• M Todd Greene, Alex C Spyropoulos, Vineet Chopra, Paul J Grant, Scott Kaatz, Steven J Bernstein, and Scott A Flanders.
• The Michigan Hospital Medicine Safety Consortium Data Coordinating Center, Ann Arbor; Department of Internal Medicine, University of Michigan Medical School, Ann Arbor. Electronic address: mtgreene@med.umich.edu.
• Am. J. Med. 2016 Sep 1; 129 (9): 1001.e9-1001.e18.

BackgroundPatients hospitalized for acute medical illness are at increased risk for venous thromboembolism. Although risk assessment is recommended and several at-admission risk assessment models have been developed, these have not been adequately derived or externally validated. Therefore, an optimal approach to evaluate venous thromboembolism risk in medical patients is not known.MethodsWe conducted an external validation study of existing venous thromboembolism risk assessment models using data collected on 63,548 hospitalized medical patients as part of the Michigan Hospital Medicine Safety (HMS) Consortium. For each patient, cumulative venous thromboembolism risk scores and risk categories were calculated. Cox regression models were used to quantify the association between venous thromboembolism events and assigned risk categories. Model discrimination was assessed using Harrell's C-index.ResultsVenous thromboembolism incidence in hospitalized medical patients is low (1%). Although existing risk assessment models demonstrate good calibration (hazard ratios for "at-risk" range 2.97-3.59), model discrimination is generally poor for all risk assessment models (C-index range 0.58-0.64).ConclusionsThe performance of several existing risk assessment models for predicting venous thromboembolism among acutely ill, hospitalized medical patients at admission is limited. Given the low venous thromboembolism incidence in this nonsurgical patient population, careful consideration of how best to utilize existing venous thromboembolism risk assessment models is necessary, and further development and validation of novel venous thromboembolism risk assessment models for this patient population may be warranted.Published by Elsevier Inc.

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