ABSTRACT
Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.
Subject(s)
COVID-19 , Blood Coagulation Disorders, InheritedABSTRACT
A key element to the prevention and management of the COVID-19 pandemic is the development of effective therapeutics. Drug combination strategies of repurposed drugs offer a number of advantages to monotherapies including the potential to achieve greater efficacy, the potential to increase the therapeutic index of drugs and the potential to reduce the emergence of drug resistance. Combination of agents with antiviral mechanisms of action with immune-modulatory or anti-inflammatory drug is also worthy of investigation. Here, we report on the in vitro synergistic interaction between two FDA approved drugs, remdesivir (RDV) and ivermectin (IVM) resulting in enhanced antiviral activity against SARS-CoV-2, the causative pathogen of COVID-19. These findings warrant further investigations into the clinical potential of this combination, together with studies to define the underlying mechanism.
Subject(s)
COVID-19ABSTRACT
Background Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing demand to identify predictors of severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors. We sought to evaluate this hypothesis by conducting an international multicenter study using HLA sequencing with subsequent independent validation. Methods We analyzed a total of 332 samples. First, we enrolled 233 patients in Germany, Spain, and Switzerland for HLA and whole exome sequencing. Furthermore, we validated our results in a public data set (United States, n=99). Patients older than 18 years presenting with COVID-19 were included, representing the full spectrum of the disease. HLA candidate alleles were identified in the derivation cohort (n=92) and tested in two independent validation cohorts (n=240). Results We identified HLA-C* 04:01 as a novel genetic predictor for severe clinical course in COVID-19. Carriers of HLA-C* 04:01 had twice the risk of intubation when infected with SARS-CoV-2 (hazard ratio 2.1, adjusted p-value=0.0036). Importantly, these findings were successfully replicated in an independent data set. Furthermore, our findings are biologically plausible, as HLA-C* 04:01 has fewer predicted bindings sites with relevant SARS-CoV-2 peptides as compared to other HLA alleles. Exome sequencing confirmed findings from HLA analysis. Conclusions HLA-C* 04:01 carriage is associated with a twofold increased risk of intubation in patients infected with SARS-CoV-2. Testing for HLA-C* 04:01 could have clinical implications to identify high-risk patients and individualize management.
Subject(s)
COVID-19 , Coronavirus InfectionsABSTRACT
BackgroundAdequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. MethodsA cohort of 168 hospitalized adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care center was analyzed. ResultsForty-four percent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95%CI 1.10-1.37, p<0.01), age 60-69 as compared to 18-59 years (aOR 4.33, 95%CI 1.07-20.10, p=0.04), and history of hypertension (aOR 5.55, 95%CI 2.00-16.82, p<0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p<0.01). Median duration of hospitalization was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV-patients. ConclusionOur results indicate a short duration of symptoms before admission as a risk factor for severe disease and different viral load kinetics in severely affected patients.