ArticleAmerican journal of hematology2024
Thrombosis risk prediction in lymphoma patients: A multi-institutional, retrospective model development and validation study.
Article in American journal of hematology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.
- Predictors and Risk Assessment Models for Venous Thromboembolism in Patients Diagnosed with Lymphoma: A Systematic Review.Current oncology (Toronto, Ont.) · 2026Pooled it
- Machine learning prediction models for deep vein thrombosis in hospitalized patients: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Temporal validation of the electronic health record cancer-associated thrombosis model in patients with low bleeding risk.Research and practice in thrombosis and haemostasis · 2026Article
- Risk Assessment for Venous Thrombosis in Lymphoma and Emerging Biomarkers.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Article
- The C-reactive protein-triglyceride glucose index (CTI) predicts mortality in cardiovascular-kidney-metabolic syndrome: a dual-cohort study with machine learning validation.International journal of surgery (London, England) · 2026Article
- Predicting thrombotic risk in patients with classical Hodgkin lymphoma: Thro-HL multicenter study.HemaSphere · 2025Article
- Circulating tumor DNA predicts venous thromboembolism in patients with cancers.Journal of thrombosis and haemostasis : JTH · 2025Article
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 1 country.
Funding
Abstract
Venous thromboembolism (VTE) poses a significant risk to cancer patients receiving systemic therapy. The generalizability of pan-cancer models to lymphomas is limited. Currently, there are no reliable risk prediction models for thrombosis in patients with lymphoma. Our objective was to create a risk assessment model (RAM) specifically for lymphomas. We performed a retrospective cohort study to develop Fine and Gray sub-distribution hazard model for VTE and pulmonary embolism (PE)/ lower extremity deep vein thrombosis (LE-DVT) respectively in adult lymphoma patients from the Veterans Affairs national healthcare system (VA). External validations were performed at the Harris Health System (HHS) and the MD Anderson Cancer Center (MDACC). Time-dependent c-statistic and calibration curves were used to assess discrimination and fit. There were 10,313 (VA), 854 (HHS), and 1858 (MDACC) patients in the derivation and validation cohorts with diverse baseline. At 6 months, the VTE incidence was 5.8% (VA), 8.2% (HHS), and 8.8% (MDACC), respectively. The corresponding estimates for PE/LE-DVT were 3.9% (VA), 4.5% (HHS), and 3.7% (MDACC), respectively. The variables in the final RAM included lymphoma histology, body mass index, therapy type, recent hospitalization, history of VTE, history of paralysis/immobilization, and time to treatment initiation. The RAM had c-statistics of 0.68 in the derivation and 0.69 and 0.72 in the two external validation cohorts. The two models achieved a clear differentiation in risk stratification in each cohort. Our findings suggest that easy-to-implement, clinical-based model could be used to predict personalized VTE risk for lymphoma patients.
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Registered trials
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