Key result
Novel nomogram outperforms traditional scores with ~0.77 AUC for predicting bleeding in cancer-associated AF.
Why the study?
Current bleeding risk scores often lack cancer-specific variables, limiting their accuracy in patients with concomitant cancer and atrial fibrillation.
Does a novel nomogram incorporating cancer-specific factors improve bleeding risk prediction compared to traditional risk scores in patients with concomitant cancer and atrial fibrillation?
Observational (n=972)
Randomly assigned to training and validation sets (7:3 ratio)
Does a novel nomogram incorporating cancer-specific factors improve bleeding risk prediction compared to traditional risk scores in patients with concomitant cancer and atrial fibrillation?
A novel bleeding risk nomogram that incorporates cancer-specific factors such as tumor stage and anti-angiogenic therapy outperforms traditional bleeding scores in patients with concomitant cancer and atrial fibrillation.
May improve bleeding risk prediction in cancer patients with AF over standard scores; leaves open prospective validation before practice change.
Background With advancements in cancer therapy and prolonged survival, the prevalence of concomitant cancer and atrial fibrillation (AF) is increasing. The interaction between antineoplastic agents and anticoagulants complicates clinical management, rendering bleeding risks unpredictable. Current bleeding risk scores (e.g., HAS-BLED) often lack cancer-specific variables, limiting their accuracy in this population. We aimed to develop and validate a novel nomogram to predict bleeding risk specifically for patients with cancer and AF. Methods We conducted a retrospective study of 972 patients with cancer and AF. Patients were randomly assigned to a training set and a validation set in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for bleeding. A nomogram was constructed using R software. The model's performance was compared with the HAS-BLED, HEMORR2HAGES, NBLDSCOR, and ORBIT scores using the Area Under the Receiver Operating Characteristic Curve (AUC). Decision Curve Analysis (DCA) was used to assess clinical utility. Results Bleeding events occurred in 173 patients (17.8%), with the gastrointestinal tract being the most common site. Multivariate analysis identified eight independent risk factors: age ≥75 years, anemia, history of bleeding, anticoagulant usage, vitamin K antagonist (VKA) therapy, concomitant antiplatelet therapy, advanced cancer stage, and anti-angiogenic therapy. The novel nomogram demonstrated robust discrimination with an AUC of 0.784 in the training set and 0.770 in the validation set. Compared to the HAS-BLED, HEMORR2HAGES, NBLDSCOR, and ORBIT scores, the new model showed a higher AUC and superior net benefit in the validation cohort. Conclusion We developed a novel bleeding risk prediction model for patients with cancer and AF that incorporates crucial cancer-specific factors, such as tumor stage and anti-angiogenic therapy. This nomogram outperforms traditional risk scores in predictive accuracy and offers a valuable tool for personalized anticoagulation decision-making in this high-risk population.
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J Liu (2026) conducted an observational in Concomitant cancer and atrial fibrillation (n=972). Novel bleeding risk nomogram vs. HAS-BLED, HEMORR2HAGES, NBLDSCOR, and ORBIT scores was evaluated on Predictive accuracy for bleeding events (AUC). A novel nomogram for predicting bleeding risk in patients with cancer and atrial fibrillation demonstrated an AUC of 0.770 in the validation set, outperforming traditional risk scores.
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