Incidence of Heparin Induced Thrombocytopenia (HIT) in patients with severe COVID-19

Introduction To prevent and treat thrombotic complications in patients hospitalized with severe COVID-19 infection, anticoagulation treatments primarily with heparin and low molecular weight heparin have been recommended. Heparin-induced thrombocytopenia (HIT) is a rare but conceivably fatal reaction to heparin that is characterized by a sudden drop in platelet count accompanied by new onset of thrombosis 4-10 days after heparin exposure. The purpose of this retrospective study was to investigate the prevalence of thrombocytopenia and HIT in hospitalized COVID-19 patients, as well as their association with mortality. Methods 3,672 plasma samples were collected from patients admitted to the first wave of COVID-19 in our institution at New York City (March to May 2020). All patients admitted with a platelet count of less than 150 k/ul were assigned to the thrombocytopenic group. In addition, two groups with similar demographics and normal platelet counts were randomly selected based on discharge outcome: alive vs. deceased (n= 88 per group). PF4 IgG Elisa and heparin neutralization were carried out in accordance with the manufacturer's instructions. A positive HIT result required an optical density (OD) greater than 0.4 and heparin neutralization greater than 50%. Statistical analysis was done in R studio (V.1.4.1717) to analyze demographics (age, gender, ethnicity), initial laboratory data, anticoagulation on admission, and thrombosis. Results Only 86 of the 3,672 (2.3%) patients admitted had thrombocytopenia. Only 1 of the 86 patients tested positive for HIT (1.1%). 4 cases of the non-survivors (4.5%) tested positive for HIT compared to none of the survivors in the two groups with normal platelet counts. One of these 4 cases had a history of thrombosis (DVT). Interestingly, the PF4 Elisa ODs in non-survivors were significantly higher than in survivors (0.09 vs. 0.06, p-value< 0.001). Although the platelet count did not differ significantly between the two groups, the mean platelet volume (MPV) on admission and its maximum peak during hospitalization were significantly higher in non-survivors than in survivors. Conclusions We only found HIT positive cases among non-survivors, implying that HIT is associated with COVID severity. The incidence of HIT in severe COVID-19 patients appears to be higher than the pre-COVID-19 historical rates of HIT in hospitalized patients (<1%). Although thrombocytopenia is relatively uncommon in COVID-19 patients, the MPV was significantly higher in non-survivors, suggesting that platelet activation and destruction may explain the higher rate of HIT in COVID-19.

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.

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.

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.

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.

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.

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.