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.

Applicability of an unsupervised cluster model developed on first wave COVID-19 patients in second/third wave critically ill patients.

OBJECTIVE: To validate the unsupervised cluster model (USCM) developed during the first pandemic wave in a cohort of critically ill patients from the second and third pandemic waves. DESIGN: Observational, retrospective, multicentre study. SETTING: Intensive Care Unit (ICU). PATIENTS: Adult patients admitted with COVID-19 and respiratory failure during the second and third pandemic waves. INTERVENTIONS: None. MAIN VARIABLES OF INTEREST: Collected data included demographic and clinical characteristics, comorbidities, laboratory tests and ICU outcomes. To validate our original USCM, we assigned a phenotype to each patient of the validation cohort. The performance of the classification was determined by Silhouette coefficient (SC) and general linear modelling. In a post-hoc analysis we developed and validated a USCM specific to the validation set. The model's performance was measured using accuracy test and area under curve (AUC) ROC. RESULTS: A total of 2330 patients (mean age 63 [53-82] years, 1643 (70.5%) male, median APACHE II score (12 [9-16]) and SOFA score (4 [3-6]) were included. The ICU mortality was 27.2%. The USCM classified patients into 3 clinical phenotypes: A (n = 1206 patients, 51.8%); B (n = 618 patients, 26.5%), and C (n = 506 patients, 21.7%). The characteristics of patients within each phenotype were significantly different from the original population. The SC was -0.007 and the inclusion of phenotype classification in a regression model did not improve the model performance (0.79 and 0.78 ROC for original and validation model). The post-hoc model performed better than the validation model (SC -0.08). CONCLUSION: Models developed using machine learning techniques during the first pandemic wave cannot be applied with adequate performance to patients admitted in subsequent waves without prior validation.

A Multicentric Observational Study to Determine Myocardial Injury in Severe Community-Acquired Pneumonia (sCAP).

Background: Severe community-acquired pneumonia (sCAP) is the most frequent admission for acute respiratory failure in intensive care medicine. Observational studies have found a correlation between patients who were admitted with CAP and the development of cardiovascular events. The risk of acute myocardial damage in patients with CAP is particularly high within the first 30 days of hospitalization. Research design and methods: Multicenter prospective cohort analysis conducted in consecutive patients admitted to an ICU with microbiologically confirmed diagnoses of sCAP. The aim was to determine any structural cardiac damage detected by advanced imagining techniques (cardiac MRI) and cardiac biomarkers in patients with sCAP. The patients were stratified, according to their etiology, into pneumococcal or not-pneumococcal sCAP. The primary outcome was cardiac damage at day 5 and 7 of clinical presentation. Results: A total of 23 patients were consecutively and prospectively enrolled for two winter periods. No significant differences were observed between the median troponin when comparing the pneumococcal vs. non-pneumococcal. The incidence of myocardial damage was numerically higher in the pneumococcal subgroup (70% vs. 50%, p = 0.61) on day 5 and on day 7 (53% vs. 40%, p = 0.81) but did not achieve significance. Confirming a correlation between the biomarkers of cell damage and the biomarkers of myocardial damage, only a positive and significant correlation was observed between h-FABP and DNA on day 1 (r = 0.74; p < 0.01) and day 3 (r = 0.83; p < 0.010). Twenty cardiac MRIs were performed on the 23 patients (87%). No presence of fibrosis was observed in any of the studies carried out within the first 15 days of admission. Conclusions: No significant myocardial damage was found in patients with sCAP independent of the bacterial etiology in accordance with biomarker alterations (Troponin and/or h-FABP) or cardiac MRI. Using cardiac MRI, we could not find any presence of myocardial fibrosis within the first 15 days of admission.