Alok A Khorana1, Gregory C Connolly. 1. James P. Wilmot Cancer Center, and Department of Medicine, University of Rochester, Rochester, NY, USA. alok_khorana@URMC.rochester.edu
Abstract
PURPOSE: Patients with cancer are increasingly at risk for venous thromboembolism (VTE). Rates of VTE, however, vary markedly among patients with cancer. DESIGN: This review focuses on recent data derived from population-based, hospital-based, and outpatient cohort studies of patients with cancer that have identified multiple clinical risk factors as well as candidate laboratory biomarkers predictive of VTE. RESULTS: Clinical risk factors for cancer-associated VTE include primary tumor site, stage, initial period after diagnosis, presence and number of comorbidities, and treatment modalities including systemic chemotherapy, antiangiogenic therapy, and hospitalization. Candidate predictive biomarkers include elevated platelet or leukocyte counts, tissue factor, soluble P-selectin, and D-dimer. A recently validated risk model, incorporating some of these factors, can help differentiate patients at high or low risk for developing VTE while receiving chemotherapy. CONCLUSION: Identifying patients with cancer who are most at risk for VTE is essential to better target thromboprophylaxis, with the eventual goal of reducing the burden as well as the consequences of VTE for patients with cancer.
PURPOSE:Patients with cancer are increasingly at risk for venous thromboembolism (VTE). Rates of VTE, however, vary markedly among patients with cancer. DESIGN: This review focuses on recent data derived from population-based, hospital-based, and outpatient cohort studies of patients with cancer that have identified multiple clinical risk factors as well as candidate laboratory biomarkers predictive of VTE. RESULTS: Clinical risk factors for cancer-associated VTE include primary tumor site, stage, initial period after diagnosis, presence and number of comorbidities, and treatment modalities including systemic chemotherapy, antiangiogenic therapy, and hospitalization. Candidate predictive biomarkers include elevated platelet or leukocyte counts, tissue factor, soluble P-selectin, and D-dimer. A recently validated risk model, incorporating some of these factors, can help differentiate patients at high or low risk for developing VTE while receiving chemotherapy. CONCLUSION: Identifying patients with cancer who are most at risk for VTE is essential to better target thromboprophylaxis, with the eventual goal of reducing the burden as well as the consequences of VTE for patients with cancer.
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