Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine.

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

A 6-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19

BackgroundDetermining the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. MethodsWe developed the classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N=705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. ResultsWe selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N=97) and retrospectively (N=100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. ConclusionsWith further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

Absent or insufficient anti-SARS-CoV-2 S antibodies at ICU admission are associated to higher viral loads in plasma, antigenemia and mortality in COVID-19 patients

Purposeto evaluate the association between anti-SARS-CoV-2 S IgM and IgG antibodies with viral RNA load in plasma, the frequency of antigenemia and with the risk of mortality in critically ill patients with COVID-19. Methodsanti-SARS-CoV-2 S antibodies levels, viral RNA load and antigenemia were profiled in plasma of 92 adult patients in the first 24 hours following ICU admission. The impact of these variables on 30-day mortality was assessed by using Kaplan-Meier curves and multivariate Cox regression analysis. Resultsnon survivors showed more frequently absence of anti-SARS-CoV-2 S IgG and IgM antibodies than survivors (26.3% vs 5.6% for IgM and 18.4% vs 5.6% for IgG), and a higher frequency of antigenemia (47.4% vs 22.2%) (p <0.05). Non survivors showed lower concentrations of anti-S IgG and IgM and higher viral RNA loads in plasma, which were associated to increased 30-day mortality and decreased survival mean time. [Adjusted HR (CI95%), p]: [S IgM (AUC [≥]60): 0.48 (0.24; 0.97), 0.040]; [S IgG (AUC [≥]237): 0.47 (0.23; 0.97), 0.042]; [Antigenemia (+): 2.45 (1.27; 4.71), 0.007]; [N1 viral load ([≥] 2.156 copies/mL): 2.21 (1.11; 4.39),0.024]; [N2 viral load ([≥] 3.035 copies/mL): 2.32 (1.16; 4.63), 0.017]. Frequency of antigenemia was >2.5-fold higher in patients with absence of antibodies. Levels of anti-SARS-CoV-2 S antibodies correlated inversely with viral RNA load. Conclusionabsence / insufficient levels of anti-SARS-CoV-2 S antibodies following ICU admission is associated to poor viral control, evidenced by increased viral RNA loads in plasma, higher frequency of antigenemia, and also to increased 30-day mortality. Take-home messageabsent or low levels of antibodies against the S protein of SARS-CoV- 2 at ICU admission is associated to an increased risk of mortality, higher frequency of antigenemia and higher viral RNA loads in plasma. Profiling anti-SARS-CoV-2 s antibodies at ICU admission could help to predict outcome and to better identify those patients potentially deserving replacement treatment with monoclonal or polyclonal antibodies.

SARS-CoV-2-RNA viremia is associated to hypercytokinemia and critical illness in COVID-19

BackgroundSevere COVID-19 is characterized by clinical and biological manifestations typically observed in sepsis. SARS-CoV-2 RNA is commonly detected in nasopharyngeal swabs, however viral RNA can be found also in peripheral blood and other tissues. Whether systemic spreading of the virus or viral components plays a role in the pathogenesis of the sepsis-like disease observed in severe COVID-19 is currently unknown. MethodsWe determined the association of plasma SARS-CoV-2 RNA with the biological responses and the clinical severity of patients with COVID-19. 250 patients with confirmed COVID-19 infection were recruited (50 outpatients, 100 hospitalised ward patients, and 100 critically ill). The association between plasma SARS-CoV-2 RNA and laboratory parameters was evaluated using multivariate GLM with a gamma distribution. The association between plasma SARS-CoV-2 RNA and severity was evaluated using multivariate ordinal logistic regression analysis and Generalized Linear Model (GLM) analysis with a binomial distribution. ResultsThe presence of SARS-CoV-2-RNA viremia was independently associated with a number of features consistently identified in sepsis: 1) high levels of cytokines (including CXCL10, CCL-2, IL-10, IL-1ra, IL-15, and G-CSF); 2) higher levels of ferritin and LDH; 3) low lymphocyte and monocyte counts 4) and low platelet counts. In hospitalised patients, the presence of SARS-CoV-2-RNA viremia was independently associated with critical illness: (adjusted OR= 8.30 [CI95%=4.21 - 16.34], p < 0.001). CXCL10 was the most accurate identifier of SARS-CoV-2-RNA viremia in plasma (area under the curve (AUC), [CI95%], p) = 0.85 [0.80 - 0.89), <0.001]), suggesting its potential role as a surrogate biomarker of viremia. The cytokine IL-15 most accurately differentiated clinical ward patients from ICU patients (AUC: 0.82 [0.76 - 0.88], <0.001). Conclusionssystemic dissemination of genomic material of SARS-CoV-2 is associated with a sepsis-like biological response and critical illness in patients with COVID-19. RNA viremia could represent an important link between SARS-CoV-2 infection, host response dysfunction and the transition from moderate illness to severe, sepsis-like COVID-19 disease.