Evidence map›Paper›PMID 41799704›Full record

ArticleTherapeutic advances in medical oncology2026

Risk stratification of in-hospital venous thromboembolism for urological cancers: a multicenter retrospective study.

Zhaoyang Li, Tonghe Zhang, Guangbin Zhu, Haishan Shen, Minghao Zhang, Huayu Wang, Huitang Yang, Hailong Hu, Yankui Li

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Article in Therapeutic advances in medical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Zhaoyang LiDepartment of Vascular Surgery, The Second Hospital of Tianjin Medical University, Tianjin, China.
Tonghe ZhangDepartment of Vascular Surgery, The Second Hospital of Tianjin Medical University, Tianjin, China.
Guangbin ZhuDepartment of Urology, Tianjin Hospital, Tianjin, China.
Haishan ShenDepartment of Urology, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Minghao ZhangDepartment of Urology, Tianjin Third Central Hospital, Tianjin, China.
Huayu WangDepartment of Pharmacy, The Second Hospital of Tianjin Medical University, Tianjin, China.
Huitang YangDepartment of Vascular Surgery, The Second Hospital of Tianjin Medical University, Tianjin, China.
Hailong HuDepartment of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China Tianjin Key Laboratory of Urology, Tianjin Institute of Urology, Tianjin 300211, China.
Yankui LiDepartment of Vascular Surgery, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.ORCID https://orcid.org/0000-0001-8048-0910

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The current predictive models for venous thromboembolism (VTE) have limitations in predicting VTE risk in patients with urological cancer. Objectives: To establish and validate a risk stratification model for in-hospital VTE in patients with urological cancer. Design: Retrospective, multicenter study. Methods: The clinical data of 735 patients with urological cancer in the Department of Urology at four hospitals in China between January 2019 and December 2024 were analyzed. VTE ( Results: In this study, we developed a risk stratification model using a logistic regression based on variables selected by LASSO and constructed a nomogram to visualize the model. The areas under the receiver operating characteristic curve for the training, validation, and external validation cohorts were 0.933 (95% confidence interval (CI): 0.909-0.957), 0.900 (95% CI: 0.850-0.950), and 0.857 (95% CI: 0.776-0.938), respectively. The corresponding Brier scores for them were 0.070, 0.089, and 0.200. The calibration curve indicated good model performance. Decision curve analysis evaluated the clinical utility of the model and showed that it provided a higher net benefit than the "treat-all" and "treat-none" strategies across a wide range of threshold probabilities, supporting its potential clinical usefulness. Conclusion: A predictive model was developed to estimate the risk of in-hospital VTE in patients with urological cancer, enabling personalized assessment and guiding preventive strategies. Further studies are needed to better validate our model.

Indexed as

D-dimerpredictive modelsrisk factorsurological cancersvenous thromboembolism

Identifiers

PMID41799704
PMCPMC12961108

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