Characterization and Probability of Upper Extremity Deep Venous Thrombosis

• Published in: Annals of vascular surgery Vol. 18; no. 5; pp. 552 - 557
• Main Authors: Schmittling, Zachary C., McLafferty, Robert B., Bohannon, W. Todd, Ramsey, Don E., Hodgson, Kim J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.09.2004; Elsevier Limited
• Subjects: Arm - blood supply; Edema - diagnosis; Female; Humans; Male; Middle Aged; Multivariate Analysis; Retrospective Studies; Risk Factors; Ultrasonography; Venous Thrombosis - diagnosis; Venous Thrombosis - diagnostic imaging
• ISSN: 0890-5096; 1615-5947
• DOI: 10.1007/s10016-004-0079-5

The objective of this study was to characterize patient demographics, risk factors, and anatomic distribution of upper extremity deep venous thrombosis (UEDVT) to develop a probability model for diagnosis. A retrospective review of all patients who underwent color-flow duplex scanning (CDS) for clinically suspected acute UEDVT over a 5-year period was performed. Patient risk factors and clinical symptoms were evaluated as predictors. Technically adequate complete CDS of 177 upper extremities (UEs) of arms were reviewed. CDS scanning identified acute UE venous thrombosis in 53 (30%) of the arms examined with deep system involvement in 40 (23%). Of the UEs affected, the subclavian was involved in 64%, the axillary in 25%, the internal jugular in 32%, the brachial in 36%, the cephalic in 32%, and the basilic in 47%. Multivariate analysis identified limb tenderness (odds ratio 9.3), history of central venous catheterization (odds ratio 7.0), and malignancy (odds ratio 2.9) as positive predictors for UEDVT. Erythema (odds ratio 0.12) and suspected pulmonary embolism (odds ration 0.06) were identified as negative predictors. A predictive model was designed from these variables. The anatomic distribution of UEDVT obtained from this study is consistent with previous reviews. Potential positive and negative risk factors can be identified from which a predictive model can be designed. Use of this model can help focus clinical suspicion, improve color-flow duplex utilization, and provide timely treatment with anticoagulation.