# Temporal stability of inflammatory subphenotypes of acute respiratory distress syndrome: 28-day insights from the ICAR trial

Laurent Renard Triché, Maxime Fosset, Matthieu Jabaudon, Emmanuel

Futier, Bruno Lucas, Claire Latroche, Béla-Simon Paschold, Elias

Baedorf-Kassis, Tarek Sharshar, Maximilian S Schaefer, et al.

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► To cite this version:

Laurent Renard Triché, Maxime Fosset, Matthieu Jabaudon, Emmanuel Futier, Bruno Lucas, et al. Temporal stability of inflammatory subphenotypes of acute respiratory distress syndrome: 28-day insights from the ICAR trial. Anaesthesia Critical Care & Pain Medicine, 2025, 44 (5), pp.101559. 10.1016/j.accpm.2025.101559. hal-05379253

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Original Article

Temporal stability of inflammatory subphenotypes of acute
respiratory distress syndrome: 28-day insights from the ICAR trial

<figure><img src="image_4.png" /></figure>

Laurent Renard Triché<sup>a,b,c</sup>, Maxime Fosset<sup>d,e,f</sup>, Matthieu Jabaudon<sup>a,b</sup>, Emmanuel Futier<sup>a,b</sup>, Bruno Lucas<sup>g</sup>, Claire Latroche<sup>h</sup>, Béla-Simon Paschold<sup>f,i</sup>, Elias Baedorf-Kassis<sup>f,j</sup>, Tarek Sharshar<sup>j</sup>, Maximilian S Schaefer<sup>f,i,i</sup>, Boris Jung<sup>d,i,j,m</sup>, Aurélien Mazeraud<sup>k,n</sup>, Joris Pensier<sup>f,i,o,*</sup>

ARTICLE INFO

Article history:
Available online 26 May 2025

Keywords:
Acute respiratory distress syndrome
Sub phenotyping
Clustering
COVID-19
Mechanical ventilation

A B S T R A C T

**Background:** International guidelines have emphasized the necessity of evaluating the temporal stability of acute respiratory distress syndrome (ARDS) subphenotypes. This study aimed to assess the temporal stability of subphenotypes of ARDS over 28 days.

**Methods:** A reanalysis of a randomized trial was conducted, including patients with COVID-19-related moderate-to-severe ARDS across 43 centers. A K-means clustering was conducted to identify subphenotypes at 7-day intervals from inclusion to day 28. A Bayesian discrete-time Markov model was constructed to assess the temporal stability of subphenotypes.

**Results:** Two subphenotypes were identified among 146 patients. At inclusion, 121 (83%) patients were in the hypoinflammatory subphenotype and 25 (17%) in the hyperinflammatory subphenotype. The hyperinflammatory subphenotype was associated with higher rates of organ failure, higher plasma levels of cytokines, chemokines, adhesion molecules, and proangiogenic factors, and lower endothelial stability than the hypoinflammatory subphenotype. The hyperinflammatory subphenotype was associated with higher 28-day mortality (13/25, 52% vs. 30/121, 25%, *p* = 0.001) and fewer ventilatory-free-days through day 28 (*p* < 0.01) than the hypoinflammatory subphenotype. In the Bayesian Markov model, over 7-day intervals, patients in the hypoinflammatory subphenotype had a

Abbreviations: ARDS, Acute Respiratory Distress Syndrome; CD, Cluster of Differentiation; CXCL-10, C-X-C Motif Chemokine Ligand 10; ICAR, Immunoglobulins in COVID-19 ARDS Randomized trial; ICU, Intensive Care Unit; IQR, Interquartile range; IL, Interleukin; IVIG, Intravenous Immunoglobulins; PEEP, Positive End-Expiratory Pressure; PIGF, Placental Growth Factor; sTNFr1, Soluble Tumor Necrosis Factor Receptor 1; SOFA, Sequential Organ Failure Assessment; TNFα, Tumor Necrosis Factor Alpha; UMAP, Uniform Manifold Approximation and Projection; VCAM-1, Vascular Cell Adhesion Molecule-1; VFDs, Ventilator-Free Days.

<footnote><sup>a</sup> Department of Perioperative Medicine, CHU Clermont-Ferrand, Clermont-Ferrand, France</footnote>

<footnote>b iGReD, CNRS, INSERM, Université Clermont Auvergne, Clermont-Ferrand, France</footnote>

<footnote>C ECSTRA Team, IRSL, INSERM UMR1342, Université Paris Cité, Paris, France</footnote>

<footnote>d Medical Intensive Care Unit, Montpellier University and Montpellier University Health Care Center, Montpellier, 34295, France</footnote>

<footnote>e Premedical Inria-Inserm Team Idesp, University of Montpellier and Montpellier University Health Care Center, Montpellier, 34295, France</footnote>

<footnote>f Center for Anesthesia Research Excellence, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America</footnote>

<footnote>g Université Paris-Cité, Institut Cochin, Centre National de la Recherche Scientifique (CNRS) UMR8104, Institut National de la Santé et de la Recherche Médicale (INSERM) U1016, Paris, France</footnote>

<footnote>h Service d'Anesthésie-Réanimation, Groupe Hospitalier Université Paris Psychiatrie et Neurosciences, Pôle Neuro, Institut Cochin, Centre National de la Recherche Scientifique UMR8104, INSERM U1016, Paris, France</footnote>

<footnote>i Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, United States of America</footnote>

<footnote>Department of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America</footnote>

<footnote>k GHU Paris Psychiatrie et Neurosciences, Pole Neuro, Service d'anesthésie Réanimation, Université Paris Cité, Paris, France</footnote>

<footnote n="1">¹Department of Anesthesiology, Duesseldorf University Hospital, Duesseldorf, Germany</footnote>

<footnote>m PhyMedExp Laboratory, Montpellier University, INSERM, CNRS, CHRU Montpellier, Montpellier, 34295, France</footnote>

<footnote>n Memory and Perception Unit, Neurosciences Department, Institut Pasteur, Paris, France</footnote>

<footnote>o Anesthesiology and Intensive Care, Anesthesia and Critical Care Department B, Saint Eloi Teaching Hospital, PhyMedExp, University of Montpellier, INSERM U1046, Montpellier, France</footnote>

<footnote>* Corresponding author.
E-mail address: j-pensier@chu-montpellier.fr (J. Pensier).</footnote>
higher probability of remaining hypoinflammatory (70%) or being extubated (17%) than of progressing to the hyperinflammatory subphenotype (7%). Inversely, patients in the hyperinflammatory subphenotype had a higher probability of remaining in the hyperinflammatory subphenotype (52%) or dying (23%) than of transitioning to the hypoinflammatory subphenotype (20%) or being extubated (5%).

**Conclusions:** Inflammatory subphenotypes were stable in COVID-19-related ARDS, with few transitions over 28 days. Monitoring these subphenotypes could be valuable for assessing patient trajectories and treatment responses.

© 2025 The Author(s). Published by Elsevier Masson SAS on behalf of Société Française d'Anesthésie et de Réanimation (SFAR). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## Background

Acute respiratory distress syndrome (ARDS), an inflammatory lung injury resulting in refractory hypoxemia, is a common and severe cause of admission to the intensive care unit (ICU) [1,2]. The mortality of ARDS patients ranges between 35% and 50% [3,4]. The recent New Global Definition has expanded the scope of ARDS [5], aiming to reduce underdiagnosis and mitigate its associated harmful consequences [3].

However, heterogeneity within ARDS might have prevented promising interventions from finding their best indication in specific subgroups of patients [6–8]. Thus, the latest international guidelines have underlined the importance of identifying sub-phenotypes of ARDS that may respond differentially to targeted therapies [9]. Two inflammatory subphenotypes (so-called hypo-inflammatory and hyperinflammatory) of ARDS have been identified using unsupervised machine-learning algorithms [2,10,11]. Inflammatory subphenotypes of ARDS have been associated with differential responses to positive end-expiratory pressure (PEEP) or fluid strategies, as well as to simvastatin [10,12,13]. Applied in the setting of coronavirus disease 2019 (COVID-19)-related ARDS, these two inflammatory subphenotypes of COVID-19 ARDS showed differential responses to corticosteroids [14].

Little is known about the temporal stability of these sub-phenotypes beyond their first days of evolution [9,12,15,16]. Sub-phenotypes in ARDS patients have been studied up to day 4 [12,16], and up to day 7 in patients at risk of ARDS [15]. The latest international guidelines have identified the stability over time of ARDS subphenotypes as a priority research question for future studies [9], underscoring the pressing need for data beyond the early stages. Understanding the temporal evolution of these subphenotypes could be instrumental in monitoring the clinical course of patients, guiding targeted anti-inflammatory treatments, and personalizing the duration of these therapies.

The primary objective of this study was to identify inflammatory subphenotypes of COVID-19-related ARDS and to assess their temporal stability from baseline to day 28, through a secondary analysis of the Immunoglobulins in COVID-19 ARDS Randomized trial (ICAR) multicenter randomized controlled trial [17]. We hypothesized that inflammatory subphenotypes of COVID-19-related ARDS were predominantly stable over a 28-day period.

## Patients and methods

### *Study design and population*

Clinical and biological data were obtained from the ICAR trial performed in 43 recruiting French ICUs between April and October 2020 (Electronic Supplemental Manuscript) and evaluating intravenous immunoglobulins (IVIG) on 28-day outcomes in patients with COVID-19-related, moderate-to-severe ARDS

[17]. Inclusion criteria were a COVID-19 diagnosis confirmed by polymerase chain reaction in critically ill patients (≥18 years) under invasive mechanical ventilation for less than 72 h, with moderate-to-severe ARDS according to the Berlin definition [18]. Exclusion criteria were contraindication to IVIG (immuno-globin A deficit, acute renal failure at admission, allergy to IVIG) and pregnancy. The trial was approved by the Paris X ethics committee on April 10, 2020 (2020-001570-30), and the French medicine agency (ANSM) on April 9, 2020 (D20-P013). The study was conducted in accordance with the TRIPOD + AI statement for reporting clinical prediction models that use regression or machine learning methods [19].

### *Data collection and measurements*

Clinical data, biological data, and outcomes were obtained for each one of the included patients at 7-day intervals from inclusion to day 28 [17]. The percentage of pulmonary impairment was retrieved from the chest CT scans, as measured by the radiologist. Moreover, biomarkers were obtained for 57 patients (39%) from inclusion to day 28 (186 patient-timepoints overall, 43%). A multiplex cytokine immunoassay panel was used to quantify plasma concentrations of biomarkers (MesoScale Discovery, Rockville, Maryland, USA) [11,14,17]. A standard curve was generated for each set of reagents. All dosages were performed in duplicates, and all were in the detection range [17]. Quantification was performed using Workbench4.0 (MesoScale Discovery). As T-cell responses have been associated with outcomes in COVID-19 disease [20], T-cell subsets in the blood were measured in 26 patients within the first seven days, with fluorescence staining and flow cytometry [17]. The following monoclonal antibodies were used: CD3-allophycocyanin-H7 (clone SK7, dilution factor: 1/20); CD4-Pacific Blue (clone RPA-T4, 1/40); CD8-Brilliant Violet 650 301 (clone SK1, 1/50); CD25-PE-Cyanine7 (clone M-A251, 1/10); CD45RA-Brilliant Violet 711 (clone HI 100, 1/40), and γδ TCR-PerCP-Cyanine5.5 (clone B1, 1/10) from BD Biosciences; CD127-allophycocyanin (clone REA614, 1/20) from Miltenyi Biotec. In all experiments, the Live/Dead Blue Dye (Invitrogen) was used to exclude dead cells. Frozen peripheral blood mononuclear cells were incubated for 10 min in RPMI 1640 Glutamax (Gibco) supplemented with 5% FCS (Biochrom) warmed up to 37 °C then washed in phosphate-buffered saline (PBS). Cell suspensions were collected and dispensed into 96-well round-bottom microtiter plates (Greiner Bioscience; 2 × 10⁶ cells/well). Peripheral blood mononuclear cells were washed and incubated for 30 min at +4 °C with Live/Dead Blue dye in PBS. Five percent (vol/vol) heat-inactivated human AB serum (Abcys) was added for an extra 15 min at +4 °C. Next, cells were labeled for 30 min at +4 °C with antibodies diluted in PBS with 5% FCS (Biochrom) and 0.1% NaN3 (Sigma-Aldrich) PBS. Cells were then washed, and fixed with 2% paraformaldehyde, and events were acquired using a BD Fortessa flow cytometer (BD Biosciences).
## Statistical analysis

The detailed statistical protocol is detailed in the *Electronic Supplemental Manuscript*. Missing data were imputed using multiple imputations with chained equations, with 15 database imputations and 20 iterations per imputation to ensure convergence, using classification and regression trees (Table S1) [21]. Cluster analysis was performed by K-means to identify subphenotypes of ARDS every 7 days from inclusion until day 28, extubation, or death [22]. K-means, as a non-parametric method, is more suitable than latent class analysis for small datasets, ensuring greater stability and reproducibility of the identified clusters. The following variables were used: age, body mass index, cardiovascular component of the Sequential Organ Failure Assessment (SOFA) score, serum creatinine, C-reactive protein, D-dimers, white blood cell count, blood lymphocyte count, ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen, blood platelets, PEEP, inspiratory plateau pressure (measured in static conditions), compliance of the respiratory system, tidal volume, procalcitonin, and the Richmond Agitation-Sedation Scale. These variables were a priori selected based on previous ARDS subphenotyping literature, particularly studies integrating clinical and inflammatory markers [17,23], as well as on their known pathophysiological relevance in acute respiratory failure and availability in our dataset. Biomarkers such as C-reactive protein, D-dimers, lymphocyte count, and procalcitonin were included to capture systemic inflammation and coagulopathy, along with organ failure markers previously associated with inflammatory subphenotypes of ARDS (creatinine, platelets) [10,13], while ventilatory parameters and gas exchange markers were chosen to reflect the severity and mechanics of lung injury [10]. The optimal number of subphenotypes was determined based on the Silhouette score [23,24].

To compare subphenotypes, a graphical representation was performed with principal component analysis, which allows dimensionality reduction [25], and by plotting the standardized values of each continuous variable by subphenotype [10]. A stability index [23] and a Uniform Manifold Approximation and Projection (UMAP) were computed to assess the stability of clusters [26]. Differences in the standardized values of biomarkers between subphenotypes were plotted after log transformation and rescaling [27]. Biomarker trajectories from baseline to 28 days according to the subphenotype at inclusion were plotted, and compared with two-way ANOVA [24].

According to their subphenotype at inclusion, patients' characteristics and outcomes were compared. Continuous variables were expressed as mean [standard deviation (SD)] or median (interquartile range [IQR], 25-75%) and compared either with the Student-t test or Mann-Whitney test as appropriate (Gaussian or non-Gaussian variables) [4]. Qualitative variables were expressed as numbers (%) and compared using an uncorrected chi-square test [4]. Heterogeneity of treatment effect for IVIG across subphenotypes was assessed on 28-day and 90-day mortality (logistic regression and Cox models) [28], and on ventilator-free days (VFD) through day 28 (zero-inflated negative binomial regression model) [29].

The temporal stability of the subphenotypes was plotted with a Sankey diagram. The Euclidean distance of each patient to their assigned cluster centroid at inclusion was measured as an indicator of the certainty with which each patient was assigned to a given subphenotype ("certainty of cluster assignment"). The association between the certainty of cluster assignment and the temporal stability of each subphenotype was assessed with univariate logistic regressions.

A Bayesian discrete-time Markov model was constructed to evaluate the transitions between four defined states: extubation,

subphenotypes according to K-means clustering, and death [30]. The model simulates the probability of progression between these states over 7-day intervals, capturing the evolution of subphenotypes and outcomes. Death, as an absorbing state, was assigned a 100% self-transition probability (e.g., individuals in the death state cannot transition to another state). A prior distribution was assigned to the self-transition probability of the extubation state, reflecting prior knowledge of a 90% likelihood of remaining in this state [31]. Posterior distributions were used to compute 95% credible intervals (95%Crl). Using the fitted Markov model, trajectories for 10,000 simulated patients were generated [32], starting from either of the ARDS subphenotypes identified with K-means clustering, to describe the evolution of subphenotypes over time.

Tests were two-sided and *p* < 0.05 were considered significant. The R software (version 4.3.1) was used to perform all statistical analyses. BioRender (Toronto, Canada) was used for figure design.

## Results

### Identification of two inflammatory subphenotypes

Overall, all the 146 patients included in the ICAR trial underwent clustering at 7-day intervals from inclusion to day 28, extubation, or death. Overall, 433 patient time points were included in the K-means clustering. The correlation matrix did not identify variables with a high correlation coefficient among the selected variables (Figure S1). The optimal number of clusters was defined as two, using the Silhouette score (Figure S2). The clusters were well separated in principal component analysis (Fig. 1A). The variables most contributing to the clustering are displayed in Fig. 1B and C. Creatinine, age, cardiovascular SOFA, and white blood cell count were the most cluster-defining variables. The stability index was 0.80, and the UMAP projection confirmed the reproducibility of the clustering (Figure S3). Among the 186 patient time points with available biomarker data, differences between subphenotypes are presented in Fig. 1D. Tumor necrosis factor α (TNFα), monocyte chemoattractant protein-1 (MCP-1), vascular cell adhesion molecule-1 (VCAM-1), interleukin-10 (IL-10), C-X-C motif chemokine Ligand 10 (CXCL-10), placental growth factor (PIGF), and the tyrosine kinase receptor Tie2 were the most cluster-defining biomarkers.

Among the 146 patients included in this analysis, 121 (83%) were classified in Subphenotype 1 at inclusion, displaying a hypoinflammatory profile (346/433 patient time points, 80%). Oppositely, 25 patients (17%) were classified in the Subphenotype 2 at inclusion, displaying a hyperinflammatory profile (87/433 patient-timepoints, 20%). The clinical, biological, and ventilatory parameters of patients at baseline are presented in Table 1. Patients in the hyperinflammatory subphenotype were significantly older (mean age: 73 years vs. 64, *p* < 0.001) and had more comorbidities at baseline. They received significantly more vasopressors (72% vs. 44%, *p* = 0.01) and had higher rates of renal failure (median renal SOFA: 3 ± 1 vs. 1 ± 1, *p* < 0.001). The hyperinflammatory subphenotype was also associated with a significantly higher percentage of pulmonary impairment assessed by CT scan (mean: 79% ±4 vs. 56% ±20, *p* = 0.01).

### Biomarkers trajectories and T-cell subsets according to subphenotypes

The main biomarkers available for the 57 patients with measurements available during the 28-day duration are plotted in Fig. 2. Overall, the hyperinflammatory subphenotype displayed significantly higher levels of pro-inflammatory and anti-inflammatory cytokines (Fig. 2A and 2B), differential expression of
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Inflammatory subphenotypes identified at inclusion with K-means clustering.</figcaption></figure>

A. Clusters separation in principal component analysis.

B. Contribution of variables to clustering in principal component analysis.

C. Differences in the standardized values of each continuous variable by subphenotype.

D. Differences in the standardized values of biomarkers by subphenotype.

WBC: white blood cell count, PCT: procalcitonin, SOFA: sequential organ failure assessment, PEEP: positive end-expiratory pressure, Pplat: inspiratory plateau pressure, Vt: tidal volume, Crs: compliance of the respiratory system, RASS: Richmond agitation-sedation scale, BMI: body mass index.

angiogenic and vascular stability factors (Fig. 2C), higher levels of chemokines (Fig. 2D), and higher levels of adhesion molecules (Fig. 2E). For most biomarkers, the trajectories over 28 days were parallel across subphenotypes (Fig. 2). Detailed results of the two-way ANOVA tests are presented in the Supplemental Appendix (Tables S2-S10).

Among the 26 patients in whom T-cell subsets were measured within the first seven days, 19 patients (73%) were in the hypoinflammatory subphenotype and 7 patients (27%) were in the hyperinflammatory subphenotype. The hyperinflammatory subphenotype displayed significantly lower overall T-cells (median [×10³ cells/µL]: 30 vs. 72, *p* = 0.02), lower memory CD8+T-cells (median [×10³ cells/µL]: 1.0 vs. 2.5, *p* < 0.01), higher memory CD4 / memory CD8 ratio (median: 21 vs. 13, *p* = 0.04), and lower γδ T-cells (median [×10³ cells/µL]: 30 vs. 72, *p* = 0.049, Table S11).

### Clinical outcomes between subphenotypes

Patients in the hyperinflammatory subphenotype had fewer VFDs through day 28 (median [IQR]: 0 [0–0] vs. 0 [0–9], *p* = 0.01 in the zero-inflated submodel, *p* = 0.79 in count submodel, Table 2, Figure S4), higher 28-day mortality rates (14/25, 52% vs. 30/121,

25%, *p* = 0.001), and higher 90-day mortality rates (15/25, 60% vs. 33/121, 27%, *p* < 0.001, Figure S5) than those in the hypoinflammatory subphenotype.

There were no significant interactions between subphenotypes and IVIG administration for 28-day or 90-day mortality, or for VFDs through day 28 (Table 3).

### Temporal stability of subphenotypes

Among the 146 included patients, 125 (86%) remained in the same subphenotype throughout 28 days, and 21 (14%) shifted from one subphenotype to the other (Table S12). Among patients in the hyperinflammatory subphenotype at inclusion, 24% shifted during the 28 days to the hypoinflammatory subphenotype (6/25), those patients had a 28-day mortality rate of 33%. Oppositely, 12% of the patients in the hypoinflammatory subphenotype at inclusion shifted during the 28 days to the hyperinflammatory subphenotype (15/121), those patients had a 28-day mortality rate of 53%. The transitions between states, from inclusion to 28 days, are reported per 7-day intervals in Fig. 3A. The certainty of cluster assignment was not significantly associated with the transitions between subphenotypes (*p* = 0.40 for hypoinflammatory patients
<table><caption>**Table 1**
Clinical, ventilatory, and biological parameters at inclusion.</caption><thead><tr><th>Variable (mean±(SD) or %)</th><th>Overall (n = 146)</th><th>Hypoinflammatory<br>subphenotype (n = 121)</th><th>Hyperinflammatory<br>subphenotype (n = 25)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years)</td><td>66 (±11)</td><td>64 (±11)</td><td>73 (±8)</td><td>&lt;0.001</td></tr><tr><td>Male sex, n (%)</td><td>103 (71%)</td><td>84 (69%)</td><td>19 (76%)</td><td>0.51</td></tr><tr><td>BMI (kg/m²)</td><td>31 (±6)</td><td>31 (±6)</td><td>30 (±5)</td><td>0.48</td></tr><tr><td>COPD, n (%)</td><td>10 (7%)</td><td>7 (6%)</td><td>3 (12%)</td><td>0.37</td></tr><tr><td>Chronic heart disease, n (%)</td><td>17 (11%)</td><td>10 (8%)</td><td>7 (25%)</td><td>&lt;0.01</td></tr><tr><td>Arterial hypertension, n (%)</td><td>65 (45%)</td><td>53 (44%)</td><td>12 (48%)</td><td>0.82</td></tr><tr><td>Diabetes, n (%)</td><td>23 (16%)</td><td>16 (13%)</td><td>7 (28%)</td><td>0.11</td></tr><tr><td>Chronic kidney disease, n (%)</td><td>7 (5%)</td><td>1 (1%)</td><td>6 (24%)</td><td>&lt;0.001</td></tr><tr><td>Hematologic malignancy, n (%)</td><td>9 (6%)</td><td>6 (5%)</td><td>3 (12%)</td><td>0.37</td></tr><tr><td>Time between onset and ICU admission (days)</td><td>10 (±7)</td><td>10 (±7)</td><td>9 (±4)</td><td>0.51</td></tr><tr><td>Pulmonary embolism at inclusion, n (%)</td><td>12 (8%)</td><td>9 (7%)</td><td>3 (12%)</td><td>0.43</td></tr><tr><td>Tidal volume (mL/kg of PBW)</td><td>6.17 (±0.89)</td><td>6.14 (±0.82)</td><td>6.61 (±1.20)</td><td>0.39</td></tr><tr><td>PEEP (cmH₂O)</td><td>12 (±3)</td><td>12 (±3)</td><td>12 (±3)</td><td>0.78</td></tr><tr><td>Inspiratory plateau pressure (cmH₂O)</td><td>25 (±5)</td><td>24 (±5)</td><td>28 (±4)</td><td>&lt;0.01</td></tr><tr><td>Compliance of the respiratory system (mL/cmH₂O)</td><td>33 (±18)</td><td>34 (±19)</td><td>30 (±14)</td><td>0.35</td></tr><tr><td>PaO₂/FiO₂ (mmHg)</td><td>124 (±47)</td><td>125 (±47)</td><td>116 (±50)</td><td>0.39</td></tr><tr><td>Percentage of pulmonary impairment</td><td>60 (±20)</td><td>56 (±20)</td><td>79 (±4)</td><td>0.01</td></tr><tr><td>SOFA score</td><td>6 (±3)</td><td>6 (±3)</td><td>8 (±3)</td><td>&lt;0.001</td></tr><tr><td>SAPS II</td><td>41 (±12)</td><td>39 (±12)</td><td>47 (±12)</td><td>&lt;0.01</td></tr><tr><td>Vasopressor use, n (%)</td><td>71 (49%)</td><td>53 (44%)</td><td>18 (72%)</td><td>0.01</td></tr><tr><td>Serum creatinine (µmol/L)</td><td>99 (±105)</td><td>75 (±29)</td><td>205 (±112)</td><td>&lt;0.001</td></tr><tr><td>Blood platelets (G/L)</td><td>290 (±122)</td><td>294 (±113)</td><td>273 (±157)</td><td>0.44</td></tr><tr><td>White blood cell count (G/L)</td><td>10.2 (±6.4)</td><td>9.6 (±5.0)</td><td>12.8 (±10.5)</td><td>0.02</td></tr><tr><td>Blood lymphocyte count (G/L)</td><td>1.2 (±3.9)</td><td>0.9 (±1.2)</td><td>3.0 (±9.3)</td><td>0.02</td></tr><tr><td>Serum C-reactive protein (mg/L)</td><td>160 (±95)</td><td>152 (±96)</td><td>194 (±87)</td><td>0.06</td></tr><tr><td>Serum procalcitonin (g/L)</td><td>1.47 (±3.85)</td><td>0.99 (±2.25)</td><td>3.22 (±6.97)</td><td>0.02</td></tr><tr><td>D-dimer (ng/mL)</td><td>2,643 (±3,093)</td><td>2,292 (±2,256)</td><td>4,016 (±5,067)</td><td>0.02</td></tr><tr><td>Complement C3 (mg/dL)</td><td>827 (±667)</td><td>932 (±657)</td><td>288 (±443)</td><td>0.03</td></tr><tr><td>Complement C4 (mg/dL)</td><td>257 (±320)</td><td>298 (±334)</td><td>49 (±59)</td><td>0.08</td></tr></tbody></table>

SD: standard difference; BMI: body mass index; COPD: chronic obstructive pulmonary disease; PBW: predicted body weight; PaO₂/FiO₂: ratio of the partial pressure of arterial oxygen (PaO₂) to the fraction of inspired oxygen, PEEP: positive end-expiratory pressure; SOFA: sequential organ failure assessment; SAPS II: simplified acute physiological score II.

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Main plasma biomarker concentrations in the hypoinflammatory and hyperinflammatory subphenotypes.</figcaption></figure>

Left panel: differences according to K-means clustering at all timepoints. Right panel: changes over time.

A. Pro-inflammatory cytokines.

B. Anti-inflammatory cytokines.

C. Angiogenic and vascular stability factors.

D. Pro-inflammatory chemokines.

E. Adhesion molecule.
<table><caption>**Table 2**
Clinical outcomes according to the subphenotype identified at inclusion.</caption><thead><tr><th></th><th>Hypoinflammatory subphenotype (n = 121)</th><th>Hyperinflammatory subphenotype (n = 25)</th><th>p-value</th></tr></thead><tbody><tr><td>28-day mortality, n (%)</td><td>30/121 (25%)</td><td>14/25 (52%)</td><td>0.001</td></tr><tr><td>90-day mortality, n (%)</td><td>33/121 (27%)</td><td>15/25 (60%)</td><td>&lt;0.001</td></tr><tr><td>Ventilator-free-days through 28-days, median [IQR]</td><td>0 [0–9]</td><td>0 [0–0]</td><td>&lt;0.01</td></tr></tbody></table>

IQR: interquartile range.

<table><caption>**Table 3**
Interaction between inflammatory subphenotypes and treatment (intravenous immunoglobulins) on clinical outcomes.</caption><thead><tr><th></th><th>Variable</th><th>Estimate<sup>a</sup> [95%CI]</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="3">28-day mortality (logistic regression)</td><td>Hyperinflammatory subphenotype</td><td>3.96 [1.64–9.95]</td><td>&lt;0.01</td></tr><tr><td>IVIG</td><td>1.48 [0.74–2.99]</td><td>0.27</td></tr><tr><td>Hyperinflammatory subphenotype x IVIG</td><td>1.72 [0.28–11.25]</td><td>0.56</td></tr><tr><td rowspan="3">28-day mortality (Cox model)</td><td>Hyperinflammatory subphenotype</td><td>2.71 [1.47–4.99]</td><td>0.001</td></tr><tr><td>IVIG</td><td>1.35 [0.77–2.39]</td><td>0.30</td></tr><tr><td>Hyperinflammatory subphenotype x IVIG</td><td>1.28 [0.37–4.42]</td><td>0.69</td></tr><tr><td rowspan="2">Ventilator-free-days through day 28 (count model)</td><td>Hyperinflammatory subphenotype</td><td>1.08 [0.62–1.87]</td><td>0.79</td></tr><tr><td>IVIG</td><td>1.01 [0.78–1.30]</td><td>0.96</td></tr><tr><td rowspan="3">Ventilator-free-days through day 28 (zero-inflated model)</td><td>Hyperinflammatory subphenotype x IVIG</td><td>5.34 [1.50–18.94]</td><td>0.01</td></tr><tr><td>IVIG</td><td>1.12 [0.57–2.21]</td><td>0.75</td></tr><tr><td>Hyperinflammatory subphenotype</td><td></td><td>0.50</td></tr></tbody></table>

at inclusion, *p* = 0.36 for hyperinflammatory patients at inclusion). There was no significant effect of IVIG administration on transitions between subphenotypes (*p* = 0.61 for hypoinflammatory patients at inclusion, *p* = 0.91 for hyperinflammatory patients at inclusion).

Then, a transition matrix (Table S13) and a Bayesian discrete-time Markov model were constructed to evaluate, for each 7-day interval, the probability of transitioning from one subphenotype to the other, to extubation, or to death (Fig. 3B). Per each 7-day interval, patients in the hypoinflammatory subphenotype had a 70% (95%CrI: 65%–75%) probability of remaining in the same subphenotype, with a 17% (13%–21%) probability of extubation and a 6% (3%–8%) probability of death. Conversely, the probability of evolving from the hypoinflammatory to the hyperinflammatory subphenotype was 7% (5%–9%) per 7-day interval. Per each 7-day interval, patients in the hyperinflammatory subphenotype demonstrated a 52% (95%CrI 41%–62%) probability of remaining in the same subphenotype, with a 23% (15%–33%) probability of death and a 5% (2%–11%) probability of extubation. Notably, the probability of transitioning from the hyperinflammatory to the hypoinflammatory subphenotype was 20% (12%–29%) per 7-day interval. Patients who were extubated had a 2% (1%–3%) probability of death per 7-day interval.

At day 28, patients in the hypoinflammatory subphenotype at inclusion had a 23% probability of remaining in the hypoinflammatory subphenotype, a 7% probability of transitioning to the hyperinflammatory subphenotype, a 48% probability of being extubated, and a 22% probability of death (Figure S6). On the contrary, patients in the hyperinflammatory subphenotype at inclusion had a 12% probability of remaining in the hyperinflammatory subphenotype, a 13% of transitioning to the hypoinflammatory subphenotype, a 19% probability of being extubated, and a 57% probability of death (Figure S7).

## Discussion

In this secondary analysis of the ICAR multicenter randomized controlled trial, the main objective was to assess the temporal stability of inflammatory subphenotypes of ARDS from inclusion to 28 days. Inflammatory subphenotypes of ARDS were stable over

28 days, with a low probability (14%) of transitioning from one subphenotype to the other during the whole period. The hypoinflammatory subphenotype had higher probabilities of remaining hypoinflammatory or leading to extubation. On the other hand, patients in the hyperinflammatory subphenotype at inclusion had a higher probability of remaining in this subphenotype or of dying by day 28. The hyperinflammatory subphenotype displayed higher levels of pro-inflammatory cytokines and chemokines, differential expression of angiogenic and vascular stability factors, higher levels of adhesion molecules, lower overall T-cells, memory CD8+ T-cells, and γδ T-cells than the hypoinflammatory subphenotype.

This study is the first to report the high stability, up to 28 days, of COVID-19-related ARDS subphenotypes over time, with 86% of patients remaining in the same subphenotype from inclusion to day 28, extubation, or death. To our knowledge, the longitudinal assessment of the stability of ARDS subphenotypes was only performed until day 4 before this study [11,12,15,23,33,34]. Interestingly, the clinical outcomes of the 21 patients who switched between subphenotypes were similar to those of the subphenotype to which those patients switched. Only 12% (15/121) of patients in the hypoinflammatory subphenotype at baseline transitioned to the hyperinflammatory subphenotype over time; however, their 28-day mortality rate reached 53%, similar to the 52% rate observed in patients in the hyperinflammatory subphenotype at baseline. On the other hand, 24% (6/24) of patients who were in the hyperinflammatory subphenotype at baseline switched to the hypoinflammatory subphenotype over time; their 28-day mortality rate was 33%, closer to the 25% rate observed in patients in the hypoinflammatory subphenotype at baseline. This finding supports regular reassessment of ARDS subphenotypes to monitor patient trajectories and guide treatment. This highlights the need for a reliable tool to identify swiftly and iteratively ARDS subphenotypes from readily available data at the bedside [28,35].

These results underscore the importance of developing personalized treatments aiming to shift the subphenotype in hyperinflammatory patients. Considering that subphenotypes are linked with markedly different outcomes and that their natural course tends towards stability, it becomes crucial to identify treatments that can facilitate a transition, particularly from

<footnote><sup>a</sup> Hazard ratio for the Cox model, Odds ratios for logistic regression model and zero-inflated submodel, relative risk for count submodel. CI: Confidence Interval, IVIG: Intravenous immunoglobulins, IQR: Interquartile range.</footnote>
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Temporal evolution of inflammatory subphenotypes in a four-state model: hypoinflammatory subphenotype, hyperinflammatory subphenotype, extubation, or death.</figcaption></figure>

A. Sankey diagram.

B. Bayesian discrete-time Markov model transition probabilities per 7-day intervals with 95% Credible Intervals.

hyperinflammatory to hypoinflammatory states. Additionally, the observation that patients who transition between subphenotypes also experience a shift in their prognosis further emphasizes the potential clinical benefits of such therapeutic interventions. However, IVIG does not appear to be this treatment [36], as no signs of treatment response heterogeneity were identified in this reanalysis of the ICAR trial.

K-means clustering identified two inflammatory subphenotypes, thus reproducing the previous reports using either K-prototypes clustering [23] or latent class analysis [37]. First, the clinical variables that best distinguish those subphenotypes in our study were creatinine [38], age, vasopressor, and white blood cell count, as previously described in patients with COVID-19-related ARDS [37]. Moreover, the most differential biomarkers in our study were TNFα and Tie-2, sharing strong correlations respectively with sTNFr1 and Angiopoietin-1, used in a recent study [37]. Finally, there was a wide difference in 28-day mortality rates between the hypoinflammatory (25%) and the hyperinflammatory subphenotype (52%). This illustrates the feasibility of identifying relevant

COVID-19 ARDS subphenotypes using readily available clinical and biological data, as previously reported in unselected ARDS patients [2,22,35]. Furthermore, our findings suggest that the biological mechanisms underlying ARDS subphenotypes may be consistent across etiologies [10,13]. Previous studies have demonstrated that a large proportion of patients with COVID-19 ARDS can be classified into inflammatory subphenotypes originally described in non-COVID ARDS [14]. The hyperinflammatory and hypoinflammatory subphenotypes in our study share the core features (elevated cytokine levels, coagulopathy, renal and cardiovascular failure) that have been robustly observed in non-COVID ARDS [10,13]. This convergence supports the external validity of our findings and suggests that inflammatory subphenotypes may represent a core biological axis of ARDS, irrespective of the underlying trigger.

This study has several limitations. First, as this cohort included COVID-19 ARDS patients, external validation in unselected ARDS is needed. Latent class analysis has been extensively used to derive clusters in ARDS patients, but this method requires larger sample
sizes to reach cluster stability. As an alternative, K-means is a non-parametric method suitable for smaller samples. Second, clinical interpretation of the longitudinal evolution of biomarkers is inherently limited by the between-subphenotype differences in mortality rates, which influences the degree of missing data over time [24,37]. Moreover, the absence of observed heterogeneity of treatment effect between IVIG administration and both clinical outcomes and subphenotype transitions may reflect a lack of statistical power. In our cohort, 14% of patients transitioned between subphenotypes over time. Although this may reflect underlying biological or treatment-related dynamics, the limited number of such events precluded robust statistical analysis of potential drivers. Finally, bicarbonate was not included in our variable selection because it was not recorded in the trial dataset [17], although it has been identified in prior studies as a potentially important predictor of ARDS subphenotypes [10,13].

The main strength of this study is the large multicentric design of the primary trial, with 43 centers involved, allowing the inclusion of patients with moderate-to-severe ARDS within a short 6-month time span [17,39]. Data collection was performed daily with few missing data, allowing this first report of the longitudinal evolution of ARDS subphenotypes up to day 28 [17]. These findings suggest that inflammatory subphenotypes are prognostically distinct, tend to remain stable over time, and may be clinically meaningful when transitions occur.

## Conclusions

In this study, two inflammatory subphenotypes of ARDS patients were stable over 28 days, with minimal transitions between the hypoinflammatory and hyperinflammatory subphenotypes. Patients in the hypoinflammatory subphenotype at baseline had a higher probability of remaining in the hypoinflammatory subphenotype or being extubated through day 28. Patients in the hyperinflammatory subphenotype at baseline had a higher probability of remaining in the hyperinflammatory subphenotype or of dying through day 28. Patients who switched subphenotype also switched their clinical outcomes. These results suggest a potential value in monitoring subphenotypes over time to assess patient trajectories and treatment responses.

## CRediT authorship contribution statement

LRT, MF, MJ, AM, and JP designed the study. BL, CL, TS, AM, and JP acquired the data. LRT, MF, BL, CL, BSP, and JP performed the data analysis. LRT, MF, MJ, EF, BSP, EBK, MSS, BJ, AM, and JP were involved in data interpretation. LRT, MJ, and JP drafted the manuscript and all authors reviewed it. All authors approved the final version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

## Consent for publication

Not applicable.

## Ethics approval, consent to participate

The study was approved by the Paris X ethics committee (2020-001570-30) on April 10, 2020, and by the French medicine agency (ANSM) on April 9, 2020 (D20-P013). The study has been led in accordance with Good Clinical Practice guidelines and the principles of the Declaration of Helsinki of 1975.

## Funding

The study was funded by the Société Française d'Anesthésie-Réanimation, the University Hospital of Montpellier, the Philippe Foundation, and Harvard Medical School. The ICAR trial was funded by a grant from the French ministry of Health (ICAR-PHRC 2020 COVID-19-20-0052) and Laboratoire Français du fractionnement et des Biotechnologies supplied the intravenous immunoglobulins for free.

## Availability of data and materials

Deidentified participant data will be made available upon request to the corresponding author of the original trial (A Mazeraud). Proposals will be reviewed and approved by the sponsor, researchers, and staff, according to scientific merit and absence of competing interests. Once the proposal has been approved, data can be transferred through a secure online platform after signing a data access agreement and a confidentiality agreement.

## Declaration of competing interest

Elias Baedorf Kassis has received research funding from the NIH, Draeger, and Hamilton Medical, and honorarium for conference lectures from Hamilton Medical. The others authors declare no competing interests.

## Acknowledgements

We acknowledge the Société Française d'Anesthésie-Réanimation Research Network for supporting the study. We thank Prof. Sylvie Chevret for her guidance on the methodology and her review of the manuscript. We also thank the participating intensive care units and their staff, the patients, and their families for their participation in the study.

## Appendix A. Supplementary data

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.accpm.2025.101559.

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