Prevention of venous thromboembolism in cancer patients: current approaches and opportunities for improvement
AbstractVenous thromboembolism (VTE), a common complication in patients with cancer, is associated with increased risk of morbidity, mortality, and recurrent VTE. Risk factors for VTE in cancer patients include the type and stage of cancer, comorbidities, age, major surgery, and active chemotherapy. Evidence-based guidelines for thromboprophylaxis in cancer patients have been published: the National Comprehensive Cancer Network and American Society for Clinical Oncology guidelines recommend thromboprophylaxis for hospitalized cancer patients, while the American College of Chest Physician guidelines recommend thromboprophylaxis for surgical patients with cancer and bedridden cancer patients with an acute medical illness. Guidelines do not generally recommend routine thromboprophylaxis in ambulatory patients during chemotherapy, but there is evidence that some of these patients are at risk of VTE; some may be at higher risk while on active chemotherapy. Approaches are needed to identify those patients most likely to benefit from thromboprophylaxis, and, to this end, a risk assessment model has been developed and validated. Despite the benefits, many at-risk patients do not receive any thromboprophylaxis, or receive prophylaxis that is not compliant with guideline recommendations. Quality improvement initiatives have been developed by the Centers for Medicare and Medicaid Services, National Quality Forum, and Joint Commission to encourage closure of the gap between guideline recommendations and clinical practice for prevention, diagnosis, and treatment of VTE in hospitalized patients. Health-care institutions and providers need to take seriously the burden of VTE, improve prophylaxis rates in patients with cancer, and address the need for prophylaxis across the patient continuum.
FULL TEXT: 178
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2011 Alpesh N. Amin, Steven B. Deitelzweig
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.