Clinical Phenotyping for Prognosis and Immunotherapy Guidance in Bacterial Sepsis and COVID-19.

OBJECTIVES: It is suggested that sepsis may be classified into four clinical phenotypes, using an algorithm employing 29 admission parameters. We applied a simplified phenotyping algorithm among patients with bacterial sepsis and severe COVID-19 and assessed characteristics and outcomes of the derived phenotypes. DESIGN: Retrospective analysis of data from prospective clinical studies. SETTING: Greek ICUs and Internal Medicine departments. PATIENTS AND INTERVENTIONS: We analyzed 1498 patients, 620 with bacterial sepsis and 878 with severe COVID-19. We implemented a six-parameter algorithm (creatinine, lactate, aspartate transaminase, bilirubin, C-reactive protein, and international normalized ratio) to classify patients with bacterial sepsis intro previously defined phenotypes. Patients with severe COVID-19, included in two open-label immunotherapy trials were subsequently classified. Heterogeneity of treatment effect of anakinra was assessed. The primary outcome was 28-day mortality. MEASUREMENTS AND MAIN RESULTS: The algorithm validated the presence of the four phenotypes across the cohort of bacterial sepsis and the individual studies included in this cohort. Phenotype α represented younger patients with low risk of death, ß was associated with high comorbidity burden, and δ with the highest mortality. Phenotype assignment was independently associated with outcome, even after adjustment for Charlson Comorbidity Index. Phenotype distribution and outcomes in severe COVID-19 followed a similar pattern. CONCLUSIONS: A simplified algorithm successfully identified previously derived phenotypes of bacterial sepsis, which were predictive of outcome. This classification may apply to patients with severe COVID-19 with prognostic implications.

Clinical Phenotyping for Prognosis and Immunotherapy Guidance in Bacterial Sepsis and COVID-19.

OBJECTIVES: It is suggested that sepsis may be classified into four clinical phenotypes, using an algorithm employing 29 admission parameters. We applied a simplified phenotyping algorithm among patients with bacterial sepsis and severe COVID-19 and assessed characteristics and outcomes of the derived phenotypes. DESIGN: Retrospective analysis of data from prospective clinical studies. SETTING: Greek ICUs and Internal Medicine departments. PATIENTS AND INTERVENTIONS: We analyzed 1498 patients, 620 with bacterial sepsis and 878 with severe COVID-19. We implemented a six-parameter algorithm (creatinine, lactate, aspartate transaminase, bilirubin, C-reactive protein, and international normalized ratio) to classify patients with bacterial sepsis intro previously defined phenotypes. Patients with severe COVID-19, included in two open-label immunotherapy trials were subsequently classified. Heterogeneity of treatment effect of anakinra was assessed. The primary outcome was 28-day mortality. MEASUREMENTS AND MAIN RESULTS: The algorithm validated the presence of the four phenotypes across the cohort of bacterial sepsis and the individual studies included in this cohort. Phenotype α represented younger patients with low risk of death, ß was associated with high comorbidity burden, and δ with the highest mortality. Phenotype assignment was independently associated with outcome, even after adjustment for Charlson Comorbidity Index. Phenotype distribution and outcomes in severe COVID-19 followed a similar pattern. CONCLUSIONS: A simplified algorithm successfully identified previously derived phenotypes of bacterial sepsis, which were predictive of outcome. This classification may apply to patients with severe COVID-19 with prognostic implications.

ESCAPE: An Open-Label Trial of Personalized Immunotherapy in Critically lll COVID-19 Patients.

BACKGROUND: Macrophage activation-like syndrome (MALS) and complex immune dysregulation (CID) often underlie acute respiratory distress (ARDS) in COVID-19. We aimed to investigate the effect of personalized immunotherapy on clinical improvement of critical COVID-19. METHODS: In this open-label prospective trial, 102 patients with ARDS by SARS-CoV-2 were screened for MALS (ferritin >4,420 ng/mL) and CID (ferritin ≤4,420 ng/mL and low human leukocyte antigen (HLA)-DR expression on CD14-monocytes). Patients with MALS or CID with increased aminotransferases received intravenous anakinra; those with CID and normal aminotransferases received tocilizumab. The primary outcome was ≥25% decrease in the Sequential Organ Failure Assessment (SOFA) score and/or 50% increase in the respiratory ratio by day 8; 28-day mortality, change of SOFA score by day 28, serum biomarkers, and cytokine production by mononuclear cells were secondary endpoints. RESULTS: The primary study endpoint was met in 58.3% of anakinra-treated patients and in 33.3% of tocilizumab-treated patients (p: 0.01). Most patients in both groups received dexamethasone as standard of care. No differences were found in secondary outcomes, mortality, and SOFA score changes. Ferritin decreased among anakinra-treated patients; interleukin-6, soluble urokinase plasminogen activator receptor, and HLA-DR expression increased among tocilizumab-treated patients. Survivors by day 28 who received anakinra were distributed to lower severity levels of the WHO clinical progression scale. Greater incidence of secondary infections was found with tocilizumab treatment. CONCLUSION: Immune assessment resulted in favorable anakinra responses among critically ill patients with COVID-19 and features of MALS.

A four-probiotic preparation for ventilator-associated pneumonia in multi-trauma patients: results of a randomized clinical trial.

The role of probiotics in the prevention of ventilator-associated pneumonia (VAP) remains inconclusive. The aim of this study was to assess the efficacy of a probiotic regimen for VAP prophylaxis in mechanically ventilated multi-trauma patients, intubated immediately after the injurious insult. In a randomized, placebo-controlled study enrolling multi-trauma patients, patients expected to require mechanical ventilation for >10 days were assigned at random to receive prophylaxis with a probiotic formula (n=59) or placebo (n=53). The probiotic formula was a preparation of Lactobacillus acidophilus LA-5 [1.75 × 109 colony-forming units (cfu)], Lactobacillus plantarum (0.5 × 109 cfu), Bifidobacterium lactis BB-12 (1.75 × 109 cfu) and Saccharomyces boulardii (1.5 × 109 cfu) in sachets. Each patient received two sachets twice daily for 15 days: one through the nasogastric tube and one spread on the oropharynx. The incidence of VAP was the primary endpoint. The incidence of other infections and sepsis, and the duration of hospital stay were the secondary endpoints. Administration of probiotics reduced the incidence of VAP [11.9% vs 28.3%, hazard ratio (HR) 0.34, 95% confidence interval (CI) 0.13-0.92; P=0.034] and sepsis [6.8% vs 24.5%, odds ratio 0.22, 95% CI 0.07-0.74: P=0.016]. Furthermore, probiotic prophylaxis reduced the time of stay in the intensive care unit (ICU) and the length of hospital stay. The prophylactic use of probiotics with a combination of enteral and topical application to the oropharynx had a positive effect on the incidence of VAP and sepsis, as well as on ICU and total hospital stay in patients receiving protracted mechanical ventilation.

Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections.

The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p < 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2.