Risk Assessment of Recurrence in Patients With Unprovoked Deep Vein Thrombosis or Pulmonary Embolism: The Vienna Prediction Model

Background— Predicting the risk of recurrent venous thromboembolism (VTE) in an individual patient is often not feasible. We aimed to develop a simple risk assessment model that improves prediction of the recurrence risk. Methods and Results— In a prospective cohort study, 929 patients with a first unprovoked VTE were followed up for a median of 43.3 months after discontinuation of anticoagulation. We excluded patients with a strong thrombophilic defect such as a natural inhibitor deficiency, the lupus anticoagulant, and homozygous or combined defects. A total of 176 patients (18.9%) had recurrent VTE. Preselected clinical and laboratory variables (age, sex, location of VTE, body mass index, factor V Leiden, prothrombin G20210A mutation, D-dimer, and in vitro thrombin generation) were analyzed in a Cox proportional hazards model, and those variables that were significantly associated with recurrence were used to compute risk scores. Male sex (hazard ratio versus female sex 1.90, 95% confidence interval 1.31 to 2.75), proximal deep vein thrombosis (hazard ratio versus distal 2.08, 95% confidence interval 1.16 to 3.74), pulmonary embolism (hazard ratio versus distal thrombosis 2.60, 95% confidence interval 1.49 to 4.53), and elevated levels of D-dimer (hazard ratio per doubling 1.27, 95% confidence interval 1.08 to 1.51) were related to a higher recurrence risk. Using these variables, we developed a nomogram that can be used to calculate risk scores and to estimate the cumulative probability of recurrence in an individual patient. The model was cross validated, and patients were assigned to different risk categories based on their risk score. Recurrence rates corresponded well with the different risk categories. Conclusions— By use of a simple scoring system, the assessment of the recurrence risk in patients with a first unprovoked VTE and without strong thrombophilic defects can be improved.

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