An increase in CD62Ldim neutrophils precedes the development of pulmonary embolisms in COVID-19 patients.

OBJECTIVES: A high incidence of pulmonary embolism (PE) is reported in patients with critical coronavirus disease 2019 (COVID-19). Neutrophils may contribute to this through a process referred to as immunothrombosis. The aim of this study was to investigate the occurrence of neutrophil subpopulations in blood preceding the development of COVID-19 associated PE. METHODS: We studied COVID-19 patients admitted to the ICU of our tertiary hospital between 19-03-2020 and 17-05-2020. Point-of-care fully automated flow cytometry was performed prior to ICU admission, measuring the neutrophil activation/maturation markers CD10, CD11b, CD16 and CD62L. Neutrophil receptor expression was compared between patients who did or did not develop PE (as diagnosed on CT angiography) during or after their ICU stay. RESULTS: Among 25 eligible ICU patients, 22 subjects were included for analysis, of whom nine developed PE. The median (IQR) time between neutrophil phenotyping and PE occurrence was 9 (7-12) days. A significant increase in the immune-suppressive neutrophil phenotype CD16bright /CD62Ldim was observed on the day of ICU admission (P = 0.014) in patients developing PE compared to patients who did not. CONCLUSION: The increase in this neutrophil phenotype indicates that the increased number of CD16bright /CD62Ldim neutrophils might be used as prognostic marker to predict those patients that will develop PE in critical COVID-19 patients.

Point-of-care neutrophil and monocyte surface markers differentiate bacterial from viral infections at the emergency department within 30†min.

Rapid discrimination between viral and bacterial infections in a point-of-care setting will improve clinical outcome. Expression of CD64 on neutrophils (neuCD64) increases during bacterial infections, whereas expression of CD169 on classical monocytes (cmCD169) increases during viral infections. The diagnostic value of automated point-of-care neuCD64 and cmCD169 analysis was assessed for detecting bacterial and viral infections at the emergency department. Additionally, their value as input for machine learning models was studied. A prospective observational cohort study in patients suspected of infection was performed at an emergency department. A fully automated point-of-care flow cytometer measured neuCD64, cmCD169, and additional leukocyte surface markers. Flow cytometry data were gated using the FlowSOM algorithm. Bacterial and viral infections were assessed in standardized clinical care. The sole and combined diagnostic value of the markers was investigated. Clustering based on unsupervised machine learning identified unique patient clusters. Eighty-six patients were included. Thirty-five had a bacterial infection, 30 had a viral infection, and 21 had no infection. neuCD64 was increased in bacterial infections (P < 0.001), with an area under the receiver operating characteristic curve (AUROC) of 0.73. cmCD169 was higher in virally infected patients (P < 0.001; AUROC 0.79). Multivariate analyses incorporating additional markers increased the AUROC for bacterial and viral infections to 0.86 and 0.93, respectively. The additional clustering identified 4 distinctive patient clusters based on infection type and outcome. Automated neuCD64 and cmCD169 determination can discriminate between bacterial and viral infections. These markers can be determined within 30 min, allowing fast infection diagnostics in the acute clinical setting.

Visualization of the inflammatory response to injury by neutrophil phenotype categories : Neutrophil phenotypes after trauma.

PURPOSE: The risk of infectious complications after trauma is determined by the amount of injury-related tissue damage and the resulting inflammatory response. Recently, it became possible to measure the neutrophil phenotype in a point-of-care setting. The primary goal of this study was to investigate if immunophenotype categories based on visual recognition of neutrophil subsets are applicable to interpret the inflammatory response to trauma. The secondary goal was to correlate these immunophenotype categories with patient characteristics, injury severity and risk of complications. METHODS: A cohort study was conducted with patients presented at a level 1 trauma center with injuries of any severity, who routinely underwent neutrophil phenotyping. Data generated by automated point-of-care flow cytometry were prospectively gathered. Neutrophil phenotypes categories were defined by visual assessment of two-dimensional CD16/CD62L dot plots. All patients were categorized in one of the immunophenotype categories. Thereafter, the categories were validated by multidimensional analysis of neutrophil populations, using FlowSOM. All clinical parameters and endpoints were extracted from the trauma registry. RESULTS: The study population consisted of 380 patients. Seven distinct immunophenotype Categories (0-6) were defined, that consisted of different neutrophil populations as validated by FlowSOM. Injury severity scores and risk of infectious complications increased with ascending immunophenotype Categories 3-6. Injury severity was similarly low in Categories 0-2. CONCLUSION: The distribution of neutrophil subsets that were described in phenotype categories is easily recognizable for clinicians at the bedside. Even more, multidimensional analysis demonstrated these categories to be distinct subsets of neutrophils. Identification of trauma patients at risk for infectious complications by monitoring the immunophenotype category is a further improvement of personalized and point-of-care decision-making in trauma care.

Identification of neutrophil phenotype categories in geriatric hip fracture patients aids in personalized medicine.

Objectives: The number of geriatric hip fracture patients is high and expected to rise in the coming years, and many are frail and at risk for adverse outcomes. Early identification of high-risk patients is crucial to balance treatment and optimize outcome, but remains challenging. Previous research in patients with multitrauma suggested that neutrophil phenotype analysis could aid in early identification of high-risk patients. This pilot study investigated the feasibility and clinical value of neutrophil phenotype analysis in geriatric patients with a hip fracture. Methods: A prospective study was conducted in a regional teaching hospital in the Netherlands. At the emergency department, blood samples were collected from geriatric patients with a hip fracture and analyzed using automated flow cytometry. Flow cytometry data were processed using an automated clustering algorithm. Neutrophil activation data were compared with a healthy control cohort. Neutrophil phenotype categories were assessed based on two-dimensional visual assessment of CD16/CD62L expression. Results: Blood samples from 45 geriatric patients with a hip fracture were included. Neutrophils showed an increased activation profile and decreased responsiveness to formyl peptides when compared to healthy controls. The neutrophil phenotype of all patients was categorized. The incidence of severe adverse outcome was significantly different between the different categories (P = 0.0331). Moreover, patients with neutrophil phenotype category 0 developed no severe adverse outcomes. Conclusions: Using point-of-care fully automated flow cytometry to analyze the neutrophil compartment in geriatric hip fracture patients is feasible and holds clinical value in determining patients at risk for adverse outcome. This study is a first step toward immuno-based precision medicine for identifying geriatric hip fracture patients that are deemed fit for surgery.

Thrombotic Events in COVID-19 Are Associated With a Lower Use of Prophylactic Anticoagulation Before Hospitalization and Followed by Decreases in Platelet Reactivity.

Background: Coronavirus disease of 2019 (COVID-19) is associated with a prothrombotic state and a high incidence of thrombotic event(s) (TE). Objectives: To study platelet reactivity in hospitalized COVID-19 patients and determine a possible association with the clinical outcomes thrombosis and all-cause mortality. Methods: Seventy nine hospitalized COVID-19 patients were enrolled in this retrospective cohort study and provided blood samples in which platelet reactivity in response to stimulation with ADP and TRAP-6 was determined using flow cytometry. Clinical outcomes included thrombotic events, and all-cause mortality. Results: The incidence of TE in this study was 28% and all-cause mortality 16%. Patients that developed a TE were younger than patients that did not develop a TE [median age of 55 vs. 70 years; adjusted odds ratio (AOR) = 0.96 per 1 year of age, 95% confidence interval (CI) 0.92-1.00; p = 0.041]. Furthermore, patients using preexisting thromboprophylaxis were less likely to develop a thrombotic complication than patients that were not (18 vs. 54%; AOR = 0.19, 95% CI 0.04-0.84; p = 0.029). Conversely, having asthma strongly increased the risk on TE development (AOR = 6.2, 95% CI 1.15-33.7; p = 0.034). No significant differences in baseline P-selectin expression or platelet reactivity were observed between the COVID-19 positive patients (n = 79) and COVID-19 negative hospitalized control patients (n = 21), nor between COVID-19 positive survivors or non-survivors. However, patients showed decreased platelet reactivity in response to TRAP-6 following TE development. Conclusion: We observed an association between the use of preexisting thromboprophylaxis and a decreased risk of TE during COVID-19. This suggests that these therapies are beneficial for coping with COVID-19 associated hypercoagulability. This highlights the importance of patient therapy adherence. We observed lowered platelet reactivity after the development of TE, which might be attributed to platelet desensitization during thromboinflammation.