Plasminogen activator inhibitor-1 levels as an indicator of severity and mortality for COVID-19

OBJECTIVE Coronavirus disease-19 (COVID-19) is a multisystemic disease that can cause severe illness and mortality by exacerbating symptoms such as thrombosis, fibrinolysis, and inflammation. Plasminogen activator inhibitor-1 (PAI-1) plays an important role in regulating fibrinolysis and may cause thrombotic events to develop. The goal of this study is to examine the relationship between PAI-1 levels and disease severity and mortality in relation to COVID-19. METHODS A total of 71 hospitalized patients were diagnosed with COVID-19 using real time-polymerase chain reaction tests. Each patient underwent chest computerized tomography (CT). Data from an additional 20 volunteers without COVID-19 were included in this single-center study. Each patient's PAI-1 data were collected at admission, and the CT severity score (CT-SS) was then calculated for each patient. RESULTS The patients were categorized into the control group (n=20), the survivor group (n=47), and the non-survivor group (n=24). In the non-survivor group, the mean age was 75.3±13.8, which is higher than in the survivor group (61.7±16.9) and in the control group (59.5±11.2), (p=0.001). When the PAI-1 levels were compared between each group, the non-survivor group showed the highest levels, followed by the survivor group and then the control group (p<0.001). Logistic regression analysis revealed that age, PAI-1, and disease severity independently predicted COVID-19 mortality rates. In this study, it was observed that PAI-1 levels with >10.2 ng/mL had 83% sensitivity and an 83% specificity rate when used to predict mortality after COVID-19. Then, patients were divided into severe (n=33) and non-severe (n=38) groups according to disease severity levels. The PAI-1 levels found were higher in the severe group (p<0.001) than in the non-severe group. In the regression analysis that followed, high sensitive troponin I and PAI-1 were found to indicate disease severity levels. The CT-SS was estimated as significantly higher in the non-survivor group compared to the survivor group (p<0.001). When comparing CT-SS between the severe group and the non-severe group, this was significantly higher in the severe group (p<0.001). In addition, a strong statistically significant positive correlation was found between CT-SS and PAI-1 levels (r: 0.838, p<0.001). CONCLUSION Anticipating poor clinical outcomes in relation to COVID-19 is crucial. This study showed that PAI-1 levels could independently predict disease severity and mortality rates for patients with COVID-19.

Prognostic significance of HDL-C on long-term mortality in patients with COVID-19 pneumonia in the Turkish population: A potential mechanism for population differences.

Coronavirus disease 2019 (COVID-19) is diagnosed by the evidence of the presence of multiple phenotypes, including thrombosis, inflammation, and alveolar and myocardial damage, which can cause severe illness and mortality. High-density lipoprotein cholesterol (HDL-C) has pleiotropic properties, including anti-inflammatory, anti-infectious, antithrombotic, and endothelial cell protective effects. The aim of this study was to investigate the HDL-C levels and one-year mortality after the first wave of patients with COVID-19 were hospitalized. Data from 101 patients with COVID-19 were collected for this single-center retrospective study. Lipid parameters were collected on the admission. The relationship between lipid parameters and long-term mortality was investigated. The mean age of the non-survivor group (n = 38) was 68.8 ± 14.1 years, and 55% were male. The HDL-C levels were significantly lower in the non-survivors group compared with the survivors (26.9 ± 9.5 vs 36.8 ± 12.8 mg/dl, respectively p < 0.001). Multivariate regression analysis determined that age, C-reactive protein, D-dimer, hypertension, and HDL-C as independent predictors for the development of COVID-19 mortality. HDL-C levels <30.5 mg/dl had 71% sensitivity and 68% specificity to predict one-year mortality after COVID-19. The findings of this study showed that HDL-C is a predictor of one-year mortality in Turkish patients with COVID-19. COVID-19 is associated with decreased lipid levels, and it is an indicator of the inflammatory burden and increased mortality rate. The consequences of long-term metabolic dysregulations in patients that have recovered from COVID-19 still need to be understood.

Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study.

BACKGROUND: The current knowledge about novel coronavirus-2019 (COVID-19) indicates that the immune system and inflammatory response play a crucial role in the severity and prognosis of the disease. In this study, we aimed to investigate prognostic value of systemic inflammatory biomarkers including C-reactive protein/albumin ratio (CAR), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in patients with severe COVID-19. METHODS: This single-center, retrospective study included a total of 223 patients diagnosed with severe COVID-19. Primary outcome measure was mortality during hospitalization. Multivariate logistic regression analyses were performed to identify independent predictors associated with mortality in patients with severe COVID-19. Receiver operating characteristic (ROC) curve was used to determine cut-offs, and area under the curve (AUC) values were used to demonstrate discriminative ability of biomarkers. RESULTS: Compared to survivors of severe COVID-19, non-survivors had higher CAR, NLR, and PLR, and lower LMR and lower PNI (P < .05 for all). The optimal CAR, PNI, NLR, PLR, and LMR cut-off values for detecting prognosis were 3.4, 40.2, 6. 27, 312, and 1.54 respectively. The AUC values of CAR, PNI, NLR, PLR, and LMR for predicting hospital mortality in patients with severe COVID-19 were 0.81, 0.91, 0.85, 0.63, and 0.65, respectively. In ROC analysis, comparative discriminative ability of CAR, PNI, and NLR for hospital mortality were superior to PLR and LMR. Multivariate analysis revealed that CAR (⩾0.34, P = .004), NLR (⩾6.27, P = .012), and PNI (⩽40.2, P = .009) were independent predictors associated with mortality in severe COVID-19 patients. CONCLUSIONS: The CAR, PNI, and NLR are independent predictors of mortality in hospitalized severe COVID-19 patients and are more closely associated with prognosis than PLR or LMR.