Key result
Alfalfa-Anticoagulant-ICH score accurately predicts ICH in anticoagulant users with an AUC of ~0.88.
Why the study?
Anticoagulant use increases the risk of intracranial hemorrhage, prompting the need to identify associated factors and develop a clinical predictive model.
Cohort (n=617)
No
Effect estimate: AUC 0.883 (95% CI 0.847-0.918)
p-value: p=<0.001
A newly developed predictive model incorporating six clinical and laboratory factors can effectively identify patients at high risk for intracranial hemorrhage during anticoagulant therapy.
May aid ICH risk stratification on anticoagulants; hypothesis-generating pending prospective validation.
Objectives: The use of anticoagulants in patients increases the risk of intracranial hemorrhage (ICH). Our aim was to identify factors associated with cerebral hemorrhage in patients using anticoagulants and to develop a predictive model that would provide an effective tool for the clinical assessment of cerebral hemorrhage. Methods: In our study, indications for patients receiving anticoagulation included AF, VTE, stroke/TIA, arteriosclerosis, peripheral vascular diseases (PVD), prosthetic mechanical valve replacement, etc. Data were obtained from the patient record hospitalization system. Logistic regression, area under the curve (AUC), and bar graphs were used to build predictive models in the development cohort. The models were internally validated, analytically characterized, and calibrated using AUC, calibration curves, and the Hosmer-Lemeshow test. Results: -blockers were protective factors. The model was constructed using these six factors with an AUC value of 0.883. In the validation cohort, the model had good discriminatory power (AUC = 0.801) and calibration power. Five-fold cross-validation showed Kappa of 0.483. Conclusion: Predictive models based on a patient's medical record hospitalization system can be used to identify patients at risk for cerebral hemorrhage. Identifying people at risk can provide proactive interventions for patients.
No takes yet. Share an insight, caveat, or question.
Ma et al. (2025) conducted a cohort in Patients using anticoagulant drugs (n=617). Alfalfa-Anticoagulant-ICH score vs. HAS-BLED score was evaluated on Prediction of intracranial hemorrhage (AUC in development cohort) (AUC 0.883, 95% CI 0.847-0.918, p=<0.001). The Alfalfa-Anticoagulant-ICH score, incorporating six clinical factors including leukoaraiosis and fall risk, accurately predicted intracranial hemorrhage in patients using anticoagulants with an AUC of 0.883.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: