Predicting the risk of recurrent venous thromboembolism in patients with cancer: A prospective cohort study
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Published on: September 27, 2024
Nick van Es1, Martha Louzada2, Marc Carrier3
1Department of Vascular Medicine, Academic Medical Center, Amsterdam, The Netherlands.
Background:
The risk of recurrent venous thromboembolism (VTE) in cancer patients despite anticoagulant therapy is high. Clinical factors and pro-coagulant markers may identify high-risk patients and guide decisions about intensifying anticoagulation therapy.
Aims:
To evaluate whether serial measurements of pro-coagulant markers can identify patients at high risk of recurrent VTE.
Methods:
In this multicenter, prospective cohort study, patients with active cancer and acute deep vein thrombosis or pulmonary embolism were enrolled. Patients received standard low-molecular-weight heparin therapy and were followed for 6 months. D-dimer and soluble P-selectin levels were measured at baseline and 1, 4, 5, 12, and 24 weeks post treatment initiation. The association between recurrent VTE and a previously developed risk score, baseline values of the biomarkers, and individual relative changes from baseline were assessed.
Results:
We enrolled 117 cancer patients (22% lung, 21% colorectal, 9% breast) with a mean age of 63 years; 62% had metastatic cancer. Eleven patients (9.4%) developed recurrent VTE, including two cases of fatal pulmonary embolism. VTE recurrence rates were 7.8% (95% CI, 3.1-18) in patients with a risk score of ≤0 points compared to 11% (95% CI, 5.2-20) for those with a score of ≥1 point (hazard ratio 1.3; 95% CI, 0.39-4.5). Baseline P-selectin levels but not D-dimer levels were significantly associated with a high risk of recurrence; the risk was four-fold higher in patients with elevated P-selectin levels than in those with normal levels (hazard ratio 4.0; 95% CI, 1.1-14). Changes in biomarker levels during treatment were not associated with recurrent VTE.
Conclusion:
Baseline P-selectin but not D-dimer levels predict recurrent VTE and may be a valuable addition to clinical prediction rules to select patients for more intensive therapy or closer observation.
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