ArticleThe oncologist2020
External Validation of a Venous Thromboembolic Risk Score for Cancer Outpatients with Solid Tumors: The COMPASS-CAT Venous Thromboembolism Risk Assessment Model.
Article in The oncologist, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Incidence and risk factors of VTE in lung cancer: a meta-analysis.Annals of medicine · 2024Pooled it
- Accuracy of the COMPASS-CAT thrombosis risk assessment scale in predicting venous thromboembolism in cancer patients: a meta-analysis.Journal of thrombosis and thrombolysis · 2024Pooled it
- Enhancing venous thromboembolism risk prediction after immunotherapy for lung cancer.iScience · 2026Article
- Development and validation of a nomogram for predicting venous thromboembolism risk in colorectal cancer patients undergoing postoperative chemotherapy.Medical oncology (Northwood, London, England) · 2026Article
- The COMPASS-CAT risk assessment model for predicting in-hospital venous thromboembolism in patients with breast, lung, colorectal, and ovarian cancer: A retrospective cohort study.The Journal of international medical research · 2026Article
- Breast Cancer-Associated Venous Thromboembolism: Risk Factors, Mechanisms, and Clinical Management.Cancers · 2026Review
- Article
- Cancer-Associated Thrombosis in Patients Treated with Immune Checkpoint Inhibitors.International journal of molecular sciences · 2026Review
- Unmet Needs and Challenges in Cancer-Associated Venous Thromboembolism.International journal of molecular sciences · 2026Review
- Construction and verification of a nomogram to predict the probability of venous thromboembolism in lung cancer patients.Frontiers in molecular biosciences · 2026Article
- Venous Thromboembolism Management and Anticoagulation Opportunities in Cancer Patients.European cardiology · 2026Review
- Development and validation of the CHI-CAT score: a novel tool for assessing cancer-associated venous thromboembolism risk in China.BMC cancer · 2025Article
- Article
- Comparative Effectiveness of Direct Oral Anticoagulants and Warfarin on Venous Thromboembolism in Cancer Patients.Cancer medicine · 2025Article
- The Association Between Thromboembolic Events and ALK, ROS1, RET Rearrangements or EGFR Mutations in Patients With Advanced Lung Adenocarcinoma: A Retrospective Cohort Study.Thoracic cancer · 2025Article
- Radiotherapy-the not-so-insignificant contributor to cancer-associated venous thrombosis.Research and practice in thrombosis and haemostasis · 2025Review
- Association of coagulation-related indicators with postoperative venous thromboembolism occurrence in patients with pituitary tumors.Scientific reports · 2025Article
- Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.BMC cancer · 2025Article
- Risk assessment and prevention of cancer-associated venous thromboembolism in ambulatory patients with solid malignancies.Research and practice in thrombosis and haemostasis · 2025Article
- Trends and Updates on the Epidemiology of Cancer Associated Thrombosis: A Systematic Review.Bleeding, thrombosis and vascular biology · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrent risk assessment models (RAMs) for prediction of venous thromboembolism (VTE) risk in the outpatient cancer population have shown poor predictive value in many of the most common cancers. The Comparison of Methods for Thromboembolic Risk Assessment with Clinical Perceptions and AwareneSS in Real Life Patients-Cancer Associated Thrombosis (COMPASS-CAT) RAM was derived in this patient population and predicted patients at high risk for VTE even after initiation of chemotherapy. We sought to externally validate this RAM. MATERIALS AND
methodsPatients aged ≥18 years who presented to a tertiary care center between January 1, 2014, and December 31, 2016, with invasive breast, ovarian, lung, or colorectal cancers were included. The COMPASS-CAT RAM was applied using our health system's tumor registry and variables that were identified by International Statistical Classification of Diseases and Related Health Problems-9 and -10 codes of the electronic health record and independent chart review. The primary endpoint at 6-month study follow-up was documented VTE.
resultsA total of 3,814 patients were included. Documented VTE at 6-month follow-up occurred in 5.85% of patients. Patients stratified into low/intermediate- and high-risk groups had VTE rates of 2.27% and 6.31%, respectively. The sensitivity, specificity, and negative and positive predictive value of the RAM were 95%, 12%, 97.73%, and 6.31%, respectively. Diagnostic accuracy via receiver operating characteristic curve was calculated at 0.62 of the area under the curve.
conclusionIn this large retrospective external validation study of the COMPASS-CAT RAM for VTE in patients with cancer undergoing active treatment, model discrimination was moderate and calibration was poor. The model had good negative predictive value. Further prospective validation studies-especially within 6 months of cancer diagnosis-are needed before the model can be implemented into routine clinical practice for primary thromboprophylaxis of high-VTE-risk patients with cancer with solid tumors. IMPLICATIONS FOR PRACTICE: This study provides further guidance for researchers and clinicians in determining clinical and laboratory risk factors associated with development of venous thromboembolism among the ambulatory population of patients being treated for lung, breast, colorectal, or ovarian cancer. It validates the COMPASS-CAT risk model that was developed in this cancer population and suggests that further prospective validation of the model, with more focus on patients within 6 months of their index cancer diagnosis, would likely enhance the accuracy and usefulness of this model as a clinical prediction tool.
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