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    Item type:Publication,
    Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations
    (Wiley, 2023-03-21)
    Mora, Damián
    ;
    Mateo, Jorge
    ;
    Nieto, José A.
    ;
    Bikdeli, Behnood
    ;
    Yamashita, Yugo
    <jats:title>Summary</jats:title><jats:p>Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE‐BLEED scores were used for comparisons. External validation was performed with the COMMAND‐VTE database. Learning was carried out with data from 49 587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE‐BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE‐BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE‐BLEED scores only in the prospective validation cohort, but not in the external validation cohort.</jats:p>
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    Item type:Publication,
    The Clinical Course of Venous Thromboembolism May Differ According to Cancer Site.
    (Elsevier, 2016)
    Mahé I
    ;
    Chidiac J
    ;
    Bertoletti L
    ;
    Font C
    ;
    Trujillo-Santos J
    Background: We hypothesized that the clinical course of venous thromboembolism in patients with active cancer may differ according to the specificities of primary tumor site. Aim and methods: We used data from RIETE (international registry of patients with venous thromboembolism) to compare the clinical venous thromboembolism-related outcomes during the course of anticoagulation in patients with one of the 4 more frequent cancers (breast, prostate, colorectal, or lung cancer). Results: As of September 2014, 3947 cancer patients were recruited, of whom 938 had breast, 629 prostate, 1189 colorectal, and 1191 lung cancer. Overall, 55% had metastatic disease (42%, 36%, 53%, and 72%, respectively). During the course of anticoagulant therapy (mean duration, 139 days), the rate of thromboembolic recurrences was similar to the rate of major bleeding in patients with breast (5.6 [95% confidence interval (CI), 3.8-8.1] vs 4.1 [95% CI, 2.7-5.9] events per 100 patient-years) or colorectal cancer (10 [95% CI, 7.6-13] vs 12 [95% CI, 9.4-15] per 100 patient-years). In contrast, in patients with prostate cancer, the rate of venous thromboembolic recurrences was half the rate of major bleeding (6.9 [95% CI, 4.4-10] vs 13 [95% CI, 9.2-17] events per 100 patient-years), whereas in those with lung cancer, the rate of thromboembolic recurrences was twofold higher than the rate of major bleeding (27 [95% CI, 22-23] vs 11 [95% CI, 8.6-15] per 100 patient-years). Conclusions: Significant differences in the clinical profile of venous thromboembolic-related outcomes were observed according to the site of cancer. These findings suggest the development of cancer-specific anticoagulant strategies as an area for further research.