ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1692156
Development and Validation of a Nomogram for Predicting Bleeding Risk in Patients with Pulmonary Embolism
Provisionally accepted
- 1Department of Emergency, Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100, China;, dongyang, China
- 2Dongyang People's Hospital, Dongyang, China
- 3Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100, China;, dongyang, China
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Purpose: Bleeding during anticoagulation therapy represents a critical challenge in pulmonary embolism (PE) management, This study aimed to develop and validate a PE-specific bleeding risk prediction model. Methods: This retrospective cohort study utilized a clinical research big data platform, including 5,632 hospitalized PE patients (January 2013-December 2024). Significant bleeding within 6 months served as the primary outcome. After excluding variables with >20% missingness, 29 predictors were analyzed. The cohort was randomly split into development (𝑛=3,942) and validation sets (𝑛=1,690). LASSO regression identified key predictors, with multivariable logistic regression constructing the final model. Performance was assessed via AUC-ROC, calibration plots, and decision curve analysis (DCA). Results: The final model identified six predictors: prior bleeding history, renal insufficiency, red blood cell count, systolic pressure, cerebral infarction, and creatinine. The model demonstrated robust discrimination (development AUC: 0.756, 95%CI:0.729-0.784; validation AUC: 0.729, 95%CI:0.685-0.773) and calibration (validation slope: 0.810). DCA confirmed significant net benefit at 5-35% thresholds, with 30% as the optimal cut-off. At this threshold, the model reduced major bleeding by 42% versus standard care. Conclusion: This novel PE-specific bleeding risk tool provides clinically actionable stratification, enabling personalized anticoagulation intensity adjustment. Implementation may reduce hemorrhage-related morbidity while optimizing resource utilization.
Keywords: Pulmonary Embolism, bleeding, Risk prediction model, nomogram, adverse effects, Anticoagulants
Received: 25 Aug 2025; Accepted: 26 Sep 2025.
Copyright: © 2025 Tian, Wanlin, Wang and Lili. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Maofeng Wang, wzmcwmf@wmu.edu.cn
Xu Lili, xulili41663@163.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.