Predictive prognostic biomarkers in patients with COVID‑19 infection

The present study aimed to identify useful biomarkers to predict deterioration in patients with coronavirus disease 2019 (COVID‑19). A total of 201 COVID‑19 patients were classified according to their disease severity into non‑severe (n=125) and severe (n=76) groups, and the behavior of laboratory biomarkers was examined according to the prognosis. Neutrophil count, aspartate aminotransferase (AST), alanine aminotransferase, lactate dehydrogenase (LDH), C‑reactive protein (CRP), sialylated carbohydrate antigen KL‑6 (KL‑6), procalcitonin (PCT), presepsin (PSP) and D‑dimer levels were significantly higher, and lymphocyte count and platelet count were significantly lower in the non‑severe group compared with the severe group. In the non‑severe group, ROC analysis demonstrated that only four biomarkers, CRP, PSP, AST and LDH were useful for differentiating the prognosis between improvement and deterioration subgroups. No strong correlation was revealed for any of the markers. Multivariate analysis identified CRP as a significant prognostic factor in non‑severe cases (odds ratio, 41.45;95% confidence interval, 4.91‑349.24;P<0.001). However, there were no blood biomarkers that could predict the outcome of patients in the severe group. Overall, several blood markers changed significantly according to disease severity in the course of COVID‑19 infection. Among them, CRP, PSP, LDH and AST were the most reliable markers for predicting the patient's prognosis in non‑severe COVID‑19 cases.

Identification of High Death Risk Coronavirus Disease-19 Patients using Blood Tests.

Background: The coronavirus disease (COVID-19) pandemic has made a great impact on health-care services. The prognosis of the severity of the disease help reduces mortality by prioritizing the allocation of hospital resources. Early mortality prediction of this disease through paramount biomarkers is the main aim of this study. Materials and Methods: In this retrospective study, a total of 205 confirmed COVID-19 patients hospitalized from June 2020 to March 2021 were included. Demographic data, important blood biomarkers levels, and patient outcomes were investigated using the machine learning and statistical tools. Results: Random forests, as the best model of mortality prediction, (Matthews correlation coefficient = 0.514), were employed to find the most relevant dataset feature associated with mortality. Aspartate aminotransferase (AST) and blood urea nitrogen (BUN) were identified as important death-related features. The decision tree method was identified the cutoff value of BUN >47 mg/dL and AST >44 U/L as decision boundaries of mortality (sensitivity = 0.4). Data mining results were compared with those obtained through the statistical tests. Statistical analyses were also determined these two factors as the most significant ones with P values of 4.4 × 10-7 and 1.6 × 10-6, respectively. The demographic trait of age and some hematological (thrombocytopenia, increased white blood cell count, neutrophils [%], RDW-CV and RDW-SD), and blood serum changes (increased creatinine, potassium, and alanine aminotransferase) were also specified as mortality-related features (P < 0.05). Conclusions: These results could be useful to physicians for the timely detection of COVID-19 patients with a higher risk of mortality and better management of hospital resources.

A systematic review and meta-analysis of the COVID-19 associated liver injury.

INTRODUCTION AND OBJECTIVES: The novel coronavirus disease 2019 (COVID-19) has affected more than 5 million people globally. Data on the prevalence and degree of COVID-19 associated liver injury among patients with COVID-19 remain limited. We conducted a systematic review and meta-analysis to assess the prevalence and degree of liver injury between patients with severe and non-severe COVID-19. METHODS: We performed a systematic search of three electronic databases (PubMed/MEDLINE, EMBASE and Cochrane Library), from inception to 24th April 2020. We included all adult human studies (>20 subjects) regardless of language, region or publication date or status. We assessed the pooled odds ratio (OR), mean difference (MD) and 95% confidence interval (95%CI) using the random-effects model. RESULTS: Among 1543 citations, there were 24 studies (5961 subjects) which fulfilled our inclusion criteria. The pooled odds ratio for elevated ALT (OR = 2.5, 95%CI: 1.6-3.7, I2 = 57%), AST (OR = 3.4, 95%CI: 2.3-5.0, I2 = 56%), hyperbilirubinemia (OR = 1.7, 95%CI: 1.2-2.5, I2 = 0%) and hypoalbuminemia (OR = 7.1, 95%CI: 2.1-24.1, I2 = 71%) were higher subjects in critical COVID-19. CONCLUSION: COVID-19 associated liver injury is more common in severe COVID-19 than non-severe COVID-19. Physicians should be aware of possible progression to severe disease in subjects with COVID-19-associated liver injury.