Evidence map›Paper›PMID 41164752›Full record

ArticleFrontiers in pharmacology2025

A study on a real-world data-based VTE risk prediction model for lymphoma patients.

Changli He, Yin Wang, Han Zhang, Sitian Li, Fengjiao Kang, Fengqun Cai, Lizhu Han, Qinan Yin, Gang Li, Xuewu Song and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Risk Assessment for Venous Thrombosis in Lymphoma and Emerging Biomarkers.International journal of molecular sciences · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Changli He *Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yin Wang *Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Han Zhang *Department of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Sitian LiDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Fengjiao KangPharmacy Department of Xinjiang Medical University Affiliated Traditional Chinese Medicine Hospital, Urumqi, Xinjiang, China.
Fengqun CaiDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Lizhu HanDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Qinan YinDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Gang LiDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Xuewu SongDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
Yuan BianDepartment of Pharmacy, Personalized Drug Research and Therapy Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Patients diagnosed with malignant tumors exhibit a markedly elevated risk of venous thromboembolism (VTE), which has a negative impact on their prognosis. Currently, there is no reliable predictive model specifically for thrombosis risk in lymphoma patients. This study aims to develop and validate a machine learning model leveraging real-world data, offering a dependable risk assessment tool for the early identification of VTE in lymphoma patients. Methods: We retrospectively analyzed 605 hospitalized patients with lymphoma between January 2019 and June 2024. Candidate predictors included demographic characteristics, comorbidities and medical history, tumor-related factors, treatment-related factors, and laboratory parameters. The primary endpoint was the occurrence of VTE within 6 months after hospitalization for confirmed lymphoma. Model development incorporated three imputation methods, three sampling strategies, three feature selection approaches, and nine machine learning algorithms. Predictive performance was compared across all models. Results: Combining different imputation, sampling, and feature selection strategies yielded 27 datasets, which were trained across nine algorithms to generate 243 models. The optimal model-Simp-SMOTE_rf_GBM, constructed using random forest imputation, SMOTE oversampling, and gradient boosting machine-achieved the highest predictive performance (AUC = 0.954). SHAP-based model interpretation identified nine key predictors ranked by importance: anticoagulant use, D-dimer, lactate dehydrogenase, central venous catheterization, carcinoembryonic antigen (CEA), Eastern Cooperative Oncology Group (ECOG) score, serum total protein (TP), total cholesterol (TC), and infectious disease. Conclusion: This study established and validated a machine learning model for predicting VTE risk in lymphoma patients, with the optimal model demonstrating excellent discriminatory ability (AUC = 0.954). The model provides evidence to guide the timing and strategy of anticoagulation, supporting early VTE screening and risk stratification in clinical practice. Its implementation has important implications for improving patient outcomes and advancing public health.

Indexed as

lymphomamachine learningpredictive factorspredictive modelvenous thromboembolism

Identifiers

PMID41164752
PMCPMC12558764

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.