Endothelial dysfunction and persistent inflammation in severe post-COVID-19 patients: implications for gas exchange.

BACKGROUND: Understanding the enduring respiratory consequences of severe COVID-19 is crucial for comprehensive patient care. This study aims to evaluate the impact of post-COVID conditions on respiratory sequelae of severe acute respiratory distress syndrome (ARDS). METHODS: We examined 88 survivors of COVID-19-associated severe ARDS six months post-intensive care unit (ICU) discharge. Assessments included clinical and functional evaluation as well as plasma biomarkers of endothelial dysfunction, inflammation, and viral response. Additionally, an in vitro model using human umbilical vein endothelial cells (HUVECs) explored the direct impact of post-COVID plasma on endothelial function. RESULTS: Post-COVID patients with impaired gas exchange demonstrated persistent endothelial inflammation marked by elevated ICAM-1, IL-8, CCL-2, and ET-1 plasma levels. Concurrently, systemic inflammation, evidenced by NLRP3 overexpression and elevated levels of IL-6, sCD40-L, and C-reactive protein, was associated with endothelial dysfunction biomarkers and increased in post-COVID patients with impaired gas exchange. T-cell activation, reflected in CD69 expression, and persistently elevated levels of interferon-ß (IFN-ß) further contributed to sustained inflammation. The in vitro model confirmed that patient plasma, with altered levels of sCD40-L and IFN-ß proteins, has the capacity to alter endothelial function. CONCLUSIONS: Six months post-ICU discharge, survivors of COVID-19-associated ARDS exhibited sustained elevation in endothelial dysfunction biomarkers, correlating with the severity of impaired gas exchange. NLRP3 inflammasome activity and persistent T-cell activation indicate on going inflammation contributing to persistent endothelial dysfunction, potentially intensified by sustained viral immune response.

Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort.

BACKGROUND: Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster. METHODS: Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3. RESULTS: Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3. CONCLUSIONS: During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis.

Predicting the Length of Mechanical Ventilation in Acute Respiratory Disease Syndrome Using Machine Learning: The PIONEER Study.

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).