Frequency of Thrombotic Risk Factors in Patients with Chronic Thromboembolic Pulmonary Hypertension and Acute Pulmonary Embolism: A Case-Control Study

Chronic thromboembolic pulmonary hypertension (CTEPH) is a serious complication of acute pulmonary embolism (PE) which remains underdiagnosed. A better understanding of risk factors for CTEPH would improve our ability to predict which PE survivors are at risk. Several medical conditions—including malignancy, splenectomy, thyroid hormone supplementation, the presence of an intravascular device, inflammatory bowel disease, osteomyelitis, and non-O blood group—have been associated with increased risk of CTEPH, primarily in studies comparing patients with CTEPH to individuals with non-thrombotic conditions. Because many of these conditions increase thrombosis risk, it remains unclear whether their association with CTEPH reflects a general effect on thrombosis risk, or a specific effect on the risk of developing CTEPH as an outcome of thrombosis. We performed a case-control study comparing the frequencies of these conditions in patients with CTEPH versus patients with acute PE who did not develop CTEPH. The conditions studied were equally frequent in the CTEPH and PE cohorts, although there was a trend towards an increased frequency of splenectomy and non-O blood group among the CTEPH cohort. Thus, other than the possible exceptions of splenectomy and non-O blood group, the investigated medical conditions do not appear likely to increase the risk of CTEPH as an outcome of acute PE, and thus are unlikely to be useful in predicting CTEPH risk among PE survivors.

Time-Varying Mortality Risk Suggests Increased Impact of Thrombosis in Hospitalized Covid-19 Patients

Treatment protocols, treatment availability, disease understanding, and viral characteristics have changed over the course of the Covid-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers have also changed. We add to the ongoing conversation regarding inflammation, hemostasis and vascular function in Covid-19 by performing a time-varying observational analysis of over 4000 patients hospitalized for Covid-19 in a New York City hospital system from March 2020 to August 2021 to elucidate the changing impact of thrombosis, inflammation, and other risk factors on in-hospital mortality. We find that the predictive power of biomarkers of thrombosis risk have increased over time, suggesting an opportunity for improved care by identifying and targeting therapies for patients with elevated thrombophilic propensity.

Contribution of the Elevated Thrombosis Risk of Males to the Excess Male Mortality Observed in COVID-19: An Observational Study

Background: The mortality rate of COVID-19 is elevated in males compared to females.Objective: Determine the extent that the elevated thrombotic risk in males relative to females contributes to excess COVID-19 mortality in males.Design: Observational study.Setting: Data sourced from electronic medical records from over 200 US hospital systems.Participants: 60,877 patients hospitalized with COVID-19.Exposure: Exposure variable: biological sex; key variable of interest: thrombosis.Main outcome measures: Primary outcome was COVID-19 mortality. We measured: 1) mortality rate of males relative to females, 2) rate of thrombotic diagnoses occurring during hospitalization for COVID-19 in both sexes, and 3) mortality rate when evidence of thrombosis was present.Results: The COVID-19 mortality rate of males was 29.9% higher than that of females. Males had a 35.8% higher rate of receiving a thrombotic diagnosis compared to females. The mortality rate of all patients with a thrombotic diagnosis was 40.0%— over twice that of COVID-19 patients without a thrombotic diagnosis (adjusted OR 2.50 [2.37 to 2.64], p-value < .001). When defining thrombosis as either a documented thrombotic diagnosis or a D-dimer level ≥ 3.0 μg/mL, 16.4% of the excess mortality in male patients could be explained by increased thrombotic risk. Conclusions and Relevance: Our findings suggest the higher COVID-19 mortality rate in males may be significantly accounted for by the elevated risk for thrombosis among males. Understanding the mechanisms that underlie increased male thrombotic risk may allow for the advancement of effective anticoagulation strategies that reduce COVID-19 mortality in males.

An Inpatient COVID-19 Prophylaxis Protocol and Its Outcomes: Adherence and Efficacy

Introduction: Patients with coronavirus disease 2019 (COVID-19) have an increased risk for venous thromboembolic events. Thrombotic events contribute to the morbidity and mortality associated with COVID-19 infection, and have prompted investigation into strategies for mitigating thrombosis risk in patients hospitalized with COVID-19 infection. Our team reviewed the charts of patients hospitalized with COVID-19 pneumonia at a tertiary hospital in metropolitan Phoenix Arizona between 2020-2021, to assess frequency and efficacy of utilizing a VTE prophylaxis algorithm designed to prevent thrombosis in patients infected with COVID-19. Methods: A total of 846 patients were retrospectively evaluated to determine if they were treated with guideline-appropriate anticoagulation while hospitalized with COVID-19, as well as if they developed venous or arterial thrombotic events, or major or minor bleeds. 317 patients were excluded for taking therapeutic anticoagulation prior to admission, or for having a COVID-19 diagnosis &gt;7 days after admission. Appropriate anticoagulation was determined by an institutionally designed COVID-19 thromboprophylaxis algorithm, based on platelet count, d-dimer, bleeding risk, and level of medical care required. Regimen options included: no anticoagulation, prophylactic enoxaparin (40 mg SQ daily) or heparin in the setting of kidney dysfunction, weight-based dosing of enoxaparin (40 mg SQ BID if BMI&gt;40), intermediate intensity enoxaparin without thrombus (30 mg BID if BMI&lt;40, or 40 mg BID if BMI&gt;40), and therapeutic anticoagulation (for example enoxaparin 1 mg/kg BID) with thrombus. Demographics: Demographic data and clinical characteristics were collected for 529 patients. Average age was 59 years old, and the majority were men (58.4%). Most patients were White (58.3%), followed by Hispanic (17.8%), or Native American (15.7%). Fewer patients had a normal BMI (21.3%; BMI 18.5 - 24.9) compared to those who were overweight (31.2%; BMI 25-29.9) or obese (43.1% BMI &gt; 30). Other comorbidities included Type 1 or Type 2 diabetes mellitus (N= 172, 32.5%), hypertension (N = 271, 51.2%), and hyperlipidemia (N = 176, 33.3%). Results: A total of 42 patients (8%), were diagnosed with a venous thrombosis during hospitalization. Patients admitted to the ICU were significantly more likely to have a thrombotic event of any type compared to non-ICU patients (21.6% to 5.7%; p &lt; 0.001). Specifically, critically ill patients had higher incidences of deep vein thrombosis (9.5% to 0.7%), pulmonary emboli (8.1% to 4.8%), and superficial thrombi (2.7% to 0.2%). Only 1.1% of patients (6/529) experienced any bleeding, of which 3 were classified as a major bleed. Discussion: Among patients hospitalized at our institution with COVID-19, the majority were anticoagulated appropriately according to the COVID-19 thromboprophylaxis algorithm. Overall incidence of thrombosis in the study population was 8%. A significantly higher percent of critically ill patients had thrombi, supporting reports of correlation between severity of illness and thrombosis risk. The two regimens of anticoagulation least adhered to were weight-based and intermediate-based dosing, likely reflecting a departure from the hospital's thromboprophylaxis regimens prior to COVID-19 pandemic. Further studies are needed to characterize whether identifiable risk factors correlate with the incidence of thrombosis, and whether treatment with lower than recommended doses of anticoagulation, based on the COVID-19 thromboprophylaxis algorithm, were associated with thrombosis. Disclosures No relevant conflicts of interest to declare.

A Novel Machine Learning-Derived Dynamic Scoring System Predicts Risk of Thrombosis in Polycythemia Vera (PV) Patients

Introduction: Thrombosis remains a leading cause of morbidity and early mortality in PV. The European LeukemiaNet (ELN) classifies patients (pts) at diagnosis as high-risk according to age ≥60 years (yr) and/ or prior thrombosis, but dynamic models predicting short-term risk of initial or recurrent thrombosis are unavailable. We utilized machine learning (ML) to develop a dynamic scoring system that predicts thrombosis in PV pts using the most important of 60 clinicopathologic features. Methods: A Random Forests ML model was trained to classify instances (3-month follow-up intervals of PV pts) as predictive or non-predictive of thrombosis, arterial or venous, in subsequent 3-6 months based on 60 features: 4 demographic, 11 history & physical, 13 treatments, 18 laboratory, and 14 pathology and molecular. The dataset was derived from Weill Cornell Medicine (WCM) Research Database Repository as previously described (Abu-Zeinah et al. Leukemia 2021) and split into training (75%) and testing (25%) sets. Hyperparameter tuning was performed to optimize model training. Synthetic minority oversampling technique (SMOTE) was implemented to reduce class imbalance since instances predictive of thrombosis were a minority. Missing data were imputed using multiple imputation by chained equations (MICE). The scoring system was developed based on ML-derived features of highest importance and confirmed by logistic regression multivariable analysis (MVA). Cumulative incidence (CI) of thrombosis was compared between risk groups using Fine-Gray model. External validation of the ML model and scoring system is underway using the Mount Sinai School of Medicine (MSSM) PV dataset. Statistical analyses and plots were performed in RStudio software v 1.4.1106. Results: 470 PV pts at WCM were included with baseline features shown in Fig 1A. During follow-up, 159 thromboses (88 venous, 71 arterial) occurred in 115 pts. CI of thrombosis was significantly higher shortly after diagnosis, as previously appreciated (Hulcrantz et al. Ann Intern Med. 2018), and following a thrombotic event (Fig 1B-C). Bilinear fitting to CI curves identified a 2-yr breakpoint that marked the transition from a high to a much lower long-term risk after diagnosis (incidence rate (IR), per year, of 4.4% vs 1%, respectively) and after thrombosis (IR of 9.7% vs 1.8%). Of the ML model's top 10 features, 5 that independently predicted thrombosis in MVA were selected for a clinically convenient scoring system to estimate thrombosis risk (Fig 1D-E). One point was assigned for each of age ≥60 yr, prior thrombosis, WBC ≥12 x 10 9/L, peri-diagnosis (&lt;2 yr from diagnosis), and peri-thrombosis (&lt;2 yr from last thrombosis). Using this scoring system, we found that high-risk (Hi) and intermediate-risk (Int) pts (score ≥2 and =1) were 6.5 and 2.3 times more likely to have thrombosis, respectively, than low-risk (Lo) pts (score = 0) (p&lt;0.001 and p=0.014). Probability of thrombosis was significantly different for Lo, Int, and Hi at 1 yr (0%, 1%, and 6%), 2 yrs (1%, 3%, and 10%), and 5 yr (2%, 9% and 21%) (Fig 1F & 1H). In contrast, ELN high-risk pts were only 2.2 times more likely to have thrombosis than ELN low-risk pts (Fig 1G-H). The concordance (C-index) of the ML-derived model (0.7± se 0.02) was higher than ELN (0.59 ± se 0.03). External validation using the MSSM PV data (Fig 1A) is ongoing. Discussion: We applied ML to our large PV-WCM dataset to identify most important clinicopathologic features predicting thrombosis. In contrast to linear models, ML has little penalty for increasing number of parameters tested and can easily accommodate high-dimensional data to improve predictions. Because "big data" is not routinely available to caregivers, we developed a simple, dynamic scoring system predicting the risk of thrombosis in PV based on 5 most important features identified by ML. This new and dynamic scoring system outperformed ELN stratification and may prove useful in guiding treatment and improving selection of pts for clinical trials aimed at preventing thrombosis in PV pts. Conclusion: The risk of thrombosis in PV pts is temporally non-linear and strongly influenced by proximity to diagnosis and recent thrombosis. A simple ML-derived dynamic scoring system is presented that better classifies pts into distinct Lo, Int, and Hi thrombosis risk groups based on age, prior thrombosis, WBC, peri-diagnosis, and peri-thrombosis. Figure 1 Figure 1. Disclosures Abu-Zeinah: PharmaEssentia: Consultancy. Silver: Abbvie: Consultancy; PharamEssentia: Consultancy, Speakers Bureau. Mascarenhas: Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; CTI Biopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Galecto: Consultancy; Geron: Consultancy; PharmaEssentia: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech/Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Prelude: Consultancy; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Merus: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Promedior: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Kartos: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Constellation: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forbius: Research Funding; Geron: Consultancy, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Scandura: MPN-RF (Foundation): Research Funding; CR&T (Foudation): Research Funding; European Leukemia net: Honoraria, Other: travel fees ; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Constellation: Research Funding.