Clinical Risk Factors and Predictors of Venous Thromboembolism Amount Hospitalized Cancer Patients

Supratik Rayamajhi, MD, FACP    

Principal Investigator: Supratik Rayamajhi, MD, FACP, Assistant Professor of Medicine, Associate Director of Internal Medicine Residency, Advanced Medicine Clerkship Director, Department of Medicine, College of Human Medicine, Michigan State University (MSU), Sparrow Hospitalist Staff member

Co-Investigators: Anas Al-Janadi MD, Associate Professor of Medicine, Chief and Fellowship Director, MSU Hematology-Oncology; Director, Breslin Cancer Center; Mukta Sharma, MD, Assistant Professor of Medicine, Division of General Internal Medicine, College of Human Medicine, Michigan State University; Prajwal Dhakal, MD, Resident, Internal Medicine Residency, Sparrow Health System; College of Human Medicine, Michigan State University; Joseph C. Gardiner PhD, University Distinguished Professor, Department of Epidemiology and Biostatistics, Michigan State University

Research Assistant: Shiva Shrotriya, MBBS, MPH, Adjunct Assistant Professor, College of Human Medicine, Michigan State University College of Human Medicine

Venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolism (PE), is a major public health burden. Approximately 20% of VTE occur in cancer patients called Cancer associated thrombosis (CAT) with up to 7-fold increased risk of VTE compared to non-cancer patients. CAT caused 3 times as many hospitalizations, increased inpatient and outpatient medical and prescription claims, and increased total health care costs per patient (USD 74,959 Vs USD 41,691; P < .0001) in comparison to non-cancer patients with VTE. The precise predictors/predictive model to estimate VTE risk among cancer patients are still inconclusive. There are inconsistencies among various risk factors in two popular models Viz. Ottawa score (looks at recurrent VTE in cancer patients ) and Khorana score (looks at chemotherapy-induced VTE in cancer patients).

Methods: Ours is a retrospective cohort study that aims to analyze demographic, clinical and laboratory variables among unique population of established CAT patients undergoing anticoagulation therapy at Sparrow Hospital. Our study focuses on recognizing the most consistent and reproducible variables associated with CAT. Identification of these parameters should help in devising precise predictive model for CAT.

Conclusion: Cancer patients who are at high risk for CAT (based on this predictive model) can be targeted with appropriate prophylactic measures thereby not only preventing hospitalization from CAT but also reducing the overall healthcare cost burden resulting from CAT.

Press Release