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Among people infected with SARS-CoV-2, the determination of clinical features associated with poor outcome is essential to identify those at high risk of deterioration. Here, we aimed to investigate clinical phenotypes of patients hospitalized due to COVID-19 and to examine the predictive value of the neutrophil-to-lymphocyte ratio (NLR) in a representative patient collective of the Swiss population. We conducted a retrospective monocentriccohort study with patients hospitalized due to COVID-19 between 27 February and 31 December 2020. Data were analyzed descriptively, using the binary logistic regression model, proportional odds logistic regression model, competing risk analysis, and summary measure analysis. A total of 454 patients were included in our study. Dyspnea, elevated respiratory rate, low oxygen saturation at baseline, age, and presence of multiple comorbidities were associated with a more severe course of the disease. A high NLR at baseline was significantly associated with disease severity, unfavorable outcome, and mortality. In non-survivors, NLR further increased during hospital stay, whereas in survivors, NLR decreased. In conclusion, our data emphasize the importance of accurate history taking and clinical examination upon admission and confirm the role of baseline NLR as a surrogate marker for increased disease severity, unfavorable outcome, and mortality in patients hospitalized due to infection with SARS-CoV-2.

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has caused a global burden for health care systems due to high morbidity and mortality rates, leading to caseloads that episodically surpass hospital resources. Due to different disease manifestations, the triage of patients at high risk for a poor outcome continues to be a major challenge for clinicians. The AIFELL score was developed as a simple decision instrument for emergency rooms to distinguish COVID-19 patients in severe disease stages from less severe COVID-19 and non-COVID-19 cases. In the present study, we aimed to evaluate the AIFELL score as a prediction tool for clinical deterioration and disease severity in hospitalized COVID-19 patients. During the second wave of the COVID-19 pandemic in Switzerland, we analyzed consecutively hospitalized patients at the Triemli Hospital Zurich from the end of November 2020 until mid-February 2021. Statistical analyses were performed for group comparisons and to evaluate significance. AIFELL scores of patients developing severe COVID-19 stages IIb and III during hospitalization were significantly higher upon admission compared to those patients not surpassing stages I and IIa. Group comparisons indicated significantly different AIFELL scores between each stage. In conclusion, the AIFELL score at admission was useful to predict the disease severity and progression in hospitalized COVID-19 patients.

BACKGROUND: T-cell lymphopenia and functional impairment is a hallmark of severe acute coronavirus disease 2019 (COVID-19). How T-cell numbers and function evolve at later timepoints after clinical recovery remains poorly investigated. METHODS: We prospectively enrolled and longitudinally sampled 173 individuals with asymptomatic to critical COVID-19 and analyzed phenotypic and functional characteristics of T cells using flow cytometry, 40-parameter mass cytometry, targeted proteomics, and functional assays. RESULTS: The extensive T-cell lymphopenia observed particularly in patients with severe COVID-19 during acute infection had recovered 6 months after infection, which was accompanied by a normalization of functional T-cell responses to common viral antigens. We detected persisting CD4$^{+}$ and CD8$^{+}$ T-cell activation up to 12 months after infection, in patients with mild and severe COVID-19, as measured by increased HLA-DR and CD38 expression on these cells. Persistent T-cell activation after COVID-19 was independent of administration of a COVID-19 vaccine post-infection. Furthermore, we identified a subgroup of patients with severe COVID-19 that presented with persistently low CD8$^{+}$ T-cell counts at follow-up and exhibited a distinct phenotype during acute infection consisting of a dysfunctional T-cell response and signs of excessive pro-inflammatory cytokine production. CONCLUSION: Our study suggests that T-cell numbers and function recover in most patients after COVID-19. However, we find evidence of persistent T-cell activation up to 12 months after infection and describe a subgroup of severe COVID-19 patients with persistently low CD8$^{+}$ T-cell counts exhibiting a dysregulated immune response during acute infection.

Background Several autoimmune features occur during coronavirus disease 2019 (COVID-19), with possible implications for disease course, immunity, and autoimmune pathology. In this study, we longitudinally screened for clinically relevant systemic autoantibodies to assess their prevalence, temporal trajectory, and association with immunity, comorbidities, and severity of COVID-19. Methods We performed highly sensitive indirect immunofluorescence assays to detect antinuclear antibodies (ANA) and antineutrophil cytoplasmic antibodies (ANCA), along with serum proteomics and virome-wide serological profiling in a multicentric cohort of 175 COVID-19 patients followed up to 1 year after infection, eleven vaccinated individuals, and 41 unexposed controls. Results Compared with healthy controls, similar prevalence and patterns of ANA were present in patients during acute COVID-19 and recovery. However, the paired analysis revealed a subgroup of patients with transient presence of certain ANA patterns during acute COVID-19. Furthermore, patients with severe COVID-19 exhibited a high prevalence of ANCA during acute disease. These autoantibodies were quantitatively associated with higher SARS-CoV-2-specific antibody titers in COVID-19 patients and in vaccinated individuals, thus linking autoantibody production to increased antigen-specific humoral responses. Notably, the qualitative breadth of antibodies cross-reactive with other coronaviruses was comparable in ANA-positive and ANA-negative individuals during acute COVID-19. In autoantibody-positive patients, multiparametric characterization demonstrated an inflammatory signature during acute COVID-19 and alterations of the B-cell compartment after recovery. Conclusion Highly sensitive indirect immunofluorescence assays revealed transient autoantibody production during acute SARS-CoV-2 infection, while the presence of autoantibodies in COVID-19 patients correlated with increased antiviral humoral immune responses and inflammato

Following acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) a significant proportion of individuals develop prolonged symptoms, a serious condition termed post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) or long COVID. Predictors of PACS are needed. In a prospective multicentric cohort study of 215 individuals, we study COVID-19 patients during primary infection and up to one year later, compared to healthy subjects. We discover an immunoglobulin (Ig) signature, based on total IgM and IgG3 levels, which – combined with age, history of asthma bronchiale, and five symptoms during primary infection – is able to predict the risk of PACS independently of timepoint of blood sampling. We validate the score in an independent cohort of 395 individuals with COVID-19. Our results highlight the benefit of measuring Igs for the early identification of patients at high risk for PACS, which facilitates the study of targeted treatment and pathomechanisms of PACS.