The role of serum inflammatory markers, albumin, and hemoglobin in predicting the diagnosis in patients admitted to the emergency department with a pre-diagnosis of COVID-19

Analysis of Risk Factors for Death in the Coronavirus Disease 2019 (COVID-19) Population: Data Analysis from a Large General Hospital in Anhui, China

During the coronavirus disease 2019 (COVID-19) pandemic, clinical prevention, early diagnosis, and hematological monitoring were challenging areas. This study aims to compare risk factors and hematological and biochemical data in non-survivor group patients with COVID-19 versus survivor group patients. A total of 204 patients with COVID-19 were selected as research subjects from December 2022 to January 2023. We analyzed the age, sex, time from onset to admission, and laboratory test indicators upon admission. The differences between surviving and deceased patients and mortality-related risk factors were examined. Among the 204 patients, 168 survived, whereas 36 died during hospitalization. Significant differences were observed between the two groups with COVID-19 across various factors, including age (p < 0.0001), WBC count (p < 0.0001), RBC count (p < 0.05), neutrophils (p < 0.0001), lymphocytes (p < 0.05), mean corpuscular hemoglobin concentration (MCHC) (p < 0.0001), RBC distribution width-standard deviation (RDW-SD) (p < 0.0001), RBC distribution width coefficient of variation (RDW-CV) (p < 0.0001), aspartate aminotransferase (AST) (p < 0.05), albumin (ALB) (p < 0.0001), creatinine (CR) (p < 0.0001), uric acid (UA) (p < 0.0001), blood urea nitrogen (BUN) (p < 0.0001), plasma thrombin time (TT) (p < 0.05), prothrombin time (PT) (p < 0.0001), and D-dimer (p < 0.0001). Multivariate logistic analysis revealed that older age, CR, UA, and ALB were independent factors associated with death (p < 0.05). Elderly patients with underlying diseases, abnormal routine blood test indices, and abnormal renal function and coagulation indices are at an increased worse prognosis and should be identified early. Age, UA, CR, and ALB can be used as predictors to assess the worse prognosis in the hospital.

Scrutinizing Clinical Biomarkers in a Large Cohort of Patients with Lyme Disease and Other Tick-Borne Infections

Standard clinical markers can improve tick-borne infection (TBI) diagnoses. We investigated immune and other clinical biomarkers in 110 patients clinically diagnosed with TBIs before (T0) and after antibiotic treatment (T2). At T0, both the initial observation group and patients without seroconversion for tick-borne pathogens exhibited notably low percentages and counts of CD3 percentage (CD3%), CD3+ cells, CD8+ suppressors, CD4 percentage (CD4%), and CD4+ helper cells, with the latter group showing reductions in CD3%, CD3+, and CD8+ counts in approximately 15-22% of cases. Following treatment at the T2 follow-up, patients typically experienced enhancements in their previously low CD3%, CD3+ counts, CD4%, and CD4+ counts; however, there was no notable progress in their low CD8+ counts, and a higher number of patients presented with insufficient transferrin levels. Moreover, among those with negative serology for tick-borne infections, there was an improvement in low CD3% and CD3+ counts, which was more pronounced in patients with deficient transferrin amounts. Among those with CD57+ (n = 37) and CD19+ (n = 101) lymphocyte analysis, 59.46% of patients had a low CD57+ count, 14.85% had a low CD19 count, and 36.63% had a low CD19 percentage (CD19%). Similar findings were observed concerning low CD57+, CD19+, and CD19% markers for negative TBI serology patients. Overall, this study demonstrates that routine standard clinical markers could assist in a TBI diagnosis.

Blood Hemoglobin Substantially Modulates the Impact of Gender, Morbid Obesity, and Hyperglycemia on COVID-19 Death Risk: A Multicenter Study in Italy and Spain

BACKGROUND

Hyperglycemia and obesity are associated with a worse prognosis in subjects with COVID-19 independently. Their interaction as well as the potential modulating effects of additional confounding factors is poorly known. Therefore, we aimed to identify and evaluate confounding factors affecting the prognostic value of obesity and hyperglycemia in relation to mortality and admission to the intensive care unit (ICU) due to COVID-19.

METHODS

Consecutive patients admitted in two Hospitals from Italy (Bologna and Rome) and three from Spain (Barcelona and Girona) as well as subjects from Primary Health Care centers. Mortality from COVID-19 and risk for ICU admission were evaluated using logistic regression analyses and machine learning (ML) algorithms.

RESULTS

As expected, among 3,065 consecutive patients, both obesity and hyperglycemia were independent predictors of ICU admission. A ML variable selection strategy confirmed these results and identified hyperglycemia, blood hemoglobin and serum bilirubin associated with increased mortality risk. In subjects with blood hemoglobin levels above the median, hyperglycemic and morbidly obese subjects had increased mortality risk than normoglycemic individuals or non-obese subjects. However, no differences were observed among individuals with hemoglobin levels below the median. This was particularly evident in men: those with severe hyperglycemia and hemoglobin concentrations above the median had 30 times increased mortality risk compared with men without hyperglycemia. Importantly, the protective effect of female sex was lost in subjects with increased hemoglobin levels.

CONCLUSIONS

Blood hemoglobin substantially modulates the influence of hyperglycemia on increased mortality risk in patients with COVID-19. Monitoring hemoglobin concentrations seem of utmost importance in the clinical settings to help clinicians in the identification of patients at increased death risk.