Clinical prediction of deep vein thrombosis in patients with leg symptoms

Symptoms and clinical signs individually are inaccurate for the diagnosis o f DVT. However, when assessing patients with leg symptoms, clinicians have access to additional information, such as whether or not DVT risk factors a re present that could improve the accuracy of clinical judgment. The purpos e of this study was to identify which clinical variables best predict DVT, and to use these variables to create a clinical prediction index for DVT. W e studied 271 university hospital patients with a first episode of symptoma tic, clinically suspected DVT. The prevalence of DVT was 27%, of which 71% were proximal. At baseline, information was collected on demographic featur es, comorbidity, and symptoms and signs. A Bayesian model selection strateg y was used to estimate the logistic regression model that best predicted DV T. Male sex [OR = 2.8 (1.5, 5.1)], orthopedic surgery [OR = 5.4 (2.2, 13.6) ], warmth [OR = 2.1 (1.2, 3.9)] and superficial venous dilation on exam [OR = 2.9 (1.4, 5.7)] were independent predictors of DVT. Using the model, a c linical prediction index that categorized patients into different levels of DVT risk was created, and was useful in a theoretical strategy aimed to li mit the need for contrast venography in patients with suspected DVT, such t hat 96% of study patients could have avoided contrast venography. This inde x should be evaluated prospectively in other patient populations.