Exposing and Overcoming Limitations of Clinical Laboratory Tests in COVID-19 by Adding Immunological Parameters; A Retrospective Cohort Analysis and Pilot Study

BackgroundTwo years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. ObjectivesTo measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. ConclusionsLaboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible

Cytotoxic NK cells phenotype and activated lymphocytes are the main characteristics of patients with alcohol-associated liver disease

Altres ajuts: acords transformatius de la UABT cells, natural killer (NK) and NKT cells have opposing actions in the development of alcohol-associated liver fibrosis. We aimed to evaluate the phenotype of NK cells, NKT cells and activated T cells in patients with alcohol use disorder (AUD) according to the presence of advanced liver fibrosis (ALF). Totally, 79 patients (51-years, 71% males) were admitted to treatment of AUD. ALF was defined as FIB4-score > 2.67. Immunophenotyping of NK cells (CD3CD56CD16, CD3CD56CD16, CD3CD56CD16), NKT-like (CD3CD56), and the activation status of CD4, CD8 and regulatory T cells (Tregs) were evaluated according to the HLA-DR expression. Patients had an AUD duration of 18 ± 11 years with a daily alcohol consumption of 155 ± 77 gr/day prior to hospital admission. The values of absolute cells were 2 ± 0.9 cells/L for total lymphocytes, 1054 ± 501 cells/µL for CD4, 540 ± 335 cells/µL for CD8, 49.3 ± 24.8 cells/µL for Tregs, 150.3 ± 97.5 cells/µL for NK cells and 69.8 ± 78.3 cells/µL for NKT-like. The percentage of total NK cells (11.3 ± 5.5% vs. 7 ± 4.3%, p < 0.01), CD3CD56CD16 regarding total lymphocytes (9.7 ± 5.1% vs. 5.8 ± 3.9%, p < 0.01), activated CD4 cells (5.2 ± 3.2% vs. 3.9 ± 3%, p = 0.04) and activated CD8 cells (15.7 ± 9.1% vs. 12.2 ± 9%, p = 0.05) were significantly higher in patients with ALF. The percentage of CD3CD56CD16 regarding NK cells (5.1 ± 3.4% vs. 7.6 ± 6.2%, p = 0.03) was significantly lower in patients with ALF. Activated Tregs (39.9 ± 11.5 vs. 32.4 ± 9.2, p = 0.06) showed a tendency to be higher in patients with ALF. The proportion of activated CD4 cells (r = 0.40, p < 0.01) and activated CD8 cells (r = 0.51, p < 0.01) was correlated with the proportion of NKT-like in patients without ALF. Patients with ALF presented an increased NK cytotoxic phenotype and activated T cells concomitant with a decreased NK cytokine-secreting phenotype

Exposing and Overcoming Limitations of clinical laboratory tests in COVID-19 by adding immunological parameters; A Retrospective cohort analysis and pilot study

Background: Two years since the onset of the COVID-19 pandemic no predictive algorithm has been generally adopted for clinical management and in most algorithms the contribution of laboratory variables is limited. Objectives: To measure the predictive performance of currently used clinical laboratory tests alone or combined with clinical variables and explore the predictive power of immunological tests adequate for clinical laboratories. Methods: Data from 2,600 COVID-19 patients of the first wave of the pandemic in the Barcelona area (exploratory cohort of 1,579, validation cohorts of 598 and 423 patients) including clinical parameters and laboratory tests were retrospectively collected. 28-day survival and maximal severity were the main outcomes considered in the multiparametric classical and machine learning statistical analysis. A pilot study was conducted in two subgroups (n=74 and n=41) measuring 17 cytokines and 27 lymphocyte phenotypes respectively. Findings: 1) Despite a strong association of clinical and laboratory variables with the outcomes in classical pairwise analysis, the contribution of laboratory tests to the combined prediction power was limited by redundancy. Laboratory variables reflected only two types of processes: inflammation and organ damage but none reflected the immune response, one major determinant of prognosis. 2) Eight of the thirty variables: age, comorbidity index, oxygen saturation to fraction of inspired oxygen ratio, neutrophil-lymphocyte ratio, C-reactive protein, aspartate aminotransferase/alanine aminotransferase ratio, fibrinogen, and glomerular filtration rate captured most of the combined statistical predictive power. 3) The interpretation of clinical and laboratory variables was moderately improved by grouping them in two categories i.e., inflammation related biomarkers and organ damage related biomarkers; Age and organ damage-related biomarker tests were the best predictors of survival, and inflammatory-related ones were the best predictors of severity. 4) The pilot study identified immunological tests (CXCL10, IL-6, IL-1RA and CCL2), that performed better than most currently used laboratory tests. Conclusions: Laboratory tests for clinical management of COVID 19 patients are valuable but limited predictors due to redundancy; this limitation could be overcome by adding immunological tests with independent predictive power. Understanding the limitations of tests in use would improve their interpretation and simplify clinical management but a systematic search for better immunological biomarkers is urgent and feasible