Background: There are no specific generally accepted therapies for the coronavirus disease 2019 (COVID-19). The full spectrum of COVID-19 ranges from asymptomatic disease to mild respiratory tract illness to severe pneumonia, acute respiratory distress syndrome (ARDS), multisystem organ failure, and death. The efficacy of corticosteroids in viral ARDS remains unknown. We postulated that adjunctive treatment of established ARDS caused by COVID-19 with intravenous dexamethasone might change the pulmonary and systemic inflammatory response and thereby reduce morbidity, leading to a decrease in duration of mechanical ventilation and in mortality. Methods/design: This is a multicenter, randomized, controlled, parallel, open-label, superiority trial testing dexamethasone in 200 mechanically ventilated adult patients with established moderate-to-severe ARDS caused by confirmed SARS-CoV-2 infection. Established ARDS is defined as maintaining a PaO₂/FiO₂ ≤ 200 mmHg on PEEP ≥ 10 cmH₂O and FiO₂ ≥ 0.5 after 12 ± 3 h of routine intensive care. Eligible patients will be randomly assigned to receive either dexamethasone plus standard intensive care or standard intensive care alone. Patients in the dexamethasone group will receive an intravenous dose of 20 mg once daily from day 1 to day 5, followed by 10 mg once daily from day 6 to day 10. The primary outcome is 60-day mortality. The secondary outcome is the number of ventilator-free days, defined as days alive and free from mechanical ventilation at day 28 after randomization. All analyses will be done according to the intention-to-treat principle. Discussion: This study will assess the role of dexamethasone in patients with established moderate-to-severe ARDS caused by SARS-CoV-2. Trial registration: ClinicalTrials.gov NCT04325061. Registered on 25 March 2020 as DEXA-COVID19.

Although much has evolved in our understanding of the pathogenesis and factors affecting outcome of patients with acute respiratory distress syndrome (ARDS), still there is no specific pharmacologic treatment for ARDS. Several clinical trials have evaluated the utility of corticoids but none of them has demonstrated a definitive benefit due to small sample sizes, selection bias, patient heterogeneity, and time of initiation of treatment or duration of therapy. We postulated that adjunctive treatment of persistent ARDS with intravenous dexamethasone might change the pulmonary and systemic inflammatory response and thereby reduce morbidity, leading to a decrease in duration of mechanical ventilation and a decrease in mortality. This is a prospective, multicenter, randomized, controlled trial in 314 patients with persistent moderate/severe ARDS. Persistent ARDS is defined as maintaining a PaO2/FiO2 ≤ 200 mmHg on PEEP ≥ 10 cmH2O and FiO2 ≥ 0.5 after 24 hours of routine intensive care. Eligible patients will be randomly allocated to two arms: (i) conventional treatment without dexamethasone, (ii) conventional treatment plus dexamethasone. Patients in the dexamethasone group will be treated with a daily dose of 20 mg iv from day 1 to day 5, and 10 mg iv from day 6 to day 10. Primary outcome is the number of ventilator-free days, defined as days alive and free from mechanical ventilation at day 28 after intubation. Secondary outcome is all-cause mortality at day 60 after enrollment. This study will be the largest randomized controlled clinical trial to assess the role of dexamethasone in patients with persistent ARDS. Registered on 21 November 2012 as DEXA-ARDS at ClinicalTrials.gov website ( NCT01731795 ).

Introduction: The purpose of this study was to investigate whether common variants across the nuclear factor erythroid 2-like 2 (NFE2L2) gene contribute to the development of the acute respiratory distress syndrome (ARDS) in patients with severe sepsis. NFE2L2 is involved in the response to oxidative stress, and it has been shown to be associated with the development of ARDS in trauma patients. Methods: We performed a case–control study of 321 patients fulfilling international criteria for severe sepsis and ARDS who were admitted to a Spanish network of post-surgical and critical care units, as well as 871 population-based controls. Six tagging single-nucleotide polymorphisms (SNPs) of NFE2L2 were genotyped, and, after further imputation of additional 34 SNPs, association testing with ARDS susceptibility was conducted using logistic regression analysis. Results: After multiple testing adjustments, our analysis revealed 10 non-coding SNPs in tight linkage disequilibrium (0.75 ≤ r² ≤ 1) that were associated with ARDS susceptibility as a single association signal. One of those SNPs (rs672961) was previously associated with trauma-induced ARDS and modified the promoter activity of the NFE2L2 gene, showing an odds ratio of 1.93 per T allele (95 % confidence interval, 1.17–3.18; p = 0.0089). Conclusions: Our findings support the involvement of NFE2L2 gene variants in ARDS susceptibility and reinforce further exploration of the role of oxidant stress response as a risk factor for ARDS in critically ill patients.

The acute respiratory distress syndrome (ARDS) is one of the main causes of mortality in adults admitted to intensive care units. Previous studies have demonstrated the existence of genetic variants involved in the susceptibility and outcomes of this syndrome. We aimed to identify novel genes implicated in sepsis-induced ARDS susceptibility. We first performed a prioritization of candidate genes by integrating our own genomic data from a transcriptomic study in an animal model of ARDS and from the only published genome-wide association study of ARDS study in humans. Then, we selected single nucleotide polymorphisms (SNPs) from prioritized genes to conduct a case-control discovery association study in patients with sepsis-induced ARDS (n = 225) and population-based controls (n = 899). Finally, we validated our findings in an independent sample of 661 sepsis-induced ARDS cases and 234 at-risk controls. Three candidate genes were prioritized: dynein cytoplasmic-2 heavy chain-1, fms-related tyrosine kinase 1 (FLT1), and integrin alpha-1. Of those, a SNP from FLT1 gene (rs9513106) was associated with ARDS in the discovery study, with an odds ratio (OR) for the C allele of 0.76, 95% confidence interval (CI) 0.58–0.98 (p = 0.037). This result was replicated in an independent study (OR = 0.78, 95% CI = 0.62–0.98, p = 0.039), showing consistent direction of effects in a meta-analysis (OR = 0.77, 95% CI = 0.65–0.92, p = 0.003). We identified FLT1 as a novel ARDS susceptibility gene and demonstrated that integration of genomic data can be a valid procedure to identify novel susceptibility genes. These results contribute to previous firm associations and functional evidences implicating FLT1 gene in other complex traits that are mechanistically linked, through the key role of endothelium, to the pathophysiology of ARDS.

Background: The ability to predict a long duration of mechanical ventilation (MV) by clinicians is very limited. We assessed the value of machine learning (ML) for early prediction of the duration of MV > 14 days in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Methods: This is a development, testing, and external validation study using data from 1173 patients on MV ≥ 3 days with moderate-to-severe ARDS. We first developed and tested prediction models in 920 ARDS patients using relevant features captured at the time of moderate/severe ARDS diagnosis, at 24 h and 72 h after diagnosis with logistic regression, and Multilayer Perceptron, Support Vector Machine, and Random Forest ML techniques. For external validation, we used an independent cohort of 253 patients on MV ≥ 3 days with moderate/severe ARDS. Results: A total of 441 patients (48%) from the derivation cohort (n = 920) and 100 patients (40%) from the validation cohort (n = 253) were mechanically ventilated for >14 days [median 14 days (IQR 8–25) vs. 13 days (IQR 7–21), respectively]. The best early prediction model was obtained with data collected at 72 h after moderate/severe ARDS diagnosis. Multilayer Perceptron risk modeling identified major prognostic factors for the duration of MV > 14 days, including PaO₂/FiO₂, PaCO₂, pH, and positive end-expiratory pressure. Predictions of the duration of MV > 14 days showed modest discrimination [AUC 0.71 (95%CI 0.65–0.76)]. Conclusions: Prolonged MV duration in moderate/severe ARDS patients remains difficult to predict early even with ML techniques such as Multilayer Perceptron and using data at 72 h of diagnosis. More research is needed to identify markers for predicting the length of MV. This study was registered on 14 August 2023 at ClinicalTrials.gov (NCT NCT05993377).

Background: Early prediction of ICU death in acute hypoxemic respiratory failure (AHRF) could inform clinicians for targeting therapies to reduce harm and increase survival. We sought to determine clinical modifiable and non-modifiable features during the first 24 h of AHRF associated with ICU death. Methods: This is a development, testing, and validation study using data from a prospective, multicenter, nation-based, observational cohort of 1241 patients with AHRF (defined as PaO₂/FiO₂ ≤ 300 mmHg on mechanical ventilation [MV] with positive end-expiratory pressure [PEEP] ≥ 5 cmH₂O and FiO₂ ≥ 0.3) from any etiology. Using relevant features captured at AHRF diagnosis and within 24 h, we developed a logistic regression model following variable selection by genetic algorithm and machine learning (ML) approaches. Results: We analyzed 1193 patients, after excluding 48 patients with no data at 24 h after AHRF diagnosis. Using repeated random sampling, we selected 75% (n = 900) for model development and testing, and 25% (n = 293) for final validation. Risk modeling identified six major predictors of ICU death, including patient’s age, and values at 24 h of PEEP, FiO₂, plateau pressure, tidal volume, and number of extrapulmonary organ failures. Performance with ML methods was similar to logistic regression and achieved a high area under the receiver operating characteristic curve (AUROC) of 0.88, 95%CI 0.86–0.90. Validation confirmed adequate model performance (AUROC 0.83, 95%CI 0.78–0.88). Conclusions: ML and traditional methods led to an encouraging model to predict ICU death in ventilated AHRF as early as 24 h after diagnosis. More research is needed to identify modifiable factors to prevent ICU deaths.