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A new score for predicting intracranial hemorrhage in patients using anticoagulant drugs
Fuxin Ma1, Zhiwei Zeng1, Jiana Chen1
1Department of Pharmacy, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Insights
Anticoagulant use increases intracranial hemorrhage risk. A predictive model identified male sex, leukoaraiosis, fall risk, high APTT, and high FIB as risk factors, with beta-blockers being protective.
Area of Science:
- Neurology
- Pharmacology
- Medical Informatics
Background:
- Anticoagulant therapy is crucial for managing various cardiovascular and thromboembolic conditions.
- However, anticoagulant use significantly elevates the risk of intracranial hemorrhage (ICH), a serious complication.
- Effective risk stratification is essential for patient safety during anticoagulation.
Purpose of the Study:
- To identify independent risk factors for cerebral hemorrhage in patients receiving anticoagulant therapy.
- To develop and validate a predictive model for assessing cerebral hemorrhage risk in this population.
Main Methods:
- A retrospective single-center study of 617 patients on anticoagulants.
- Logistic regression analysis was employed to identify risk factors and build a predictive model.
- Model performance was assessed using Area Under the Curve (AUC), calibration curves, and Hosmer-Lemeshow tests, with internal validation and cross-validation.
Main Results:
- Male sex, leukoaraiosis, high fall risk, elevated activated partial thromboplastin time (APTT) (≥45.4 s), and high fibrinogen (FIB) (≥4.2 g/L) were identified as independent risk factors for cerebral hemorrhage.
- Beta-blocker use demonstrated a protective effect.
- The developed predictive model achieved an AUC of 0.883 in the development cohort and 0.801 in the validation cohort, indicating good discriminatory and calibration power.
Conclusions:
- A predictive model incorporating key clinical and laboratory factors can effectively identify patients at high risk of cerebral hemorrhage while on anticoagulants.
- Early identification of at-risk individuals allows for proactive clinical interventions and improved patient management.
- The model provides a valuable tool for the clinical assessment of cerebral hemorrhage risk in anticoagulant users.
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:
This single-center retrospective study included 617 patients treated with anticoagulants. Multifactorial analysis showed that male, leukoaraiosis, high risk of falls, APTT ≥ 45.4 s, and FIB ≥ 4.2 g/L were independent risk factors for cerebral hemorrhage, and β-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.
