Elevations in Liver Transaminases in COVID-19: (How) Are They Related?

Hematological profile, inflammatory markers and serum liver enzymes in COVID 19 positive children vs. COVID 19 negative ones-a comparative study

Background

Complete blood count, C-reactive protein and transaminases are routine laboratory parameters investigated in children with infections, including COVID 19. We aimed to evaluate the diagnostic accuracy of these parameters in children diagnosed with COVID 19.

Methods

At the time of admission, children with COVID 19 suggestive symptoms were tested RT-PCR for SARS CoV-2 and were allocated to either the study group (RT-PCR SARS CoV-2 positive) or control group (RT-PCR SARS CoV-2 negative). All children were evaluated by complete blood count, CRP, and transaminases.

Results

When comparing the two groups, we identified significantly lower values for leukocytes (p < 0.001), neutrophils (p < 0.001), lymphocytes (p < 0.001) and thrombocytes (p = 0.014), but no significantly different values for CRP (p = 0.916) and monocytes (p = 0.082). A diagnostic score for COVID-19 was compiled using the abovementioned parameters-presence of fever, number of lymphocytes and aspartate-aminotransferase. Performance was tested, showing a positive discrimination value (AUC of 0.703)-81.5% sensitivity, 50.6% specificity.

Conclusions

The leukocytes, neutrophils and lymphocytes have significantly lower values in COVID-19 children. The proposed score based on the presence of fever the values of lymphocytes and AST has a good sensitivity in predicting COVID-19 infection.

COVID-19, Possible Hepatic Pathways and Alcohol Abuse-What Do We Know up to 2023?

The pandemic period due to coronavirus disease 2019 (COVID-19) revolutionized all possible areas of global health. Significant consequences were also related to diverse extrapulmonary manifestations of this pathology. The liver was found to be a relatively common organ, beyond the respiratory tract, affected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Multiple studies revealed the essential role of chronic liver disease (CLD) in the general outcome of coronavirus infection. Present concerns in this field are related to the direct hepatic consequences caused by COVID-19 and pre-existing liver disorders as risk factors for the severe course of the infection. Which mechanism has a key role in this phenomenon-previously existing hepatic disorder or acute liver failure due to SARS-CoV-2-is still not fully clarified. Alcoholic liver disease (ALD) constitutes another not fully elucidated context of coronavirus infection. Should the toxic effects of ethanol or already developed liver cirrhosis and its consequences be perceived as a causative or triggering factor of hepatic impairment in COVID-19 patients? In the face of these discrepancies, we decided to summarize the role of the liver in the whole picture of coronavirus infection, paying special attention to ALD and focusing on the pathological pathways related to COVID-19, ethanol toxicity and liver cirrhosis.

Hematologic characteristics and coagulopathy in pregnancy with COVID-19 succeeding the first wave: a multicenter retrospective cross-sectional study

Background

Early reports have demonstrated an association of COVID-19 infection during pregnancy and postpartum period with coagulopathy and bleeding complications and indicated that pregnant people with COVID-19 are more likely to experience coagulopathy and venous thromboembolism. A recent report concerning such complications during the first wave of the pandemic was reassuring; however, no publications have evaluated these issues in the context of increased illness severity with the emergence of SARS-CoV-2 variants of concern.

Objectives

We performed a retrospective, multinational cohort study in Canada, Romania, and the United Kingdom, aiming to provide a comprehensive analysis of the hematologic test characteristics of pregnancies affected by COVID-19 after the first wave of the pandemic.

Results

Three-hundred-seventy patients were evaluated. Markers of inflammation and endothelial dysfunction were significantly elevated, in keeping with observations in the nonpregnant population. Reassuringly, despite more severe disease noted in succeeding waves of the pandemic, there was no significant evidence of COVID-19-associated coagulopathy, and overall, no association was demonstrated between isolated coagulation abnormalities and bleeding risk. Notably, fibrinogen below 2g/L was again linked with the risk of postpartum hemorrhage. Finally, venous thromboembolism risk was low but noted more frequently in those with severe illness despite thromboprophylaxis.

Conclusion

Our findings add valuable insights into the nature of hematologic test characteristics, bleeding, and thrombotic complications for those affected with COVID-19 in pregnancy, reassuring readers of the low incidence of bleeding and thrombotic complications but inviting further debate as to the degree of thromboprophylaxis that may benefit the subgroup with severe disease.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes

BACKGROUND

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

METHODS

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

FINDINGS

We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.

INTERPRETATION

Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

FUNDING

NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Complications During Hospitalization in Patients With SARS-CoV-2 Pneumonia in a Romanian Pulmonary Center

Introduction The coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. Facing a new and unknown virus, the entire medical community made considerable efforts to find a specific treatment, develop guidelines, and even create a vaccine. Besides all the measures taken, a wide range of complications associated with the disease increased the mortality and morbidity rates, adding more difficulty to the management of the patients. Study design We performed a retrospective study, including the patients with SARS-CoV-2 pneumonia who were admitted to our hospital between March 2020 and August 2021. We analyzed complications that developed during the hospitalization, such as respiratory failure or acute injury to other organs (the heart, pancreas, kidneys, and liver), and whether they were treatment- and hospitalization-related. Results One thousand eight hundred and forty-four cases were evaluated, and we analyzed the complications that developed during the hospitalization. Out of this, 1392 (75.48%) cases developed at least one complication during hospitalization, most frequently respiratory failure (71.14%), hyperglycemia (43.54%), renal injury (42.67%), or cardiovascular events (7.10%). Conclusion SARS-CoV-2 infection in hospitalized patients with pneumonia can cause injuries to any organ, making the management of those patients even more difficult.

COVID-19 Presenting With Acute Anicteric Hepatitis in Pediatric Patient: A Case Report

The impact that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has on the hepatobiliary system is poorly described in the pediatric population compared with the adult population. In adults, gastrointestinal symptoms and marked elevation in liver enzymes in the setting of coronavirus disease 2019 (COVID-19) has been directly correlated with disease severity. This case is a unique presentation of a pediatric patient with a relatively mild disease course despite the presence of gastrointestinal symptoms and marked elevation in transaminases, suggesting that SARS-CoV-2 virus may cause isolated acute hepatitis in pediatric patients.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs

Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.

Phenylalanine and COVID-19: Tracking disease severity markers

BACKGROUND

Although there are several severity predictors for COVID-19, none are specific. Serum levels of phenylalanine were recently associated with increased inflammation, higher SOFA scores, ICU admission, and mortality rates among non-COVID-19 patients. Here, we investigated the relationship between phenylalanine and inflammatory markers in adults with COVID-19.

METHODS

We assessed adults with COVID-19 at hospital admission for clinical and laboratory data. Nuclear magnetic resonance spectroscopy measured serum levels of phenylalanine and other amino acids of its metabolomic pathway. Flow Cytometry measured serum levels of IL-2, IL-4, IL-6, Il-10, TNF-α, and IFN-γ. Linear regression models adjusted for potential confounders assessed the relationship between serum levels of phenylalanine and inflammatory cytokines.

RESULTS

Phenylalanine and tyrosine were significantly lower in mild disease as compared to moderate and severe groups. Linear regression models showed that phenylalanine is independently and positively associated with disease severity regardless of the cytokine analyzed and after adjustment for potential confounders. In addition, mild cases showed consistently lower serum phenylalanine levels within the first ten days from disease onset to hospital admission.

CONCLUSIONS

Phenylalanine is a marker of disease severity. This association is independent of the time between the onset of symptoms and the magnitude of the inflammatory state.