ArticleJournal of thrombosis and haemostasis : JTH2020
Dynamic assessment of venous thromboembolism risk in patients with cancer by longitudinal D-Dimer analysis: A prospective study.
Article in Journal of thrombosis and haemostasis : JTH, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- Machine learning-based prediction of peripherally inserted central catheter-related thrombosis in hematological malignancies: development and validation of a dynamic model in a multicenter cohort.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
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- A deep learning model to dynamically predict cancer-associated thromboembolism in large-scale healthcare systems.NPJ digital medicine · 2026Article
- Breast Cancer-Associated Venous Thromboembolism: Risk Factors, Mechanisms, and Clinical Management.Cancers · 2026Review
- Development and validation of an interpretable machine learning model for venous thromboembolism risk prediction in patients with lung cancer: a real-world study.Frontiers in medicine · 2026Article
- AI-enabled D-dimeromics in precision breast oncology: a transformative framework for the identification, stratification, and prognostication of ultra-high-risk disease phenotypes.Frontiers in oncology · 2026Review
- Incidence and Predictors of Pulmonary Embolism Recurrence and Mortality in Patients With Lung Cancers.Cureus · 2025Article
- Construction and validation of a risk prediction model for venous thromboembolism post-VATS in simultaneous multicentric primary lung cancers.Journal of thoracic disease · 2025Article
- Prevalence and risk factors for asymptomatic venous thromboembolism in patients with primary brain tumors.iScience · 2025Article
- Longitudinal dynamics of hemostatic biomarkers in patients with cancer receiving immune checkpoint inhibitors vs chemotherapy: results from the Vienna CAT-BLED study.Research and practice in thrombosis and haemostasis · 2025Article
- The Vienna CATScore for predicting cancer-associated venous thromboembolism: an external validation across multiple time points.ESMO open · 2025Article
- Risk assessment and prevention of cancer-associated venous thromboembolism in ambulatory patients with solid malignancies.Research and practice in thrombosis and haemostasis · 2025Article
- Simple, Effective and Validated. VTE CASE Risk Assessment Score for Venous Thromboembolism in Metastatic Germ Cell Tumour Patients Before First-Line Chemotherapy.Cancer medicine · 2024Article
- Multi-factor early monitoring method based on D-dimer for iliac crest flap loss.BMC oral health · 2024Article
- Cancer progression and tumor hypercoagulability: a platelet perspective.Journal of thrombosis and thrombolysis · 2024Review
- Tissue factor pathway inhibitor is associated with risk of venous thromboembolism and all-cause mortality in patients with cancer.Haematologica · 2024Observational
- Article
- Identifying novel biomarkers using proteomics to predict cancer-associated thrombosis.Bleeding, thrombosis and vascular biology · 2024Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundVenous thromboembolism (VTE) is a frequent complication of cancer. Elevated D-dimer is associated with an increased risk of cancer-associated VTE. Whether changes in D-dimer over time harbor additional prognostic information that may be exploited clinically for dynamic prediction of VTE is unclear.
objectivesTo explore the potential role of longitudinal D-dimer trajectories for personalized prediction of cancer-associated VTE. PATIENTS/
methodsA total of 167 patients with active malignancy were prospectively enrolled (gastrointestinal: n = 59 [35%], lung: n = 56 [34%], brain: n = 50 [30%], others: n = 2 [1%]; metastatic disease: n = 74 [44%]). D-dimer (median = 0.8 µg/mL [25th-75th percentile: 0.4-2.0]) was measured at baseline and during 602 monthly follow-up visits. Joint models of longitudinal and time-to-event data were implemented to quantify the association between D-dimer trajectories and prospective risk of VTE.
resultsVTE occurred in 20 patients (250-day VTE risk = 12.1%, 95% confidence interval [CI], 7.8-18.5). D-dimer increased by 34%/month (0.47 µg/mL/month, 95% CI, 0.22-0.72, P < .0001) in patients who developed VTE, but remained constant in patients who did not develop VTE (change/month = -0.06 µg/mL, 95% CI, -0.15 to 0.02, P = .121). In joint modeling, a doubling of the D-dimer trajectory was associated with a 2.8-fold increase in the risk of VTE (hazard ratio = 2.78, 95% CI, 1.69-4.58, P < .0001). This finding was independent of established VTE risk factors. Highly personalized, dynamic predictions of VTE conditional on individual patients' D-dimer trajectories could be obtained.
conclusionsD-dimer increases before the onset of cancer-associated VTE, but remains constant over time in patients without VTE. This study represents proof-of-concept that longitudinal trajectories of D-Dimer may advance the personalized assessment of VTE risk in the oncologic setting.
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