Search

Protein disulfide isomerase (PDI) is a thiol isomerase secreted by vascular cells that is required for thrombus formation. Quercetin flavonoids inhibit PDI activity and block platelet accumulation and fibrin generation at the site of a vascular injury in mouse models, but the clinical effect of targeting extracellular PDI in humans has not been studied. We conducted a multicenter phase II trial of sequential dosing cohorts to evaluate the efficacy of targeting PDI with isoquercetin to reduce hypercoagulability in cancer patients at high risk for thrombosis. Patients received isoquercetin at 500 mg (cohort A, n = 28) or 1000 mg (cohort B, n = 29) daily for 56 days, with laboratory assays performed at baseline and the end of the study, along with bilateral lower extremity compression ultrasound. The primary efficacy endpoint was a reduction in D-dimer, and the primary clinical endpoint included pulmonary embolism or proximal deep vein thrombosis. The administration of 1000 mg isoquercetin decreased D-dimer plasma concentrations by a median of -21.9% (P = 0.0002). There were no primary VTE events or major hemorrhages observed in either cohort. Isoquercetin increased PDI inhibitory activity in plasma (37.0% in cohort A, n = 25, P < 0.001; 73.3% in cohort B, n = 22, P < 0.001, respectively). Corroborating the antithrombotic efficacy, we also observed a significant decrease in platelet-dependent thrombin generation (cohort A median decrease -31.1%, P = 0.007; cohort B median decrease -57.2%, P = 0.004) and circulating soluble P selectin at the 1000 mg isoquercetin dose (median decrease -57.9%, P < 0.0001). Isoquercetin targets extracellular PDI and improves markers of coagulation in advanced cancer patients. Clinicaltrials.gov NCT02195232. Quercegen Pharmaceuticals; National Heart, Lung, and Blood Institute (NHLBI; U54HL112302, R35HL135775, and T32HL007917); and NHLBI Consortium Linking Oncology and Thrombosis (U01HL143365).

PurposeHospitalized patients with cancer are considered to be at high risk for venous thromboembolism (VTE). Despite strong recommendations in numerous clinical practice guidelines, retrospective studies have shown that pharmacologic thromboprophylaxis is underutilized in hospitalized patients with cancer.Patients and methodsWe conducted a prospective, cross-sectional study of hospitalized patients with cancer at five academic hospitals to determine prescription rates of thromboprophylaxis and factors influencing its use during hospitalization.ResultsA total of 775 patients with cancer were enrolled across five academic medical centers. Two hundred forty-seven patients (31.9%) had relative contraindications to pharmacologic prophylaxis. Accounting for contraindications to anticoagulation, the overall rate of pharmacologic thromboprophylaxis was 74.2% (95% CI, 70.4% to 78.0%; 392 of 528 patients). Among the patients with cancer without contraindications for anticoagulation, individuals hospitalized with nonhematologic malignancies were significantly more likely to receive pharmacologic thromboprophylaxis than those with hematologic malignancies (odds ratio [OR], 2.34; 95% CI, 1.43 to 3.82; P=.007). Patients with cancer admitted for cancer therapy were significantly less likely to receive pharmacologic thromboprophylaxis than those admitted for other reasons (OR, 0.37; 95% CI, 0.22 to 0.61; P

Background Direct oral anticoagulants (DOACs) have comparable efficacy with low-molecular-weight heparin (LMWH) for the treatment of cancer-associated venous thromboembolism (VTE). Whether there is a mortality benefit of DOACs compared with warfarin in the management of VTE in cancer is not established. Methods and findings Utilizing the United States' Surveillance, Epidemiology, and End Results (SEER)-Medicare linked databases from 2012 through 2016, we analyzed overall survival in individuals diagnosed with a primary gastric, colorectal, pancreas, lung, ovarian, or brain cancer and VTE who received a prescription of DOAC or warfarin within 30 days of VTE diagnosis. Patients were matched 1:2 (DOAC to warfarin) through exact matching for cancer stage and propensity score matching for age, cancer site, cancer stage, and time interval from cancer to VTE diagnosis. The analysis identified 4,274 patients who received a DOAC or warfarin for the treatment of VTE within 30 days of cancer diagnosis (1,348 in DOAC group and 2,926 in warfarin group). Patients were of median age 75 years and 56% female. Within the DOAC group, 1,188 (88%) received rivaroxaban, and 160 (12%) received apixaban. With a median follow-up of 41 months, warfarin was associated with a statistically significantly higher overall survival compared to DOACs (median overall survival 12.0 months [95% confidence interval (CI): 10.9 to 13.5] versus 9.9 months [95% CI: 8.4 to 11.2]; hazard ratio (HR) 0.85; 95% CI: 0.78 to 0.91; p < 0.001). Observed differences in survival were consistent across subgroups of cancer sites, cancer stages, and type of VTE. The study limitations include retrospective design with potential for unaccounted confounders along with issues of generalizability beyond the cancer diagnoses studied. Conclusions In this analysis of a population-based registry, warfarin was associated with prolonged overall survival compared to DOACs for treatment of cancer-associated VTE., Author(s): Adeel M. Khan 1, Thita Chiasakul 1,2, Robert Redd 3, Rushad Patell 1, Ellen P. McCarthy 4,5, Donna Neuberg 3, Jeffrey I. Zwicker 1,* Introduction Cancer-associated thromboembolism (CAT) is [...]

The goal of machine learning (ML) is to create informative signals and useful tasks by leveraging large datasets to derive computational algorithms. ML has the potential to revolutionize the healthcare industry by boosting productivity, enhancing safe and effective patient care, and lightening the load on clinicians. In addition to gaining mechanistic insights into cancer-associated thrombosis (CAT), ML can be used to improve patient outcomes, streamline healthcare delivery, and spur innovation. Our review paper delves into the present and potential applications of this cutting-edge technology, encompassing three areas: i) computer vision-assisted diagnosis of thromboembolism from radiology data; ii) case detection from electronic health records using natural language processing; iii) algorithms for CAT prediction and risk stratification. The availability of large, well-annotated, high-quality datasets, overfitting, limited generalizability, the risk of propagating inherent bias, and a lack of transparency among patients and clinicians are among the challenges that must be overcome in order to effectively develop ML in the health sector. To guarantee that this powerful instrument can be utilized to maximize innovation in CAT, clinicians can collaborate with stakeholders such as computer scientists, regulatory bodies, and patient groups. [ABSTRACT FROM AUTHOR]

Comprehensive protein analyses of plasma are made possible by high-throughput proteomic screens, which may help find new therapeutic targets and diagnostic biomarkers. Patients with cancer are frequently affected by venous thromboembolism (VTE). The limited predictive accuracy of current VTE risk assessment tools highlights the need for new, more targeted biomarkers. Although coagulation biomarkers for the diagnosis, prognosis, and treatment of VTE have been investigated, none of them have the necessary clinical validation or diagnostic accuracy. Proteomics holds the potential to uncover new biomarkers and thrombotic pathways that impact the risk of thrombosis. This review explores the fundamental methods used in proteomics and focuses on particular biomarkers found in VTE and cancer-associated thrombosis. [ABSTRACT FROM AUTHOR]

Background: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. Aims: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. Methods: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t‐test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). Results: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI‐based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74–0.85) versus 0.61 (95% CI: 0.54–0.68), respectively (p <.001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. Conclusion: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models. [ABSTRACT FROM AUTHOR]