Cytokine levels associated with favorable clinical outcome in the CAPSID randomized trial of convalescent plasma in patients with severe COVID-19.

Objectives: To determine the profile of cytokines in patients with severe COVID-19 who were enrolled in a trial of COVID-19 convalescent plasma (CCP). Methods: Patients were randomized to receive standard treatment and 3 CCP units or standard treatment alone (CAPSID trial, ClinicalTrials.gov NCT04433910). The primary outcome was a dichotomous composite outcome (survival and no longer severe COVID-19 on day 21). Time to clinical improvement was a key secondary endpoint. The concentrations of 27 cytokines were measured (baseline, day 7). We analyzed the change and the correlation between serum cytokine levels over time in different subgroups and the prediction of outcome in receiver operating characteristics (ROC) analyses and in multivariate models. Results: The majority of cytokines showed significant changes from baseline to day 7. Some were strongly correlated amongst each other (at baseline the cluster IL-1ß, IL-2, IL-6, IL-8, G-CSF, MIP-1α, the cluster PDGF-BB, RANTES or the cluster IL-4, IL-17, Eotaxin, bFGF, TNF-α). The correlation matrix substantially changed from baseline to day 7. The heatmaps of the absolute values of the correlation matrix indicated an association of CCP treatment and clinical outcome with the cytokine pattern. Low levels of IP-10, IFN-γ, MCP-1 and IL-1ß on day 0 were predictive of treatment success in a ROC analysis. In multivariate models, low levels of IL-1ß, IFN-γ and MCP-1 on day 0 were significantly associated with both treatment success and shorter time to clinical improvement. Low levels of IP-10, IL-1RA, IL-6, MCP-1 and IFN-γ on day 7 and high levels of IL-9, PDGF and RANTES on day 7 were predictive of treatment success in ROC analyses. Low levels of IP-10, MCP-1 and high levels of RANTES, on day 7 were associated with both treatment success and shorter time to clinical improvement in multivariate models. Conclusion: This analysis demonstrates a considerable dynamic of cytokines over time, which is influenced by both treatment and clinical course of COVID-19. Levels of IL-1ß and MCP-1 at baseline and MCP-1, IP-10 and RANTES on day 7 were associated with a favorable outcome across several endpoints. These cytokines should be included in future trials for further evaluation as predictive factors.

Results of the CAPSID randomized trial for high-dose convalescent plasma in patients with severe COVID-19.

BACKGROUNDCOVID-19 convalescent plasma (CCP) has been considered a treatment option for COVID-19. This trial assessed the efficacy of a neutralizing antibody containing high-dose CCP in hospitalized adults with COVID-19 requiring respiratory support or intensive care treatment.METHODSPatients (n = 105) were randomized 1:1 to either receive standard treatment and 3 units of CCP or standard treatment alone. Control group patients with progress on day 14 could cross over to the CCP group. The primary outcome was a dichotomous composite outcome of survival and no longer fulfilling criteria for severe COVID-19 on day 21.ResultsThe primary outcome occurred in 43.4% of patients in the CCP group and 32.7% in the control group (P = 0.32). The median time to clinical improvement was 26 days in the CCP group and 66 days in the control group (P = 0.27). The median time to discharge from the hospital was 31 days in the CCP group and 51 days in the control group (P = 0.24). In the subgroup that received a higher cumulative amount of neutralizing antibodies, the primary outcome occurred in 56.0% of the patients (vs. 32.1%), with significantly shorter intervals to clinical improvement (20 vs. 66 days, P < 0.05) and to hospital discharge (21 vs. 51 days, P = 0.03) and better survival (day-60 probability of survival 91.6% vs. 68.1%, P = 0.02) in comparison with the control group.ConclusionCCP added to standard treatment was not associated with a significant improvement in the primary and secondary outcomes. A predefined subgroup analysis showed a significant benefit of CCP among patients who received a larger amount of neutralizing antibodies.Trial registrationClinicalTrials.gov NCT04433910.FundingBundesministerium für Gesundheit (German Federal Ministry of Health): ZMVI1-2520COR802.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.

Surgical tracheostomy in a cohort of COVID-19 patients.

BACKGROUND: One of the main symptoms of severe infection with the new coronavirus­2 (SARS-CoV-2) is hypoxemic respiratory failure because of viral pneumonia with the need for mechanical ventilation. Prolonged mechanical ventilation may require a tracheostomy, but the increased risk for contamination is a matter of considerable debate. OBJECTIVE: Evaluation of safety and effects of surgical tracheostomy on ventilation parameters and outcome in patients with COVID-19. STUDY DESIGN: Retrospective observational study between March 27 and May 18, 2020, in a single-center coronavirus disease-designated ICU at a tertiary care German hospital. PATIENTS: Patients with COVID-19 were treated with open surgical tracheostomy due to severe hypoxemic respiratory failure requiring mechanical ventilation. MEASUREMENTS: Clinical and ventilation data were obtained from medical records in a retrospective manner. RESULTS: A total of 18 patients with confirmed SARS-CoV­2 infection and surgical tracheostomy were analyzed. The age range was 42-87 years. All patients received open tracheostomy between 2-16 days after admission. Ventilation after tracheostomy was less invasive (reduction in PEAK and positive end-expiratory pressure [PEEP]) and lung compliance increased over time after tracheostomy. Also, sedative drugs could be reduced, and patients had a reduced need of norepinephrine to maintain hemodynamic stability. Six of 18 patients died. All surgical staff were equipped with N99-masks and facial shields or with powered air-purifying respirators (PAPR). CONCLUSION: Our data suggest that open surgical tracheostomy can be performed without severe complications in patients with COVID-19. Tracheostomy may reduce invasiveness of mechanical ventilation and the need for sedative drugs and norepinehprine. Recommendations for personal protective equipment (PPE) for surgical staff should be followed when PPE is available to avoid contamination of the personnel.