RESUMO
BACKGROUND: The association between neutrophil extracellular traps (NETs) and the requirement for vasopressor and inotropic support in vasoplegic shock is unclear. This study aimed to investigate the dynamics of plasma levels of NETs and cell-free DNA (cfDNA) up to 48 hours after the admission to the intensive care unit (ICU) for management of vasoplegic shock of infectious (SEPSIS) or non-infectious (following cardiac surgery, CARDIAC) origin. METHODS: Prospective, observational study of NETs and cfDNA plasma levels at 0H (admission) and then at 12H, 24H and 48H in SEPSIS and CARDIAC patients. The Vasopressor Inotropic Score (VIS), the Sequential Organ Failure Assessment (SOFA) score and time spent with invasive ventilation, in ICU and in hospital were recorded. Associations between NETs/cfDNA and VIS and SOFA were analysed by Spearman's correlation (rho), and between NETs/cfDNA and ventilation/ICU/hospitalisation times by generalised linear regression. RESULTS: Both NETs and cfDNA remained elevated over 48 hours in SEPSIS (n = 46) and CARDIAC (n = 30) patients, with time weighted average concentrations greatest in SEPSIS (NETs median difference 0.06 [0.02-0.11], p = 0.005; cfDNA median difference 0.48 [0.20-1.02], p < 0.001). The VIS correlated to NETs (rho = 0.3-0.60 in SEPSIS, p < 0.01, rho = 0.36-0.57 in CARDIAC, p ≤ 0.01) and cfDNA (rho = 0.40-0.56 in SEPSIS, p < 0.01, rho = 0.38-0.47 in CARDIAC, p < 0.05). NETs correlated with SOFA. Neither NETs nor cfDNA were independently associated with ventilator/ICU/hospitalisation times. CONCLUSION: Plasma levels of NETs and cfDNA correlated with the dose of vasopressors and inotropes administered over 48 hours in patients with vasoplegic shock from sepsis or following cardiac surgery. NETs levels also correlated with organ dysfunction. These findings suggest that similar mechanisms involving release of NETs are involved in the pathophysiology of vasoplegic shock irrespective of an infectious or non-infectious etiology.
RESUMO
Background: Machine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. Hence, we hypothesize that blood immune cell signatures can act as a biomarker for cancer progression. Methods: To probe this, we have developed and tested a multiparameter cell-surface marker screening pipeline, using flow cytometry to obtain high-resolution systemic leukocyte population profiles that correlate with detection and characterization of several cancers in murine syngeneic tumor models. Results: We discovered a signature of several blood leukocyte subsets, the most notable of which were monocyte subsets, that could be used to train CATboost ML models to predict the presence and type of cancer present in the animals. Conclusions: Our findings highlight the potential utility of a screening approach to identify robust leukocyte biomarkers for cancer detection and characterization. This pipeline can easily be adapted to screen for cancer specific leukocyte markers from the blood of cancer patient.
