ABSTRACT
Background: The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood. Methods: A longitudinal prospective cohort of hospitalised patients with COVID-19 (n=254) was followed up to 35 days after admission (median, 8 days). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T-, B- and natural killer lymphocyte subsets and serum interleukin-6 (IL-6) response. We used machine learning to identify patterns of the immune response and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors. Results: Overall, 45 (18%) patients died within 28 days after hospitalisation. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR 3.36-21.69; 95% CI 1.51-163.61 and HR 8.39-10.79; 95% CI 1.20-82.67; p≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterised by a very low risk of mortality. Conclusions: By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6-mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice.
ABSTRACT
Background The relationship between anti-SARS-CoV-2 humoral immune response, pathogenic inflammation, lymphocytes and fatal COVID-19 is poorly understood. Methods Longitudinal prospective cohort of hospitalized patients with COVID-19 (N=254) was followed up to 35 d after admission (median, 8 d). We measured early anti-SARS-CoV-2 S1 antibody IgG levels and dynamic (698 samples) of quantitative circulating T, B, NK lymphocyte subsets and serum interleukin-6 response. We used machine learning to identify patterns of the immune response, and related these patterns to the primary outcome of 28-day mortality in analyses adjusted for clinical severity factors. Results Overall, 45 (18%) patients died within 28 days after hospitalization. We identified six clusters representing discrete anti-SARS-CoV-2 immunophenotypes. Clusters differed considerably in COVID-19 survival. Two clusters, the anti-S1-IgGlowestTlowestBlowestNKmodIL-6mod, and the anti-S1-IgGhighTlowBmodNKmodIL-6highest had a high risk of fatal COVID-19 (HR, 3.36–21.69;95% CI, 1.51–163.61 and HR, 8.39–10.79;95% CI, 1.20–82.67;P≤0.03, respectively). The anti-S1-IgGhighestTlowestBmodNKmodIL-6mod and anti-S1-IgGlowThighestBhighestNKhighestIL-6low cluster were associated with moderate risk of mortality. In contrast, two clusters the anti-S1- anti-S1-IgGhighThighBmodNKmodIL-6low and anti-S1-IgGhighestThighestBhighNKhighIL-6lowest clusters were characterized by a very low risk of mortality. Conclusions By employing unsupervised machine learning we identified multiple anti-SARS-CoV-2 immune response clusters and observed major differences in COVID-19 mortality between these clusters. Two discrete immune pathways may lead to fatal COVID-19. One is driven by impaired or delayed antiviral humoral immunity, independently of hyper-inflammation, and the other may arise through excessive IL-6 mediated host inflammation response, independently of the protective humoral response. Those observations could be explored further for application in clinical practice.
ABSTRACT
BACKGROUND: Previous studies have suggested that the coronavirus disease 2019 (COVID-19) pandemic was associated with a decreased rate of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Data on how the COVID-19 pandemic has influenced mortality, seasonality of, and susceptibility to AECOPD in the chronic obstructive pulmonary disease (COPD) population is scarce. METHODS: We conducted a national population-based retrospective study using data from the Health Insurance Institute of Slovenia from 2015 to February 2021, with 2015-2019 as the reference. We extracted patient and healthcare data for AECOPD, dividing AECOPD into severe, resulting in hospitalisation, and moderate, requiring outpatient care. The national COPD population was generated based on dispensed prescriptions of inhalation therapies, and moderate AECOPD events were analysed based on dispensed AECOPD medications. We extracted data on all-cause and non-COVID mortality. RESULTS: The numbers of severe and moderate AECOPD were reduced by 48% and 34%, respectively, in 2020. In the pandemic year, the seasonality of AECOPD was reversed, with a 1.5-fold higher number of severe AECOPD in summer compared to winter. The proportion of frequent exacerbators (⩾2 AECOPD hospitalisations per year) was reduced by 9% in 2020, with a 30% reduction in repeated severe AECOPD in frequent exacerbators and a 34% reduction in persistent frequent exacerbators (⩾2 AECOPD hospitalisations per year for 2 consecutive years) from 2019. The risk of two or more moderate AECOPD decreased by 43% in 2020. In the multivariate model, pandemic year follow-up was the only independent factor associated with a decreased risk for severe AECOPD (hazard ratio [HR]: 0.71; 95% confidence interval [CI]: 0.61-0.84; p < 0.0001). In 2020, non-COVID mortality decreased (-15%) and no excessive mortality was observed in the COPD population. CONCLUSION: In the pandemic year, we found decreased susceptibility to AECOPD across severity spectrum of COPD, reversed seasonal distribution of severe AECOPD and decreased non-COVID mortality in the COPD population.