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.

Evaluation of COVID-19 Patients According to the Survival Time.

BACKGROUND: Coronavirus disease 2019 (COVID-19) was first detected as a form of atypical pneumonia. COVID-19 is a highly contagious virus, and some patients may experience acute respiratory distress syndrome (ARDS) and acute respiratory failure leading to death. We aim to evaluate the clinical, imaging, and laboratory parameters according to survival time to predict mortality in fatal COVID-19 patients. METHODS: Fatal 350 and survived 150 COVID-19 patients were included in the study. Fatal patients were divided into three groups according to the median value of the survival days. Demographic characteristics and in-hospital complications were obtained from medical databases. RESULTS: Of the non-survived patients, 30% (104) died within three days, 32% (110) died within 4-10 days, and 39% (136) died within over ten days. Pneumonia on computational tomography (CT), symptom duration before hospital admission (SDBHA), intensive care unit (ICU), hypertension (HT), C-reactive protein (CRP), D-dimer, multi-organ dysfunction syndrome (MODS), cardiac and acute kidney injury, left ventricular ejection fraction (LVEF), right ventricular fractional area change (RV-FAC), and Tocilizumab/Steroid therapy were independent predictors of mortality within three days compared to between 4-10 days and over ten days mortality.  A combined diagnosis model was evaluated for the age, CT score, SDBHA, hs-TnI, and D-dimer. The combined model had a higher area under the ROC curve (0.913). CONCLUSION: This study showed that age, pneumonia on CT, SDBHA, ICU, HT, CRP, d-dimer, cardiac injury, MODS, acute kidney injury, LVEF, and RV-FAC were independently associated with short-term mortality in non-surviving COVID-19 patients in the Turkish population. Moreover, Tocilizumab/Steroid therapy was a protective and independent predictor of mortality within three days.

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.

Evaluation of Modified ATRIA Risk Score in Predicting Mortality in Hospitalized Patients With COVID-19.

BACKGROUND: As the Modified Anticoagulation and Risk Factors in Atrial Fibrillation Risk Score (M-ATRIA-RS) encompasses prognostic risk factors of novel coronavirus-2019 (COVID-19), it may be used to predict in-hospital mortality. We aimed to investigate whether M-ATRIA-RS was an independent predictor of mortality in patients hospitalized for COVID-19 and compare its discrimination capability with CHADS, CHA2DS2-VASc, and modified CHA2DS2-VASc (mCHA2DS2-VASc)-RS. METHODS: A total of 1,001 patients were retrospectively analyzed and classified into three groups based on M-ATRIA-RS, designed by changing sex criteria of ATRIA-RS from female to male: Group 1 for points 0-1 (n = 448), Group 2 for points 2-4 (n = 268), and Group 3 for points ≥5 (n = 285). Clinical outcomes were defined as in-hospital mortality, need for high-flow oxygen and/or intubation, and admission to intensive care unit. RESULTS: As the M-ATRIA-RS increased, adverse clinical outcomes significantly increased (Group 1, 6.5%; Group 2, 15.3%; Group 3, 34.4%; p <0.001 mortality for in-hospital). Multivariate logistic regression analysis showed that M-ATRIA-RS, malignancy, troponin increase, and lactate dehydrogenase were independent predictors of in-hospital mortality (p<0.001, per scale possibility rate for ATRIA-RS 1.2). In receiver operating characteristic (ROC) analysis, the discriminative ability of M-ATRIA-RS was superior to mCHA2DS2-VASc-RS and ATRIA-RS, but similar to that Charlson Comorbidity Index (CCI) score (AUCM-ATRIAvs AUCATRIA Z-test=3.14 p = 0.002, AUCM-ATRIAvs. AUCmCHA2DS2-VASc Z-test=2.14, p = 0.03; AUCM-ATRIAvs. AUCCCI Z-test=1.46 p = 0.14). CONCLUSIONS: M-ATRIA-RS is useful to predict in-hospital mortality among patients hospitalized with COVID-19. In addition, it is superior to the mCHA2DS2-VASc-RS in predicting mortality in patients with COVID-19 and is more easily calculable than the CCI score.