Value of dynamic plasma cell-free DNA monitoring in septic shock syndrome: A case report

Clinical Characteristics of Severe COVID-19 Patients During Omicron Epidemic and a Nomogram Model Integrating Cell-Free DNA for Predicting Mortality: A Retrospective Analysis

Objective

This study aimed to investigate the clinical characteristics and risk factors of death in severe coronavirus disease 2019 (COVID-19) during the epidemic of Omicron variants, assess the clinical value of plasma cell-free DNA (cfDNA), and construct a prediction nomogram for patient mortality.

Methods

The study included 282 patients with severe COVID-19 from December 2022 to January 2023. Patients were divided into survival and death groups based on 60-day prognosis. We compared the clinical characteristics, traditional laboratory indicators, and cfDNA concentrations at admission of the two groups. Univariate and multivariate logistic analyses were performed to identify independent risk factors for death in patients with severe COVID-19. A prediction nomogram for patient mortality was constructed using R software, and an internal validation was performed.

Results

The median age of the patients included was 80.0 (71.0, 86.0) years, and 67.7% (191/282) were male. The mortality rate was 55.7% (157/282). Age, tracheal intubation, shock, cfDNA, and urea nitrogen (BUN) were the independent risk factors for death in patients with severe COVID-19, and the area under the curve (AUC) for cfDNA in predicting patient mortality was 0.805 (95% confidence interval [CI]: 0.713-0.898, sensitivity 81.4%, specificity 75.6%, and cut-off value 97.67 ng/mL). These factors were used to construct a prediction nomogram for patient mortality (AUC = 0.856, 95% CI: 0.814-0.899, sensitivity 78.3%, and specificity 78.4%), C-index was 0.856 (95% CI: 0.832-0.918), mean absolute error of the calibration curve was 0.007 between actual and predicted probabilities, and Hosmer-Lemeshow test showed no statistical difference (χ2=6.085, P=0.638).

Conclusion

There was a high mortality rate among patients with severe COVID-19. cfDNA levels ≥97.67 ng/mg can significantly increase mortality. When predicting mortality in patients with severe COVID-19, a nomogram based on age, tracheal intubation, shock, cfDNA, and BUN showed high accuracy and consistency.

Specialized Cell-Free DNA Blood Collection Tubes Can Be Repurposed for Extracellular Vesicle Isolation: A Pilot Study

Background: Liquid biopsies offer a minimally invasive approach to patient disease diagnosis and monitoring. However, these are highly affected by preprocessing variables with many protocols designed for downstream analysis of a single molecular biomarker. Here we investigate whether specialized blood tubes could be repurposed for the analysis of an increasingly valuable biomarker, extracellular vesicles (EVs). Methods: Blood was collected from three donors into K3-EDTA, Roche, or Streck cell-free DNA (cfDNA) collection tubes and processed using sequential centrifugation either immediately or after storage for 3 days. MicroEV were collected from platelet-poor plasma by 10,000 g centrifugation and NanoEVs isolated using size exclusion chromatography. Particle size and counts were assessed by Nanoparticle Tracking Analysis, protein quantitation by bicinchoninic acid assay (BCA) assay, and dot blotting for blood cell surface proteins. Results: MicroEVs and NanoEVs could be isolated from plasma collected using all three tube types. Major variations were seen with delayed time to processing. Both MicroEV particle number and protein content increased with the processing delay. The NanoEV number did not change with the time-delay but their protein quantity increased. EV-associated proteins predominantly arose from platelets (CD61) and erythrocytes (CD235a). However, leukocyte marker CD45 was only increased in NanoEVs from ethylenediaminetetraacetic acid (EDTA) tubes, suggestive of stabilization of nucleated cells by the specialized blood tubes. Epithelial cell surface marker EpCAM, often used as a marker of cancer, remained the same across conditions in both MicroEV and NanoEV preparations indicating that these EVs were stable with time. Conclusions: Specialized cfDNA collection tubes can be repurposed for MicroEV and NanoEV analysis; however, simple counting or using protein quantity as a surrogate of EV number may be confounded by preanalytical processing. The EVs would be suitable for disease selective EV subtype analysis if the molecular target of interest is not present in blood cells.