{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T14:06:23Z","timestamp":1788703583892,"version":"build-2803163510"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective cohort study utilized a clinical research big data platform, including 5,632 hospitalized PE patients (January 2013\u2013December 2024). Significant bleeding within 6\u202fmonths served as the primary outcome. After excluding variables with &amp;gt;20% missingness, 29 predictors were analyzed. The cohort was randomly split into development (<jats:italic>n<\/jats:italic>\u202f=\u202f3,942) and validation sets (<jats:italic>n<\/jats:italic>\u202f=\u202f1,690). 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).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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\u20130.784; validation AUC: 0.729, 95%CI: 0.685\u20130.773) and calibration (validation slope: 0.810). DCA confirmed significant net benefit at 5\u201335% thresholds, with 30% as the optimal cut-off. At this threshold, the model reduced major bleeding by 42% versus standard care.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1692156","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:52:58Z","timestamp":1760017978000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and validation of a nomogram for predicting bleeding risk in patients with pulmonary 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