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

Annals of Intensive Care volume 15, Article number: 177 (2025) Published: 04 November 2025

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 FiO2 ≥ 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.

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.

 

 

 

 

Imaging the acute respiratory distress syndrome: past, present and future

 

Intensive Care Medicine volume 48, pages 995–1008 (2022) Published: 14 July 2022

 

In patients with the acute respiratory distress syndrome (ARDS), lung imaging is a fundamental tool in the study of the morphological and mechanistic features of the lungs. Chest computed tomography studies led to major advances in the understanding of ARDS physiology. They allowed the in vivo study of the syndrome’s lung features in relation with its impact on respiratory physiology and physiology, but also explored the lungs’ response to mechanical ventilation, be it alveolar recruitment or ventilator-induced lung injuries. Coupled with positron emission tomography, morphological findings were put in relation with ventilation, perfusion or acute lung inflammation. Lung imaging has always been central in the care of patients with ARDS, with modern point-of-care tools such as electrical impedance tomography or lung ultrasounds guiding clinical reasoning beyond macro-respiratory mechanics. Finally, artificial intelligence and machine learning now assist imaging post-processing software, which allows real-time analysis of quantitative parameters that describe the syndrome’s complexity. This narrative review aims to draw a didactic and comprehensive picture of how modern imaging techniques improved our understanding of the syndrome, and have the potential to help the clinician guide ventilatory treatment and refine patient prognostication.