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

RationaleMacrophage activation syndrome (MAS) and complex immune dysregulation (CID) often underlie acute respiratory distress (ARDS) in COVID-19. ObjectiveTo investigate the outcome of personalized immunotherapy in critical COVID-19. MethodsIn this open-label prospective trial, 102 patients with SOFA (sequential organ failure assessment) score [≥]2 or ARDS by SARS-CoV-2 were screened for MAS (ferritin more than 4420 ng/ml) and CID (ferritin [≤]4420 ng/ml and low expression of HLA-DR on CD14-monocytes). Patients with MAS and CID with increased aminotransferases were assigned to intravenous anakinra; those with CID and normal aminotransferases to tocilizumab. The primary outcome was at least 25% decrease of SOFA score and/or 50% increase of 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. Measurements and Main ResultsThe primary study endpoint was met in 58.3% of anakinra-treated patients and in 33.3% of tocilizumab-treated patients (odds ratio 3.11; 95% CIs 1.29-7.73; P: 0.011). No differences were found in mortality and in SOFA score changes. By day 4, ferritin was decreased among anakinra-treated patients; interleukin (IL)-6, soluble urokinase plasminogen activator receptor (suPAR) and the expression of HLA-DR were increased among tocilizumab-treated patients. Anakinra increased capacity of mononuclear cells to produce IL-6. Survivors by day 28 who received anakinra were distributed to scales of the WHO clinical progression of lower severity. Greater incidence of secondary infections was found with tocilizumab treatment. ConclusionsBiomarkers may guide favourable anakinra responses in critically ill patients with COVID-19. Trial RegistrationClinicalTrials.gov, NCT04339712

Anakinra To Prevent Respiratory Failure In COVID-19

IntroductionThe management of pneumonia caused by SARS-CoV-2 should rely on early recognition of the risk for progression to severe respiratory failure (SRF) and its prevention. We investigated if early suPAR (soluble urokinase plasminogen activator receptor)-guided anakinra treatment could prevent COVID-19-assocated SRF. MethodsIn this open-label prospective trial, 130 patients admitted with SARS-CoV-2 pneumonia SARS-CoV-2 and suPAR levels [≥]6 g/l were assigned to subcutaneous anakinra 100mg once daily for 10 days. The primary outcome was the incidence of SRF at day 14. Secondary outcomes were 30-day mortality, changes in sequential organ failure assessment (SOFA) score, of cytokine-stimulation pattern and of circulating inflammatory mediators. Equal number of propensity score-matched comparators for comorbidities, severity on admission and standard-of care (SOC) were studied. ResultsThe incidence of SRF was 22.3% (95% CI, 16.0-30.2%) among anakinra-treated patients and 59.2% (95% CI, 50.6-67.3%; P: 4.6 x 10-8) among SOC comparators (hazard ratio, 0.30; 95%CI, 0.20-0.46). 30-day mortality was 11.5% (95% CI, 7.1-18.2%) and 22.3% (95% CI, 16.0-30.2%) respectively (hazard ratio 0.49; 95% CI 0.25-0.97%; P: 0.041). Anakinra treatment was associated with decrease in SOFA score and in circulating interleukin (IL)-6, sCD163 and sIL2-R; the serum IL-10/IL-6 ratio on day 7 was inversely associated with the change in SOFA score. Duration of stay at the intensive care unit and at hospital was shortened compared to the SOC group; the cost of hospitalization was decreased. ConclusionsEarly suPAR-guided anakinra treatment is associated with decrease of the risk for SRF and restoration of the pro- /anti-inflammatory balance. Trial RegistrationClinicalTrials.gov, NCT04357366

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases calls for a better characterization and understanding of the changes in the immune system. Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 11 COVID-19 patients. Comparison of COVID-19 blood transcriptomes with those of a collection of over 2,800 samples derived from 11 different viral infections, inflammatory diseases and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.