Distinct Features of Vascular Diseases in COVID-19

Current development of severe acute respiratory syndrome coronavirus 2 neutralizing antibodies (Review)

The coronavirus disease 2019 pandemic due to severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) seriously affected global public health security. Studies on vaccines, neutralizing antibodies (NAbs) and small molecule antiviral drugs are currently ongoing. In particular, NAbs have emerged as promising therapeutic agents due to their well‑defined mechanism, high specificity, superior safety profile, ease of large‑scale production and simultaneous application for both prevention and treatment of viral infection. Numerous NAb therapeutics have entered the clinical research stages, demonstrating promising therapeutic and preventive effects. These agents have been used for outbreak prevention and control under urgent authorization processes. The present review summarizes the molecular targets of SARS‑CoV‑2‑associated NAbs and screening and identification techniques for NAb development. Moreover, the current shortcomings and challenges that persist with the use of NAbs are discussed. The aim of the present review is to offer a reference for the development of NAbs for any future emergent infectious diseases, including SARS‑CoV‑2.

Exploring the impact of recent COVID-19 infection on perfusion and functional parameters derived from gated myocardial perfusion imaging in patients undergoing evaluation for coronary artery disease

OBJECTIVE

This study seeks to evaluate how recent COVID-19 infection affects myocardial perfusion and functional parameters derived from gated myocardial perfusion imaging in patients undergoing evaluation for coronary artery disease. The goal is to enhance our understanding of COVID-19's influence on the cardiovascular system.

METHOD

Conducted at Farshchian Heart Hospital from 2022 to 2023, this case-control study enrolled patients suspected of coronary artery disease, stratified into two groups: those with confirmed COVID-19 infection within the past 6 months (study group) and those without prior COVID-19 infection (control group). Employing a 2-day protocol, stress testing and gated SPECT MPI were performed. Statistical analysis included descriptive statistics, Chi-square test, Student's t test, and Mann-Whitney U test.

RESULT

Among the 86 patients included, 43 were in each group. Significantly higher summed stress core and summed difference score values were observed in the study group compared to the control group (p < 0.05). In addition, the study group exhibited significantly altered global left ventricular ejection fraction, end-diastolic volume, and end-systolic volume (p < 0.05). Non-perfusion findings, including transient ischemic dilation and transient right ventricular visualization, were more prevalent in the study group.

CONCLUSION

Recent COVID-19 infection is associated with impaired myocardial perfusion and altered functional parameters as detected by MPI. These findings underscore the intricate interplay between COVID-19 and cardiovascular health, emphasizing the importance of comprehensive evaluation and management strategies to address cardiac complications in affected individuals. Further research is warranted to elucidate the underlying mechanisms and optimize patient care in the context of COVID-19-associated cardiovascular manifestations.

Is There a Causal Link Between Acute Myocarditis and COVID-19 Vaccination: An Umbrella Review of Published Systematic Reviews and Meta-Analyses

Introduction

A few months after the beginning of the coronavirus disease of 2019 (COVID-19) vaccination, several reports of myocarditis secondary to the vaccines were published, sometimes with fulminant cases, but until today there is no proven causal link between these 2 events, but with many hypotheses proposed.

Methods

A systematic review of current evidence regarding myocarditis after COVID-19 vaccination was performed by searching several databases including PubMed/Medline and Web of Science. The quality of Meta-analysis was assessed using the AMSTAR-2 tool as well as other qualitative criteria.

Results

Our umbrella review appraised 4 Meta-analysis of retrospective studies (range: 5-12), The number of vaccine doses included ranged from 12 to 179 million, with the number of myocarditis cases observed ranging from 343 to 1489. All types of vaccines were evaluated, with no exclusions. The overall incidence ranged from 0.89 to 2.36 cases of myocarditis per 100 000 doses of vaccine received. Heterogeny was assessed in 3 of the Meta-analysis, and was highly significant (>75%) in all included studies, and with a significant P-value (P < .05). Regarding publication bias, 3 of the Meta-analysis conducted the egger and begg regression, with a significant result in only 1. Regarding the assessment of the methodology by the AMSTAR-2 scale indicating that the quality was very critical in 1, low in 2, and moderate in 1 Meta-analysis.

Conclusion

The quality of current non-randomized evidence on real causality and incidence of myocarditis after COVID-19 vaccine is still low.

Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection

AIM

To evaluate the performance of a machine learning model based on demographic variables, blood tests, pre-existing comorbidities, and computed tomography(CT)-based radiomic features to predict critical outcome in patients with acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

We retrospectively enrolled 694 SARS-CoV-2-positive patients. Clinical and demographic data were extracted from clinical records. Radiomic data were extracted from CT. Patients were randomized to the training (80%, n = 556) or test (20%, n = 138) dataset. The training set was used to define the association between severity of disease and comorbidities, laboratory tests, demographic, and CT-based radiomic variables, and to implement a risk-prediction model. The model was evaluated using the C statistic and Brier scores. The test set was used to assess model prediction performance.

RESULTS

Patients who died (n = 157) were predominantly male (66%) over the age of 50 with median (range) C-reactive protein (CRP) = 5 [1, 37] mg/dL, lactate dehydrogenase (LDH) = 494 [141, 3631] U/I, and D-dimer = 6.006 [168, 152.015] ng/mL. Surviving patients (n = 537) had median (range) CRP = 3 [0, 27] mg/dL, LDH = 484 [78, 3.745] U/I, and D-dimer = 1.133 [96, 55.660] ng/mL. The strongest risk factors were D-dimer, age, and cardiovascular disease. The model implemented using the variables identified using the LASSO Cox regression analysis classified 90% of non-survivors as high-risk individuals in the testing dataset. In this sample, the estimated median survival in the high-risk group was 9 days (95% CI; 9-37), while the low-risk group did not reach the median survival of 50% (p < 0.001).

CONCLUSIONS

A machine learning model based on combined data available on the first days of hospitalization (demographics, CT-radiomics, comorbidities, and blood biomarkers), can identify SARS-CoV-2 patients at risk of serious illness and death.

Association between Lipid Levels, Anti-SARS-CoV-2 Spike Antibodies and COVID-19 Mortality: A Prospective Cohort Study

BACKGROUND

Recent studies suggest that both lipid levels and anti-severe-acute-respiratory-syndrome-coronavirus-2 (SARS-CoV-2) antibody levels are associated with outcome in coronavirus disease 2019 (COVID-19). While both parameters have separately been implicated in the neutralization and clearance of pathogens during severe infections, it is currently unclear whether the interplay of these parameters affects outcome in COVID-19. We therefore aimed to determine whether there was a relationship between lipoproteins, anti-SARS-CoV-2 antibodies, and COVID-19 mortality.

METHODS

In this prospective, multicenter cohort study, we recruited 1152 hospitalized patients with COVID-19 from five hospitals. Total cholesterol (TC), LDL-C, HDL-C, triglycerides, and anti-SARS-CoV-2 spike antibodies were measured on hospital admission. The investigated endpoint was in-hospital mortality.

RESULTS

LDL-C, HDL-C, and TC were significantly lower in non-survivors than in survivors (mg/dL, 95%CI; 56.1, 50.4-61.8 vs. 72.6, 70.2-75.0, p < 0.001; 34.2, 31.7-36.8 vs. 38.1, 37.2-39.1, p = 0.025; 139.3, 130.9-147.7 vs. 157.4, 54.1-160.6, p = 0.002). Mortality risk increased progressively with lower levels of LDL-C, HDL-C, and TC (aOR 1.73, 1.30-2.31, p < 0.001; 1.44, 1.10-1.88, p = 0.008; 1.49, 1.14-1.94, p < 0.001). Mortality rates varied between 2.1% for high levels of both LDL-C and anti-SARS-CoV-2 antibodies and 16.3% for low levels of LDL-C and anti-SARS-CoV-2 antibodies (aOR 9.14, 95%CI 3.17-26.34, p < 0.001). Accordingly, for total cholesterol and anti-SARS-CoV-2 antibodies, mortality rates varied between 2.1% and 15.0% (aOR 8.01, 95%CI 2.77-23.18, p < 0.001).

CONCLUSION

The combination of serum lipid levels and anti-SARS-CoV-2 antibodies is strongly associated with in-hospital mortality of patients with COVID-19. Patients with low levels of LDL-C and total cholesterol combined with low levels of anti-SARS-CoV-2 antibodies exhibited the highest mortality rates.