ABO blood type association with SARS-CoV-2 infection mortality: A single-center population in New York City.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a variable clinical course with significant mortality. Early reports suggested higher rates of SARS-CoV-2 infection in patients with type A blood and enrichment of type A individuals among COVID-19 mortalities. STUDY DESIGN AND METHODS: The study includes all patients hospitalized or with an emergency department (ED) visit who were tested for SARS-CoV-2 between March 10, 2020 and June 8, 2020 and had a positive test result by nucleic acid test (NAT) performed on a nasopharyngeal swab specimen. A total of 4968 patients met the study inclusion criteria, with a subsequent 23.1% (n = 1146/4968) all-cause mortality rate in the study cohort. To estimate overall risk by ABO type and account for the competing risks of in-hospital mortality and discharge, we calculated the cumulative incidence function (CIF) for each event. Cause-specific hazard ratios (csHRs) for in-hospital mortality and discharge were analyzed using multivariable Cox proportional hazards models. RESULTS: Type A blood was associated with the increased cause-specific hazard of death among COVID-19 patients compared to type O (HR = 1.17, 1.02-1.33, p = .02) and type B (HR = 1.32,1.10-1.58, p = .003). CONCLUSIONS: Our study shows that ABO histo-blood group type is associated with the risk of in-hospital death in COVID-19 patients, warranting additional inquiry. Elucidating the mechanism behind this association may reveal insights into the susceptibility and/or immunity to SARS-CoV-2.

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study.

BACKGROUND: During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model. OBJECTIVE: In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model. METHODS: Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables. RESULTS: Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791). CONCLUSIONS: We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning-based clinical decision support tools.

First Report of Compound Heterozygosity for Hb S (HBB: c.20A>T) and Hb Haringey (HBB: c.131A>G).

Sickle cell disease variants include hemoglobinopathies that result from inheritance of the sickle cell globin mutation with another globin mutation. The most common variants include the homozygous disease state (Hb SS disease), Hb S (HBB: c.20A>T)/Hb C (HBB: c.19G>A) disease and Hb S/ß-thalassemia (Hb S/ß-thal). Other rare/less common variants such as Hb S/Hb E (HBB: c.79G>A) and Hb S/HPFH [hereditary persistence of fetal hemoglobin (Hb)] disease exist. We report the first case of compound heterozygosity for Hb S and Hb Haringey (HBB: c.131A>G) in a 35-year-old male following a positive sickle screen test on hospital admission for pancreatitis. Ion exchange high performance liquid chromatography (HPLC), Hb electrophoresis and genetic sequencing were utilized to identify a new sickle Hb variant: Hb S/Hb Haringey. Hb S/Hb Haringey is a newly discovered sickle cell variant which seems to portray a mild/benign clinical phenotype of sickle cell disease.

Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy.

BACKGROUND AND PURPOSE: This study evaluated the use of an artificial intelligence platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants, while reducing the need for monitoring, have also placed pressure on patients to self-manage. Suboptimal adherence goes undetected as routine laboratory tests are not reliable indicators of adherence, placing patients at increased risk of stroke and bleeding. METHODS: A randomized, parallel-group, 12-week study was conducted in adults (n=28) with recently diagnosed ischemic stroke receiving any anticoagulation. Patients were randomized to daily monitoring by the artificial intelligence platform (intervention) or to no daily monitoring (control). The artificial intelligence application visually identified the patient, the medication, and the confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. RESULTS: For all patients (n=28), mean (SD) age was 57 years (13.2 years) and 53.6% were women. Mean (SD) cumulative adherence based on the artificial intelligence platform was 90.5% (7.5%). Plasma drug concentration levels indicated that adherence was 100% (15 of 15) and 50% (6 of 12) in the intervention and control groups, respectively. CONCLUSIONS: Patients, some with little experience using a smartphone, successfully used the technology and demonstrated a 50% improvement in adherence based on plasma drug concentration levels. For patients receiving direct oral anticoagulants, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on direct oral anticoagulant therapy. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT02599259.

Performance evaluation of heparin-induced platelet aggregation vs serotonin release assay.

OBJECTIVES: Heparin-induced thrombocytopenia (HIT) is a rare but life-threatening condition that requires rapid diagnosis for proper management. Laboratory testing should only be performed on patients with intermediate- or high-risk pretest probability. The platelet factor 4 (PF4) enzyme-linked immunosorbent assay (ELISA) is the screening test that should be confirmed by higher specificity testing such as the heparin-induced platelet aggregation (HIPA) or the serotonin release assay (SRA). This study aims to evaluate the performance of the HIPA in comparison to the SRA, establish cutoffs of the PF4 ELISA to predict positivity for HIPA/SRA, and study the mortality rate between patients with suspected HIT confirmed as HIT positive vs negative. METHODS: All positive PF4 ELISA cases with confirmatory HIPA and SRA testing were included. As the SRA was considered the gold standard, the HIPA performance was evaluated in comparison to SRA before and after the implementation of a new standardized interpretation guide in 2022. The mortality of these cases was also documented by chart reviews. RESULTS: In total, 232 cases with positive or indeterminate anti-PF4 IgG ELISA had confirmatory testing with HIPA and SRA. The sensitivity of the HIPA improved from 55.4% in 2018 to 2021 to 73.8% in 2022. The specificity remained similarly high in 2018 to 2021 vs 2022 (94.9% vs 87.5%). The negative predictive value (NPV) improved in 2022. The PF4 optical density cutoff to predict the positivity of SRA was 0.85 vs 1.47 to predict the positivity of HIPA but decreased to 0.83 when combining HIPA and/or SRA. There was no significant difference in mortality between patients with suspected HIT confirmed positive vs negative. CONCLUSIONS: Although the HIPA has a lower sensitivity than the SRA, the new standardized interpretation guide improved its sensitivity and NPV in 2022. Future improvements are needed to use the HIPA as a stand-alone confirmatory test with the goal to shorten hospital length of stay and expedite proper anticoagulation management.

Prothrombotic autoantibodies targeting platelet factor 4/polyanion are associated with pediatric cerebral malaria.

BACKGROUNDFeatures of consumptive coagulopathy and thromboinflammation are prominent in cerebral malaria (CM). We hypothesized that thrombogenic autoantibodies contribute to a procoagulant state in CM.METHODSPlasma from children with uncomplicated malaria (UM) (n = 124) and CM (n = 136) was analyzed by ELISA for a panel of 8 autoantibodies including anti-platelet factor 4/polyanion (anti-PF4/P), anti-phospholipid, anti-phosphatidylserine, anti-myeloperoxidase, anti-proteinase 3, anti-dsDNA, anti-ß-2-glycoprotein I, and anti-cardiolipin. Plasma samples from individuals with nonmalarial coma (NMC) (n = 49) and healthy controls (HCs) (n = 56) were assayed for comparison. Associations with clinical and immune biomarkers were determined using univariate and logistic regression analyses.RESULTSMedian anti-PF4/P and anti-PS IgG levels were elevated in individuals with malaria infection relative to levels in HCs (P < 0.001) and patients with NMC (PF4/P: P < 0.001). Anti-PF4/P IgG levels were elevated in children with CM (median = 0.27, IQR: 0.19-0.41) compared with those with UM (median = 0.19, IQR: 0.14-0.22, P < 0.0001). Anti-PS IgG levels did not differ between patients with UM and those with CM (P = 0.39). When patients with CM were stratified by malaria retinopathy (Ret) status, the levels of anti-PF4/P IgG correlated negatively with the peripheral platelet count in patients with Ret+ CM (Spearman's rho [Rs] = 0.201, P = 0.04) and associated positively with mortality (OR = 15.2, 95% CI: 1.02-275, P = 0.048). Plasma from patients with CM induced greater platelet activation in an ex vivo assay relative to plasma from patients with UM (P = 0.02), and the observed platelet activation was associated with anti-PF4/P IgG levels (Rs= 0.293, P = 0.035).CONCLUSIONSThrombosis mediated by elevated anti-PF4/P autoantibodies may be one mechanism contributing to the clinical complications of CM.

Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions.

Artificial Intelligence and machine-learning (ML) studies are increasingly populating the life science space and some have also started to integrate certain clinical decision support tasks. However, most of the activities within this space understandably remain within the investigational domain and are not yet ready for broad use in healthcare. In short, artificial intelligence/ML is still in an infancy stage within the healthcare arena, and we are nowhere near reaching its full potential. Various factors have contributed to this slow adoption rate within healthcare, which include but are not limited to data accessibility and integrity issues, paucity of specialized data science personnel, certain regulatory measures, and various voids within the ML operational platform domain. However, these obstacles and voids have also introduced us to certain opportunities to better understand this arena as we fully embark on this new journey, which undoubtedly will become a major part of our future patient care activities. Considering the aforementioned needs, this review will be concentrating on various ML studies within the coagulation and hemostasis space to better understand their shared study needs, findings, and limitations. However, the ML needs within this subspecialty of medicine are not unique and most of these needs, voids, and limitations also apply to the other medical disciplines. Therefore, this review will not only concentrate on introducing the audience to ML concepts and ML study design elements but also on where the future within this arena in medicine is leading us.

Identifying Neutrophil Extracellular Traps (NETs) in Blood Samples Using Peripheral Smear Autoanalyzers.

Neutrophil Extracellular Traps (NETs) are large neutrophil-derived structures composed of decondensed chromatin, cytosolic, and granule proteins. NETs play an important role in fighting infection, inflammation, thrombosis, and tumor progression processes, yet their fast and reliable identification has been challenging. Smudge cells (SCs) are a subcategory of white cells identified by CellaVision®, a hematology autoanalyzer routinely used in clinical practice that uses digital imaging to generate "manual" differentials of peripheral blood smears. We hypothesize that a proportion of cells identified in the SC category by CellaVision® Hematology Autoanalyzers are actually NETs. We demonstrate that NET-like SCs are not present in normal blood samples, nor are they an artifact of smear preparation. NET-like SCs stain positive for neutrophil markers such as myeloperoxidase, leukocyte alkaline phosphatase, and neutrophil elastase. On flow cytometry, cells from samples with high percent NET-like SCs that are positive for surface DNA are also positive for CD45, myeloperoxidase and markers of neutrophil activation and CD66b. Samples with NET-like SCs have a strong side fluorescent (SFL) signal on the white count and nucleated red cells (WNR) scattergram, representing cells with high nucleic acid content. When compared to patients with low percent SCs, those with a high percentage of SCs have a significantly higher incidence of documented bacterial and viral infections. The current methodology of NET identification is time-consuming, complicated, and cumbersome. In this study, we present data supporting identification of NETs by CellaVision®, allowing for easy, fast, cost-effective, and high throughput identification of NETs that is available in real time and may serve as a positive marker for a bacterial or viral infections.

Indeterminate serotonin release assays are associated with a high mortality rate.

Background: The serotonin release assay (SRA) is considered the gold standard for diagnosis of heparin-induced thrombocytopenia (HIT). Although the SRA holds high sensitivity and specificity when results are definitive, up to 10% of samples from patients with suspected HIT yield "indeterminate" results. Objectives: We aimed to study the clinical course of patients with indeterminate results. Methods: We conducted a cohort analysis of 2056 patients that underwent SRA testing. Results: Of 2056 total patients, 152 (7.4%) had indeterminate assays. The prevalence of thrombocytopenia <50,000 × 106 was higher in patients with an indeterminate or positive SRA, compared with a negative SRA (39.5% and 40.0% vs. 27.5%, p < 4.0 × 10-4). Patients with an indeterminate SRA were more likely to have been treated in the intensive care unit than patients with a positive SRA (93.3% vs. 73.7%, p = 0.03). The mean thrombocytopenia, timing of platelet count fall, thrombosis or other sequelae, and other causes for thrombocytopenia score in patients with indeterminate SRA was 2.9, corresponding to a HIT probability of <5%. Of 152 patients, 128 (78.9%) had heparin-PF4 optical densities (ODs) below 0.60 OD, whereas four patients (2.6%) had ODs above 2.00 OD. Inpatient mortality was significant in patients with indeterminate SRAs compared with positive or negative SRA (49.3% vs. 21.1% and 27.2%, p < 2.4 × 10-10). Conclusions: Our data suggest that an indeterminate SRA may signal an in vivo platelet activation process that is not related to heparin but is associated with increased mortality.

Outcomes With Direct and Indirect Thrombin Inhibition During Extracorporeal Membrane Oxygenation for COVID-19.

Anticoagulation during extracorporeal membrane oxygenation (ECMO) for Coronovirus Disease 2019 (COVID-19) can be performed by direct or indirect thrombin inhibitors but differences in outcomes with these agents are uncertain. A retrospective, multicenter study was conducted. All consecutive adult patients with COVID-19 placed on ECMO between March 1, 2020 and April 30, 2021 in participating centers, were included. Patients were divided in groups receiving either a direct thrombin inhibitor (DTI) or an indirect thrombin inhibitor such as unfractionated heparin (UFH). Overall, 455 patients with COVID-19 from 17 centers were placed on ECMO during the study period. Forty-four patients did not receive anticoagulation. Of the remaining 411 patients, DTI was used in 160 (39%) whereas 251 (61%) received UFH. At 90-days, in-hospital mortality was 50% (DTI) and 61% (UFH), adjusted hazard ratio: 0.81, 95% confidence interval (CI): 0.49-1.32. Deep vein thrombosis [adjusted odds ratio (aOR): 2.60, 95% CI: 0.90-6.65], ischemic (aOR: 1.58, 95% CI: 0.18-14.0), and hemorrhagic (aOR:1.22, 95% CI: 0.39-3.87) stroke were similar with DTI in comparison to UFH. Bleeding requiring transfusion was lower in patients receiving DTI (aOR: 0.40, 95% CI: 0.18-0.87). Anticoagulants that directly inhibit thrombin are associated with similar in-hospital mortality, stroke, and venous thrombosis and do not confer a higher risk of clinical bleeding in comparison to conventional heparin during ECMO for COVID-19.

Silencing VEGFR-2 Hampers Odontoblastic Differentiation of Dental Pulp Stem Cells.

Dental pulp stem cells (DPSCs) are a source of postnatal stem cells essential for maintenance and regeneration of dentin and pulp tissues. Previous in vivo transplantation studies have shown that DPSCs are able to give rise to odontoblast-like cells, form dentin/pulp-like structures, and induce blood vessel formation. Importantly, dentin formation is closely associated to blood vessels. We have previously demonstrated that DPSC-induced angiogenesis is VEGFR-2-dependent. VEGFR-2 may play an important role in odontoblast differentiation of DPSCs, tooth formation and regeneration. Nevertheless, the role of VEGFR-2 signaling in odontoblast differentiation of DPSCs is still not well understood. Thus, in this study we aimed to determine the role of VEGFR-2 in odontoblast differentiation of DPSCs by knocking down the expression of VEGFR-2 in DPSCs and studying their odontoblast differentiation capacity in vitro and in vivo. Isolation and characterization of murine DPSCs was performed as previously described. DPSCs were induced by VEGFR-2 shRNA viral vectors transfection (MOI = 10:1) to silence the expression of VEGFR-2. The GFP+ expression in CopGFP DPSCs was used as a surrogate to measure the efficiency of transfection and verification that the viral vector does not affect the expression of VEGFR-2. The efficiency of viral transfection was shown by significant reduction in the levels of VEGFR-2 based on the Q-RT-PCR and immunofluorescence in VEGFR-2 knockdown DPSCs, compared to normal DPSCs. VEGFR-2 shRNA DPSCs expressed not only very low level of VEGFR-2, but also that of its ligand, VEGF-A, compared to CopGFP DPSCs in both transcriptional and translational levels. In vitro differentiation of DPSCs in osteo-odontogenic media supplemented with BMP-2 (100 ng/ml) for 21 days demonstrated that CopGFP DPSCs, but not VEGFR-2 shRNA DPSCs, were positive for alkaline phosphatase (ALP) staining and formed mineralized nodules demonstrated by positive Alizarin Red S staining. The expression levels of dentin matrix proteins, dentin matrix protein-1 (Dmp1), dentin sialoprotein (Dspp), and bone sialoprotein (Bsp), were also up-regulated in differentiated CopGFP DPSCs, compared to those in VEGFR-2 shRNA DPSCs, suggesting an impairment of odontoblast differentiation in VEGFR-2 shRNA DPSCs. In vivo subcutaneous transplantation of DPSCs with hydroxyapatite (HAp/TCP) for 5 weeks demonstrated that CopGFP DPSCs were able to differentiate into elongated and polarized odontoblast-like cells forming loose connective tissue resembling pulp-like structures with abundant blood vessels, as demonstrated by H&E, Alizarin Red S, and dentin matrix staining. On the other hand, in VEGFR-2 shRNA DPSC transplants, odontoblast-like cells were not observed. Collagen fibers were seen in replacement of dentin/pulp-like structures. These results indicate that VEGFR-2 may play an important role in dentin regeneration and highlight the potential of VEGFR-2 modulation to enhance dentin regeneration and tissue engineering as a promising clinical application.

Correlation of Coagulation Parameters With Clinical Outcomes During the Coronavirus-19 Surge in New York: Observational Cohort.

IMPORTANCE: COVID-19 has caused a worldwide illness and New York became the epicenter of COVID-19 in the United States from Mid-March to May 2020. OBJECTIVE: To investigate the coagulopathic presentation of COVID and its natural course during the early stages of the COVID-19 surge in New York. To investigate whether hematologic and coagulation parameters can be used to assess illness severity and death. DESIGN: Retrospective case study of positive COVID inpatients between March 20, 2020-March 31, 2020. SETTING: Montefiore Health System main hospital, Moses, a large tertiary care center in the Bronx. PARTICIPANTS: Adult inpatients with positive COVID tests hospitalized at MHS. EXPOSURE FOR OBSERVATIONAL STUDIES: Datasets of participants were queried for demographic (age, sex, socioeconomic status, and self-reported race and/or ethnicity), clinical and laboratory data. MAIN OUTCOME AND MEASURES: Relationship and predictive value of measured parameters to mortality and illness severity. RESULTS: Of the 225 in this case review, 75 died during hospitalization while 150 were discharged home. Only the admission PT, absolute neutrophil count (ANC) and first D-Dimer could significantly differentiate those who were discharged alive and those who died. Logistic regression analysis shows increased odds ratio for mortality by first D-Dimer within 48 hrs. of admission. The optimal cut-point for the initial D-Dimer to predict mortality was found to be 2.1 µg/mL. 15% of discharged patients required readmission and more than a third of readmitted patients died (5% of all initially discharged). CONCLUSION: We describe here a comprehensive assessment of hematologic and coagulation parameters in COVID-19 and examine the relationship of these to mortality. We demonstrate that both initial and maximum D-Dimer values are biomarkers that can be used for survival assessments. Furthermore, D-Dimer may be useful to follow up discharged patients.

Mater Artium Necessitas: The Birth of a COVID-19 Command Center.

In February of 2020, New York City was unprepared for the COVID-19 pandemic. Cases of SARS-CoV-2 infection appeared and spread rapidly. Hospitals had to repurpose staff and establish diagnostic testing for this new viral infection. In the background of the usual respiratory pathogen testing performed in the clinical laboratory, SARS-CoV-2 testing at the Montefiore Medical System grew exponentially, from none to hundreds per day within the first week of testing. The job of appropriately routing SARS-CoV-2 viral specimens became overwhelming. Additional staff was required to triage these specimens to multiple in-house testing platforms as well as external reference laboratories. Since medical school classes and many research laboratories shut down at the Albert Einstein College of Medicine and students were eager to help fight the pandemic, we seized the opportunity to engage and train senior MD-PhD students to assist in triaging specimens. This volunteer force enabled us to establish the "Pathology Command Center," staffed by these students as well as residents and furloughed dental associates. The Pathology Command Center staff were tasked with the accessioning and routing of specimens, answering questions from clinical teams, and updating ever evolving protocols developed in collaboration with a team of Infectious Disease clinicians. Many lessons were learned during this process, including how best to restructure an accessioning department and how to properly onboard students and repurpose staff while establishing safeguards for their well-being during these unprecedented times. In this article, we share some of our challenges, successes, and what we ultimately learned as an organization.

A Histoplasma capsulatum Lipid Metabolic Map Identifies Antifungal Targets.

Lipids play a fundamental role in fungal cell biology, being essential cell membrane components and major targets of antifungal drugs. A deeper knowledge of lipid metabolism is key for developing new drugs and a better understanding of fungal pathogenesis. Here, we built a comprehensive map of the Histoplasma capsulatum lipid metabolic pathway by incorporating proteomic and lipidomic analyses. We performed genetic complementation and overexpression of H. capsulatum genes in Saccharomyces cerevisiae to validate reactions identified in the map and to determine enzymes responsible for catalyzing orphan reactions. The map led to the identification of both the fatty acid desaturation and the sphingolipid biosynthesis pathways as targets for drug development. We found that the sphingolipid biosynthesis inhibitor myriocin, the fatty acid desaturase inhibitor thiocarlide, and the fatty acid analog 10-thiastearic acid inhibit H. capsulatum growth in nanomolar to low-micromolar concentrations. These compounds also reduced the intracellular infection in an alveolar macrophage cell line. Overall, this lipid metabolic map revealed pathways that can be targeted for drug development. IMPORTANCE It is estimated that 150 people die per hour due to the insufficient therapeutic treatments to combat fungal infections. A major hurdle to developing antifungal therapies is the scarce knowledge on the fungal metabolic pathways and mechanisms of virulence. In this context, fungal lipid metabolism is an excellent candidate for developing drugs due to its essential roles in cellular scaffolds, energy storage, and signaling transductors. Here, we provide a detailed map of Histoplasma capsulatum lipid metabolism. The map revealed points of this fungus lipid metabolism that can be targeted for developing antifungal drugs.

Anticoagulation in COVID-19: Effect of Enoxaparin, Heparin, and Apixaban on Mortality.

BACKGROUND: Mortality in coronavirus disease of 2019 (COVID-19) is associated with increases in prothrombotic parameters, particularly D-dimer levels. Anticoagulation has been proposed as therapy to decrease mortality, often adjusted for illness severity. OBJECTIVE: We wanted to investigate whether anticoagulation improves survival in COVID-19 and if this improvement in survival is associated with disease severity. METHODS: This is a cohort study simulating an intention-to-treat clinical trial, by analyzing the effect on mortality of anticoagulation therapy chosen in the first 48 hours of hospitalization. We analyzed 3,625 COVID-19+ inpatients, controlling for age, gender, glomerular filtration rate, oxygen saturation, ventilation requirement, intensive care unit admission, and time period, all determined during the first 48 hours. RESULTS: Adjusted logistic regression analyses demonstrated a significant decrease in mortality with prophylactic use of apixaban (odds ratio [OR] 0.46, p = 0.001) and enoxaparin (OR = 0.49, p = 0.001). Therapeutic apixaban was also associated with decreased mortality (OR 0.57, p = 0.006) but was not more beneficial than prophylactic use when analyzed over the entire cohort or within D-dimer stratified categories. Higher D-dimer levels were associated with increased mortality (p < 0.0001). When adjusted for these same comorbidities within D-dimer strata, patients with D-dimer levels < 1 µg/mL did not appear to benefit from anticoagulation while patients with D-dimer levels > 10 µg/mL derived the most benefit. There was no increase in transfusion requirement with any of the anticoagulants used. CONCLUSION: We conclude that COVID-19+ patients with moderate or severe illness benefit from anticoagulation and that apixaban has similar efficacy to enoxaparin in decreasing mortality in this disease.

Hemolysis and Nonhemorrhagic Stroke During Venoarterial Extracorporeal Membrane Oxygenation.

BACKGROUND: Hemolysis, even at low levels, activates platelets to create a prothrombotic state and is common during mechanical circulatory support. We examined the association of low-level hemolysis (LLH) and nonhemorrhagic stroke during venoarterial extracorporeal membrane oxygenation (VA ECMO) support. METHODS: A single-center retrospective review of all adult patients placed on VA ECMO from January 2012 to September 2017 was conducted. To determine the association between LLH and nonhemorrhagic stroke, patients were categorized as those with and without LLH. LLH was defined by 48-hour plasma free hemoglobin (PFHb) of 11 to 50 mg/dL after VA ECMO implantation. RESULTS: Of 201 patients who underwent VA ECMO placement, 150 (75%) met inclusion criteria and comprised the study population. They were 55 ± 14 years of age and 50 (33%) were women. Sixty-two (41%) patients had LLH. Patients with LLH had a higher likelihood of incident nonhemorrhagic stroke during VA ECMO support (20 [32%] versus 4 [5%]; adjusted hazard ratio [HR], 7.6; 95% confidence interval [CI], 2.2 to 25.9; p = 0.001). The severity of LLH was associated with an incrementally higher likelihood of a nonhemorrhagic stroke (PFHb 26 to 50 mg/dL: HR, 11.3; 95% CI, 3.6 to 35.1; p = 0.001; PFHb 11 to 25 mg/dL: HR, 4.4; 95% CI, 1.36 to 14.85; p = 0.014) in comparison with no LLH. Those with LLH had a 2-fold greater increase in mean platelet volume after VA ECMO placement (0.98 ± 1.1 fL versus 0.49 ± 0.96 fL; p = 0.03). Patients with a nonhemorrhagic stroke had a higher operative mortality (20 [83%] versus 57 [45%]; adjusted HR, 3.1; 95% CI, 1.8 to 5.3; p < 0.001). CONCLUSIONS: Hemolysis at low levels during VA ECMO support is associated with subsequent nonhemorrhagic stroke.

Speed Reduction Does Not Restore High Molecular Weight von Willebrand Multimers During HeartMate II Support: An In Vivo Study.

Acquired von Willebrand Syndrome (AVWS) in patients undergoing continuous-flow left ventricular assist device support is due to the loss of von Willebrand factor (vWF) high molecular weight multimers (HMWMs) by shear-mediated mechanisms. We investigated whether reducing speed in vivo would mitigate the shear effect. In outpatients (n = 6) with a HeartMate II, pump speed was decreased to 8,000 rpm for 6 hours. At baseline (9,140 ± 189 rpm), patients had an AVWS as evidenced by low vWF activity:antigen ratios (0.58 ± 0.13, normal >0.7) and reduced HMWMs. After 6 hours, there was no significant change in either the vWF activity:antigen ratio or the HMWMs. Decreasing pump speed does not ameliorate AVWS.