (20, 60.7)</td><td>&lt;0.001</td></tr><tr><td>Length of ICU stay, days</td><td>12 (7, 28)</td><td>9.5 (6, 18)</td><td>24 (12, 40.7)</td><td>&lt;0.001</td></tr></tbody></table>

Data are expressed as n (%), [N0: Number of patients with available data in case of missing data], n/N0, or median (IQR), unless stated otherwise

FiO₂ max, maximum of fraction of inspired oxygen; HFOT, high flow oxygen therapy; ICU, intensive care unit; IMV, invasive mechanical ventilation; NIV, non-invasive mechanical ventilation; P/F ratio, partial pressure of arterial oxygen divided by fraction of inspired oxygen; PEEP max, maximum of positive end-expiratory pressure; VV ECMO, veno-venous extracorporeal membrane oxygenation

more impaired forced vital capacity (FVC), total lung capacity (TLC), diffusion capacity of the lung for carbon monoxide (DL<sub>CO</sub>), and carbon monoxide transfer coefficient (K<sub>CO</sub>) (Fig. 4). Regarding QoL, only the physical domain of the SF-36 was significantly more impaired for patients with FC (additional file, Fig. S6), with no differences observed in the physical or mental summary scores (additional file, Fig. S7) or in the VSRQ (additional file, Table S5).

Discussion

To our knowledge, this study presents the largest cohort of COVID-19 ARDS survivors undergoing chest CT evaluation at a median interval of 6.5 months post-ICU discharge. In our population, FC were observed in 36.8% of the 440 survivors, a prevalence that is hard to compare with existing literature due to variations in the definitions of fibrosis and the timing of follow-up CT [4, 19, 20]. Despite a restrictive definition, our cohort exhibited a high frequency of FC, somewhat comparable to the rate of 39% observed at 4 months in the ARDS subgroup of the COMEBAC cohort [21]. The elementary lesions used, conforming to Fleischner Society's glossary from 2008 (which was in use at the time of the image interpretation) and consistent with the 2024 updated version [22], are theoretically non-regressive when identified in a chronic setting [12]. This may account for the stable prevalence

of FC observed between the earliest and latest follow-up CTs in our study, despite a reduction in the overall burden of lesions. Additionally, studies have confirmed that FC identified at 6 months remained up to 24 months post-ARDS-COVID [21, 22].

Anterior fibrosis emerged as the predominant pattern in 30 (19%) of the 160 evaluable patients with FC. IMV carries a risk of inducing or perpetuating lung injury, corresponding to the term "Ventilator-Induced Lung Injury (VILI)," whose effects may be limited by applying protective ventilation [25]. During ARDS, the anterior zones of the lungs are most accessible to IMV due to consolidation of posterior dependent lung regions. This increases the risk of regional lung overdistension in the nondependent anterior areas, potentially leading to lung injuries. This anterior localization of fibrotic damage could thus correspond to the sequelae of VILI among COVID-19-ARDS survivors who likely suffered the most in terms of ventilation, akin to survivors of ARDS of other origins who underwent prolonged IMV [26]. In our study, the duration of IMV and the proportion of tracheotomy for IMV weaning were significantly higher in the FC group. The measurement of airway plateau pressure, used to evaluate end-inspiratory intra-alveolar pressure, was higher in the FC group, albeit not statistically significant (p = 0.064), with a median of 30 cmH<sub>2</sub>O (IQR 26–33), a threshold beyond which the risk of VILI is significant [25]. Prone
<table><caption>**Table 2** Description of baseline CT according to the presence or absence of fibrotic changes</caption><thead><tr><th></th><th>All patients, N = 440</th><th>No fibrotic changes, N = 278</th><th>Fibrotic changes, N = 162</th><th>P-value</th></tr></thead><tbody><tr><td>Time between ICU admission and Baseline CT, days</td><td>0 (-2,0)</td><td>0 (-2,0)</td><td>0 (-1,0)</td><td>0.004</td></tr><tr><td>Timing of Baseline CT, in intervals (0 = ICU admission), days</td><td></td><td></td><td></td><td>0.093</td></tr><tr><td>-15;-5[</td><td>18 (4.1%)</td><td>11 (4%)</td><td>7 (4.3%)</td><td></td></tr><tr><td>-5;5[</td><td>374 (85%)</td><td>243 (87.3%)</td><td>131 (80.9%)</td><td></td></tr><tr><td>[5;10[</td><td>21 (4.8%)</td><td>13 (4.7%)</td><td>8 (4.9%)</td><td></td></tr><tr><td>[10;21]</td><td>27 (6.1%)</td><td>11 (4%)</td><td>16 (9.9%)</td><td></td></tr><tr><td>Number of early fibrotic signs</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>0</td><td>284 (64.5%)</td><td>197 (70.9%)</td><td>87 (53.7%)</td><td></td></tr><tr><td>1</td><td>79 (18.8%)</td><td>48 (17.3%)</td><td>31 (19.1%)</td><td></td></tr><tr><td>2</td><td>49 (11.1%)</td><td>24 (8.6%)</td><td>25 (15.4%)</td><td></td></tr><tr><td>≥ 3</td><td>28 (6.4%)</td><td>9 (3.2%)</td><td>19 (11.7%)</td><td></td></tr><tr><td>Type of lesion</td><td></td><td></td><td></td><td></td></tr><tr><td>Traction bronchiectasis (fibrotic sign)</td><td>80 (18.2%)</td><td>30 (10.8%)</td><td>50 (30.9%)</td><td>&lt;0.001</td></tr><tr><td>Loss of lung volume (fibrotic sign)</td><td>62 (14.1%)</td><td>29 (10.4%)</td><td>33 (20.4%)</td><td>0.004</td></tr><tr><td>Lung architectural distortion (fibrotic sign)</td><td>118 (26.8%)</td><td>64 (23%)</td><td>54 (33.3%)</td><td>0.019</td></tr><tr><td>Honeycombing (fibrotic sign)</td><td>2 (0.5%)</td><td>0 (0%)</td><td>2 (1.2%)</td><td>0.135</td></tr><tr><td>Ground-glass opacity</td><td>438 (99.5%)</td><td>277 (99.6%)</td><td>161 (99.44%)</td><td>0.999</td></tr><tr><td>Consolidation</td><td>378 (85.9%)</td><td>237 (85.3%)</td><td>141 (87%)</td><td>0.604</td></tr><tr><td>Crazy Paving</td><td>115 (26.1%)</td><td>61 (21.9%)</td><td>54 (33.3%)</td><td>0.009</td></tr><tr><td>Bronchial distortion within COVID-19 opacities</td><td>183 (41.6%)</td><td>93 (33.5%)</td><td>90 (55.6%)</td><td>&lt;0.001</td></tr><tr><td>Traction bronchiectasis outside COVID-19 opacities</td><td>21 (4.8%)</td><td>4 (1.4%)</td><td>17 (10.5%)</td><td>&lt;0.001</td></tr><tr><td>Reticulation</td><td>269 (61.1%)</td><td>157 (56.5%)</td><td>112 (69.1%)</td><td>0.009</td></tr><tr><td>Parenchymal band</td><td>219 (49.8%)</td><td>143 (51.4%)</td><td>76 (46.9%)</td><td>0.360</td></tr><tr><td>Translobular line</td><td>242 (55%)</td><td>157 (56.5%)</td><td>85 (52.2%)</td><td>0.415</td></tr><tr><td>Posterior irregular or thickened pleural interface</td><td>280 (63.6%)</td><td>170 (61.2%)</td><td>110 (67.9%)</td><td>0.156</td></tr><tr><td>Pleural effusion</td><td>155 (35.2%)</td><td>97 (34.9%)</td><td>58 (35.8%)</td><td>0.847</td></tr><tr><td>Reversed halo</td><td>18 (4.1%)</td><td>10 (3.6%)</td><td>8 (4.9%)</td><td>0.493</td></tr><tr><td>Halo</td><td>10 (2.3%)</td><td>5 (1.8%)</td><td>5 (3.1%)</td><td>0.509</td></tr><tr><td>Indicators of bacterial superinfection</td><td>49 (11.1%)</td><td>32 (11.5%)</td><td>17 (10.5%)</td><td>0.744</td></tr><tr><td>Tree-in-bud nodules</td><td>9 (2%)</td><td>4 (1.4%)</td><td>5 (3.1%)</td><td>0.299</td></tr><tr><td>Lobar or segmental single consolidation</td><td>27 (6.1%)</td><td>19 (6.8%)</td><td>8 (4.9%)</td><td>0.424</td></tr><tr><td>Endobronchial secretion</td><td>22 (5%)</td><td>15 (5.4%)</td><td>7 (4.3%)</td><td>0.618</td></tr><tr><td>Iodinated contrast agent injection</td><td></td><td></td><td></td><td>0.515</td></tr><tr><td>No</td><td>160 (36.4%)</td><td>96 (34.5%)</td><td>64 (39.5%)</td><td></td></tr><tr><td>Yes</td><td>260 (59.1%)</td><td>168 (60.4%)</td><td>92 (56.8%)</td><td></td></tr><tr><td>Both pre-contrast and post-contrast images</td><td>20 (4.5%)</td><td>14 (5%)</td><td>6 (3.7%)</td><td></td></tr><tr><td>Pulmonary embolism</td><td>42/247 (17%)</td><td>25/164 (15.2%)</td><td>17/83 (20.5%)</td><td>0.301</td></tr><tr><td>Endotracheal tube</td><td>63 (14.3%)</td><td>27 (9.7%)</td><td>36 (22.2%)</td><td>&lt;0.001</td></tr><tr><td>Evidence of Barotrauma</td><td>14/438 (3.2%)</td><td>6/277 (2.2%)</td><td>8/161 (5.2%)</td><td>0.108</td></tr><tr><td>Lung involvement</td><td></td><td></td><td></td><td></td></tr><tr><td>Visual evaluation, %</td><td>44.7 (28.1, 60.6)</td><td>37.5 (23.7, 52.9)</td><td>51.8 (38.9, 69.8)</td><td>&lt;0.001</td></tr><tr><td>Visual lung involvement, %, in categories</td><td></td><td></td><td></td><td>&lt;0.001</td></tr><tr><td>&lt; 10%</td><td>18 (4.1%)</td><td>15 (5.4%)</td><td>3 (1.9%)</td><td></td></tr><tr><td>10-25%</td><td>72 (16.4%)</td><td>59 (21.2%)</td><td>13 (8%)</td><td></td></tr><tr><td>25-50%</td><td>172 (39.1%)</td><td>119 (42.8%)</td><td>53 (32.7%)</td><td></td></tr><tr><td>50-75%</td><td>128 (29.1%)</td><td>68 (24.5%)</td><td>60 (37%)</td><td></td></tr><tr><td>&gt; 75%</td><td>50 (11.4%)</td><td>17 (6.1%)</td><td>33 (20.4%)</td><td></td></tr><tr><td>CT pneumonia analysis % of opacities, %</td><td>49.5 (33.8, 64.4) [432]</td><td>44.3 (28.8, 61.1) [273]</td><td>57.3 (41.7, 69.8) [159]</td><td>&lt;0.001</td></tr><tr><td>Opacity score (0-20)</td><td>12 (9,15) [432]</td><td>11 (9,14) [273]</td><td>14 (11,17) [159]</td><td>&lt;0.001</td></tr></tbody></table>

Data are expressed as n (%), [N0: Number of patients with available data in case of missing data], n/N0, or median (IQR), unless stated otherwise. CT: Chest computed tomography; ICU: Intensive Care Unit
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Primary analysis: multivariate analysis for predictive factors of fibrotic changes 6 months after ICU discharge. Each additional year of age and each additional percent of lung involvement on baseline CT was associated with an increase of 4% and 3% of developing fibrotic changes at 6 months, respectively. aOR; adjusted odds ratio, BMI, Body mass Index; CT, Computed tomography; IMV, Invasive Mechanical Ventilation; NIV, Non-invasive ventilation</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 3** Nomogram for predicting fibrotic changes 6 month after ICU discharge. For example, a 60-year-old [46 points] male patient [14 points] with a BMI < 30 [47 points], a Charlson comorbidity score of 1 [17 points], requiring invasive mechanical ventilation in the ICU [45 points], and a baseline CT showing two signs of fibrosis [16 points] and lung involvement of 55% [55 points], has an estimated risk of developing fibrotic changes within 6 months of 74% [240 points]. BMI, Body Mass Index; CT, Computed tomography</figcaption></figure>

positioning enhances the aeration of the dependent regions, promoting uniform ventilation and reducing the risk of overdistension [25], however we observed no protective effect against FC in our cohort.

Late OP with or without distortion was the most commonly encountered CT pattern, possibly corresponding to the specific residual impact of COVID-19 as demonstrated in histologic specimen from COVID-19 survivors
<table><caption>**Table 3** Description of follow-up CT according to fibrotic changes after ICU discharge</caption><thead><tr><th></th><th>All patients, N=440</th><th>No Fibrotic Changes, N=278</th><th>Fibrotic Changes, N=162</th><th>P-value</th></tr></thead><tbody><tr><td>Time between ICU discharge and follow-up CT, months</td><td>6.5 (6.1, 7.2)</td><td>6.4 (5.9, 6.9)</td><td>6.7 (6.2, 7.5)</td><td><strong>0.003</strong></td></tr><tr><td>Intervals between ICU discharge and follow-up CT, months</td><td></td><td></td><td></td><td><strong>0.017</strong></td></tr><tr><td>[2;4[</td><td>10 (2.3%)</td><td>10 (3.6%)</td><td>0 (0%)</td><td></td></tr><tr><td>[4;6[</td><td>93 (21.1%)</td><td>63 (22.7%)</td><td>30 (18.5%)</td><td></td></tr><tr><td>[6;8[</td><td>289 (65.7%)</td><td>180 (64.7%)</td><td>109 (67.3%)</td><td></td></tr><tr><td>≥ 8</td><td>48 (10.9%)</td><td>25 (9%)</td><td>23 (14.2%)</td><td></td></tr><tr><td>Type of lesion</td><td></td><td></td><td></td><td></td></tr><tr><td>Traction bronchiectasis</td><td>149 (33.9%)</td><td>0 (0%)</td><td>149 (92%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Loss of lung volume</td><td>79 (18)</td><td>0 (0%)</td><td>79 (48.8%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Lung architectural distortion</td><td>107 (24.3%)</td><td>0 (0%)</td><td>107 (66%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Honeycombing</td><td>10 (2.3%)</td><td>0 (0%)</td><td>10 (6.2%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Number of fibrotic signs</td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>0</td><td>278 (63%)</td><td>278 (100%)</td><td>0 (0%)</td><td></td></tr><tr><td>1</td><td>58 (13%)</td><td>0 (0%)</td><td>58 (36%)</td><td></td></tr><tr><td>2</td><td>35 (8%)</td><td>0 (0%)</td><td>35 (22%)</td><td></td></tr><tr><td>≥ 3</td><td>69 (16%)</td><td>0 (0%)</td><td>69 (43%)</td><td></td></tr><tr><td>Combined traction bronchiectasis and architectural distortion</td><td>95 (22%)</td><td>0 (0%)</td><td>95 (58.6%)</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Predominant lesional pattern</td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>Anterior Fibrosis</td><td>30/437 (6.9%)</td><td>0/275 (0%)</td><td>30 (18.5%)</td><td></td></tr><tr><td>Other Fibrosis</td><td>20/437 (4.9%)</td><td>0/275 (0%)</td><td>20 (12.3%)</td><td></td></tr><tr><td>Minimal Lesions</td><td>26/437 (5.9%)</td><td>26/275 (9.5%)</td><td>0 (0%)</td><td></td></tr><tr><td>Late Organizing Pneumonia with distortion</td><td>103/437 (23.6%)</td><td>11/275 (4%)</td><td>92 (56.8%)</td><td></td></tr><tr><td>Late Organizing Pneumonia without distortion</td><td>103/437 (23.6%)</td><td>90/275 (32.7%)</td><td>13 (8%)</td><td></td></tr><tr><td>Complete resolution</td><td>53/437 (12.1%)</td><td>53/275 (19.3%)</td><td>0 (0%)</td><td></td></tr><tr><td>Ground-glass</td><td>102/437 (23.3%)</td><td>95/275 (34.5%)</td><td>7 (4.3%)</td><td></td></tr><tr><td>Type of ground-glass opacities</td><td></td><td></td><td></td><td>0.489</td></tr><tr><td>Mosaic attenuation</td><td>14/102 (13.7%)</td><td>12/95 (12.6%)</td><td>2/7 (28.6%)</td><td></td></tr><tr><td>Melting sugar appearance</td><td>83/102 (81.4%)</td><td>78/95 (82.1%)</td><td>5/7 (71.4%)</td><td></td></tr><tr><td>Non specific</td><td>5/102 (4.9%)</td><td>5/95 (5.3%)</td><td>0/7 (0%)</td><td></td></tr><tr><td>Lung involvement</td><td></td><td></td><td></td><td></td></tr><tr><td>Visual Evaluation, %</td><td>5.6 (1.8, 13.7) [428]</td><td>2.5 (1.1, 6.3) [268]</td><td>13.7 (8.1, 24.1) [160]</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Percentage visual lung involvement, in categories</td><td></td><td></td><td></td><td><strong>&lt;0.001</strong></td></tr><tr><td>[0–10[</td><td>278/428 (65.5%)</td><td>225/268 (84%)</td><td>53/160 (33.1%)</td><td></td></tr><tr><td>[10–25[</td><td>100/428 (23.4%)</td><td>33/268 (12.3%)</td><td>67/160 (41.9%)</td><td></td></tr><tr><td>[25; 50[</td><td>36/428 (8.4%)</td><td>9/268 (3.4%)</td><td>27/160 (16.9%)</td><td></td></tr><tr><td>[50–75[</td><td>13/428 (3.3%)</td><td>1/268 (0.4%)</td><td>12/160 (7.5%)</td><td></td></tr><tr><td>[75–100]</td><td>1/428 (0.2%)</td><td>0/268 (0%)</td><td>1/160 (0.6%)</td><td></td></tr><tr><td>Presence of Emphysema</td><td>131/434 (30.2%)</td><td>72/274(26.3)</td><td>59/160 (36.9%)</td><td><strong>0.020</strong></td></tr><tr><td>Median extent of Emphysema (% of Low Attenuation Area over total lung volume)</td><td>1.0 (0.3, 2.8) [118]</td><td>1.4 (0.3,2.9) [65]</td><td>0.7 (0.3, 2.5) [53]</td><td>0.406</td></tr></tbody></table>

Data are expressed as n (%), [N0: Number of patients with available data in case of missing data], n/N0, or median (IQR), unless stated otherwise. CT: Chest computed tomography; ICU: Intensive Care Unit

demonstrating radiological sequelae [25, 26]. Indeed, GGO and consolidations tend to regress by one year [19].

The association between OP and FC could stem from the severity of alveolar damage inflicted by the viral infection, influenced by the patient's inflammatory or immune response, and may be further compounded by impaired or delayed repair mechanisms, which are exacerbated by specific risk factors [3]. We suppose that the association of FC with a Charlson comorbidity score may be attributed to the increased likelihood that more

frail patients will require IMV and experience delayed or incomplete repair. Some of the risk factors for FC identified here have also been identified in other cohorts with non-severe or mixed severity COVID-19. Increasing age appears to significantly elevate the risk of FC, with a threshold identified at 50 years in one study [4]. The increased risk related to the type of ventilatory support initially used (non-invasive ventilation or IMV) has also been reported [20, 27]. Interestingly, 26 of 162 patients with FC (16%) had HFOT as maximal respiratory
<figure><img src="image_7.png" /><figcaption>**Fig. 4** Main respiratory outcomes according to lung fibrotic changes 6 months after ICU discharge</figcaption></figure>

support. The higher prevalence of emphysema observed in the FC group—despite comparable rates of tobacco use and somewhat similar extent—may be explained by the greater use of IMV, as well as more severe acute disease in this group, as previously suggested in the literature [20]. In this study, the baseline CT played a crucial role in identifying patients at risk of FC. The association between the extent of initial lesions and FC has already been demonstrated, whether through a semi-quantitative analysis by the severity score [4, 29] or automated quantification [30]. However, the relevance of identifying early signs of potential fibrosis had not yet been reported, with an adjusted OR of 3.7 (95%CI 1.5–10) for predicting FC. We noted that early fibrosis was absent in over half of the patients who later developed FC. Conversely, these signs regressed in under a third of the patients without FC at follow-up, underscoring both the potential for repair and the risk of developing FC later during the ICU stay.

Interestingly, obesity (BMI > 30 kg/m²) appeared to protect against FC, despite its association with an increased risk of COVID-19 ARDS [31]. The protective effect of obesity on non-COVID-19 ARDS mortality, previously termed “the obesity paradox” [32], remains uncertain in ARDS-COVID-19, since one meta-analysis found no significant impact on the survival of obese patients [31]. In obese patients ventilated invasively, hypoxemia is often the consequence of atelectasis and mismatch between ventilation and perfusion [33]. This may lead to an overestimation of ARDS severity related to collapsed but unharmed lung areas that exhibit no scarring once the atelectasis is resolved. Furthermore, inflammatory and metabolic factors linked to obesity could account for

lesser lung involvement and ultimately fewer sequelae [30, 31].

Our study suggests a significant clinical impact of FC, since patients with FC had increased dyspnea, lower post-exertion SpO₂, and covered 29 m less on the 6MWT, although this difference borders statistical significance, it remains clinically relevant, particularly given that the observed effect was not explained by the higher prevalence of emphysema in the FC group [34]. These clinical characteristics could be linked to FC and possible exertional hypoxemia. However, it is likely that the consequences of intensive care on the functional state, notably neuromuscular, had a significant impact, since patients in the FC group more frequently required IMV, and had a hospital stay more than twice as long as patients without FC. Nevertheless, direct impairment from FC should not be ruled out, as PFT revealed a restrictive respiratory profile, along with lower DLco and no elevation of Kco, further advocating for a functional reality linked with the FC on follow-up CT [35]. In our study, the residual lesions in the FC group affected a median of 14% (8–24) of the lungs, surpassing the 10% threshold proposed by Mehta et al. to deem lesions symptomatically significant [1]. The impact on QoL across the cohort was more significant in physical than in mental terms, comparable to what is usually described in the post-ARDS recovery period [36]. However, the QoL related to the physical state (physical role) was more markedly diminished in the FC group.

The potential impact of FC on clinical outcomes highlights the possible benefit of antifibrotic treatments and the need for early identification of high-risk patients [37]. A nomogram derived from our cohort can evaluate the
risk of FC, enabling tailored assessment of patients based on initial data, including CT. Visual lesion extent on baseline CT in patients with FC varied with the time from admission to ICU, indicating that scans performed more than five days before ICU admission may underestimate lesion burden. Maximal lesion quantification occurred between five and ten days, suggesting this as the most suitable interval for future assessment, at a time where all the selected risk factors are readily accessible.

Our study has several limitations. Firstly, we cannot rule out that FC on CT preceded ARDS and COVID-19. However, the exclusion of patients with advanced respiratory histories and seemingly pre-existing interstitial abnormalities limited this possibility. Secondly, our evaluation at 6 months after ICU discharge may overestimate FC, as some residual non-fibrotic lesions may still improve over time. Current data suggest that FC would remain stable. Thirdly, it is difficult to distinguish between symptoms arising from FC and those related to post intensive care syndrome in these patients. A further evaluation more distant from ICU discharge could yield a better understanding of this issue. Finally, the model for predicting FC may not be generalizable to ARDS of other aetiologies due to the specificities of COVID-19 ARDS even if these ones are still debate [38] 39. However, the identified risk factors should be taken into consideration in future studies.

## Conclusion

Post COVID-19 ARDS FC were frequently observed at 6 months post-ICU discharge. Two major lesion patterns were identified, namely anterior fibrosis, suggesting sequelae from IMV, and more commonly, a pattern of late OP with varying degrees of distortion, which may correspond to viral sequelae. Furthermore, our study highlighted clinical and radiological factors which could enable the early identification of patients who may benefit from targeted follow-up and specific management.

## Abbreviations

<table><tr><td>6MWT</td><td>6-Minute walk test</td></tr><tr><td>ARDS</td><td>Acute respiratory distress syndrome</td></tr><tr><td>AUC</td><td>Area under the curve</td></tr><tr><td>BMI</td><td>Body mass index</td></tr><tr><td>CIs</td><td>Confidence intervals</td></tr><tr><td>COVID-19</td><td>Corona Virus Disease-2019</td></tr><tr><td>CT</td><td>Computed tomography</td></tr><tr><td>DLco</td><td>Diffusion capacity of the lung for carbon monoxide</td></tr><tr><td>FC</td><td>Fibrotic changes</td></tr><tr><td>FiO<sub>2</sub></td><td>Fraction of inspired Oxygen</td></tr><tr><td>FVC</td><td>Forced vital capacity</td></tr><tr><td>GGO</td><td>Ground glass opacities</td></tr><tr><td>HFOT</td><td>High flow nasal oxygen</td></tr><tr><td>ICU</td><td>Intensive care unit</td></tr><tr><td>ILD</td><td>Interstitial lung disease</td></tr><tr><td>IMV</td><td>Invasive mechanical ventilation</td></tr><tr><td>IPF</td><td>Idiopathic pulmonary fibrosis</td></tr><tr><td>IQR</td><td>Interquartile ranges</td></tr><tr><td>Kco</td><td>Carbon monoxide transfer coefficient</td></tr></table>

<table><tr><td>mMRC</td><td>Medical Research Council dyspnea scale</td></tr><tr><td>OP</td><td>Organizing pneumoniae</td></tr><tr><td>ORs</td><td>Odds ratios</td></tr><tr><td>PFT</td><td>Pulmonary function tests</td></tr><tr><td>QoL</td><td>Quality of life</td></tr><tr><td>SAPS II</td><td>Simplified Acute Physiology Score II</td></tr><tr><td>SD</td><td>Standard deviation</td></tr><tr><td>SF-36</td><td>Short Form Health Survey modified 36</td></tr><tr><td>TLC</td><td>Total lung capacity</td></tr><tr><td>VILI</td><td>Ventilator-induced lung injury</td></tr><tr><td>VSRQ</td><td>Visual Simplified Respiratory Questionnaire</td></tr></table>

## Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13613-025-01577-2.

Additional file1 (DOCX 1084 kb)

## Acknowledgements

The authors thank participating ICUs; the department of Radiology of the University Hospital of Rouen, France, the General Hospital Center of Dieppe, France and the Burgundy University Hospital of Dijon, France for their support; Siemens Healthineers for conducting the automated analysis of the radiological data, Emilie Galizzi, project leader (INSERM C1432 Epidémiologie Clinique, Dijon, France); Delphine Pecqueur, data- manager (INSERM C1432 Epidémiologie Clinique, Dijon, France); and Fiona Ecarnot, PhD (EA3920, University Hospital Besançon, France) for translation and critical revision. RECOVIDS study group: Antoine Rivière, Service de Réanimation Polyvalente, CH d'Abbeville, France; Mélody de Jesus, Sébastien Normant, Jean-Nicolas Dacher, Département de Radiologie, CHU de Rouen, Rouen, France; Thomas Stoup, Service de Pneumologie, CH de Béthune, France; Christophe Vinsonneau, Service de Médecine Intensive Réanimation, CH de Béthune, France; Anne Dewatine, Unité de Recherche Clinique, CH de Béthune, France; Pierre Cuchet, Service de Pneumologie, CHU de Caen, France; Delphine Rots, Unité de Recherche Clinique, CHU de Caen, France; Julien Calus, Service de Médecine Intensive Réanimation, CH Public du Cotentin, Cherbourg-en-Cotentin, France; Gabriel Le Moel, Service de Pneumologie, CH du Cotentin, Cherbourg-en-Cotentin, France; Pierre Kalfon, Gaëtan Badre, Jean-François Roy, Service de Médecine Intensive Réanimation, CH Chartres, France; Laura Federici, Service de Médecine Intensive Réanimation, Hôpital Louis Mourier, Colombes, France; Christopher Sacarabany, Olivier Kordzinski, Service de Radiologie, CH de Dieppe, France; Marie Marcq, Thomas Georges, Service de Pneumologie, CHD Vendée, France; Caroline Pouplet, Service de Médecine Intensive Réanimation, CH La Roche sur Yon, France; Mehdi Bousta, Service de Réanimation Médico-Chirurgicale, Groupe Hospitalier du Havre, Le Havre, France; Nicolas Masson, Matthieu Devos, Pneumology unit, St Philibert hospital, Lille catholic university, Lille, France; Raphaël Favory, Sébastien Preau, Alexandre Gaudet, Service de Médecine Intensive Réanimation, CHRU Roger Salengro, Lille, France; Pierre Bouju, Service de Réanimation Polyvalente, Groupe Hospitalier Bretagne Sud, Lorient, France; Lidia Nichita, Pascal Maignan, Service de Pneumologie, Centre Hospitalier Mémorial de Saint Lô, France; Laurence Labourot, Service de Médecine Physique et Réadaptation, CH de Tourcoing, France; Francesco Molinari, Service de Radiologie, CH de Tourcoing, France; Laurence Thirard, Service de Pneumologie, CH de Tourcoing, France; Charlotte Larrat, Service de Médecine Intensive Réanimation, CHRU Bretonneau, Tours, France; Fernando Berdaguer, Service de Médecine Intensive Réanimation, Hôpital Nord-Franche Comté, Trevenans, France; Meltem Karakaya Akgun, Service de Pneumologie, Hôpital Nord Franche-Comte, Trevenans, France; Yannick Fedun, Service de Réanimation Polyvalente, CHBA Vannes, Vannes, France; Thiphaine Guy, Service de Pneumologie, CHBA, Vannes, France; Marie Gousseff, Service de Médecine Interne et Maladies Infectieuses, CHBA Vannes, Vannes, France; Déborah Boyer, Medical intensive care unit, Rouen university hospital, France; Tristan Bonnevie, Normandie Univ, UNIROUEN, EA3830, CHU Rouen, ADIR Association; Elsa Demarest, Service de Médecine Intensive Réanimation, CHES Evreux, France; Sami Hraiech, Aude Sylvestre, Céline Sanz, Service de Médecine Intensive Réanimation, Hôpital Nord, Marseille, France; Anne Veinstein, Service de Médecine Intensive Réanimation, CHU de Poitiers, France; Xavier Tchenio, Service de Réanimation Polyvalente, Centre Hospitalier Fleyriat, Bourg en Bresse, France
## Author contributions

MD, IF, AKS and PLD, conceived and coordinated the design and overall study conduct, data analysis, and manuscript writing, and vouch for the data analysis accuracy. MD and DD analysed all chest CT as thoracic radiologist. MD and PLD participated in literature search CL participated in initial study design, literature search and data interpretation. IF and AKS supervised methodology, constructed the figure and did the statistical analysis. All other authors except MD, DD and CL participated in patient recruitment. All authors participated in the study conduct. All authors revised the manuscript for important intellectual content and approved the final manuscript. MD, IF, AMS, JPQ and PLD had direct access to the data and are responsible for the decision to submit for publication.

## Funding

None.

## Availability of data and material

Study data will be made available upon reasonable requests made to the corresponding author.

## Declarations

### Ethics approval and consent to participate

The study was approved by the ethics committee "Comité de Protection des Personnes Sud Méditerranée II" on 10/07/2020 under the number 2020-A02014-35 for France, and by the ethics committee of Namur on 11/12/2020 under the number B0392020000073 for Belgium.

### Consent for publication

Not applicable.

### Competing interests

MD reports lecture fees, travel support, and consulting fees from Boehringer Ingelheim.

## Author details

¹Service de Radiologie, CHU Rouen, 76000 Rouen, France

²CHU Dijon Bourgogne, INSERM, Université de Bourgogne, CIC 1432, Module Epidémiologique Clinique, 21000 Dijon, France

³Service de Médecine Intensive Réanimation, Centre Hospitalier de Dieppe, 76200 Dieppe, France

⁴Service de Médecine Intensive Réanimation, CH de Roubaix, Roubaix, France

⁵Service de Pneumologie, CH Chartres, Chartres, France

⁶Department of Intensive Care, Burgundy University Hospital, Dijon, France

⁷Lipness Team, INSERM Research Center LNC-UMR1231 and LabEx LipSTIC, University of Burgundy, Dijon, France

⁸INSERM CIC 1432, Clinical Epidemiology, University of Burgundy, Dijon, France

⁹Service de Pneumologie, CH de Béthune, Béthune, France

¹⁰Service de Médecine Intensive Réanimation, CH Public du Cotentin, Cherbourg-en-Cotentin, France

¹¹Department of Medical Intensive Care, CHU de Caen Normandie, Caen, France

¹²Service de Réanimation Polyvalente, CHBA Vannes, Vannes, France

¹³Service de Médecine Intensive Réanimation, Hopital Nord Franche-Comte, Trevenans, France

¹⁴Service des Soins Intensifs, Mont-Godinne University Hospital, CHU UCL Namur, Yvoir, Belgium

¹⁵Service de Radiologie, Clinique du Cèdre, Bois-Guillaume, France

¹⁶Service de Médecine Intensive Réanimation, CHU de Lille, Lille, France

¹⁷Inserm U1285, Univ. Lille, CNRS, UMR 8576-UGSF-Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France

¹⁸Department of Pneumology, Thoracic Oncology and Respiratory Intensive Care Unit, Normandie Univ, UNIROUEN, EA3830, CHU Rouen, Rouen, France

¹⁹Service de Médecine Intensive Réanimation, Centre Hospitalier Mémorial de Saint-Lô, Saint-Lô, France

²⁰Service de Pneumologie, CHU Poitiers, INSERM CIC 1402, ALIVES Research Group, Université de Poitiers, Poitiers, France

²¹Service de Médecine Intensive Réanimation, CH de Tourcoing, Tourcoing, France

²²Service de Réanimation Chirurgicale, CHRU Strasbourg, Strasbourg, France

²³Service de Médecine Intensive Réanimation, CHU Nantes, Nantes, France

²⁴Department of Intensive Care Medicine, St James Hospital, Dublin, Ireland

²⁵Service de Pneumologie, CHES Evreux, Evreux, France

²⁶Service de Réanimation Polyvalente, Groupe Hospitalier Bretagne Sud, Lorient, France

²⁷Service de Pneumologie, CHD Vendée, Vendée, France

²⁸Service de Pneumologie, CH d'Abbeville, Abbeville, France

²⁹Service de Réanimation Polyvalente, Centre Hospitalier Fleyriat, Bourg en Bresse, France

³⁰Service de Soins Intensifs, Clinique Saint Pierre, 1340 Ottignies, Belgique

³¹Service de Réanimation Polyvalente et USC, CH d'Angoulême, d'Angoulême, France

³²Service de Réanimation Médico-Chirurgicale, Groupe Hospitalier du Havre, Le Havre, France

³³Service de Médecine Intensive Réanimation, CHRU de Tours, INSERM CIC 1415, CRICS-TrIGGERSep., Tours, France

³⁴Service de Médecine Intensive Réanimation, CHU d'Amiens, d'Amiens, France

³⁵Intensive Care Unit, St Philibert Hospital, ETHICS EA 7446, Lille Catholic University, Lille, France

³⁶Service de Réanimation Polyvalente, Hôpital Saint Joseph Saint Luc, Lyon, France

³⁷Service de Médecine Intensive Réanimation, Hôpitaux de Marseille, Hôpital Nord, Marseille, France

³⁸Service de Médecine Intensive Réanimation, CH d'Argenteuil, d'Argenteuil, France

³⁹Department of Medical Intensive Care, Univ Rouen Normandie, GRHVN UR 3830, CHU Rouen, 76000 Rouen, France

⁴⁰Department of Pulmonary Medicine and Intensive Care Unit, University Hospital, Dijon, France

Received: 30 March 2025 / Accepted: 18 September 2025

Published online: 04 November 2025

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