Frontiers in medicine · 2025
Development and validation of a nomogram for predicting bleeding risk in patients with pulmonary embolism.
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Ye Tian, Lei Wanlin, Wang Maofeng, Xu Lili
Аннотация
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.
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.