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A new score for predicting intracranial hemorrhage in patients using antiplatelet drugs
Fuxin Ma1,2, Zhiwei Zeng2, Jiana Chen2
1School of Pharmacy, Fujian Medical University, Fuzhou, China.
Insights
A new predictive model identifies nine key factors to assess the risk of intracranial hemorrhage (ICH) in patients taking antiplatelet drugs. This tool aims to help clinicians reduce ICH occurrence and improve patient outcomes.
Area of Science:
- Neurology
- Pharmacology
- Medical Informatics
Background:
- Antiplatelet drugs are crucial for preventing thrombotic events but increase the risk of intracranial hemorrhage (ICH).
- Currently, no specific clinical score exists to predict ICH risk in patients using antiplatelet therapy.
- ICH poses a significant threat to patient quality of life and survival.
Purpose of the Study:
- To identify independent risk factors associated with ICH in patients treated with antiplatelet drugs.
- To develop and validate a predictive model for ICH in this patient population.
- To provide a validated clinical tool for early risk assessment and prevention of ICH.
Main Methods:
- A retrospective, single-center study involving 753 patients on antiplatelet drugs.
- Logistic regression was used to build the predictive model.
- Internal validation employed AUC, calibration curves, and the Hosmer-Lemeshow test.
Main Results:
- Nine factors were identified as independent risk factors for ICH: male sex, headache/vomiting, hypertension, cerebrovascular disease, white matter hypodensity, abnormal GCS, elevated fibrinogen, and D-dimer.
- Lipid-lowering drugs emerged as a protective factor.
- The predictive model demonstrated strong discriminatory power (AUC=0.949 in development, 0.943 in validation) and good calibration.
Conclusions:
- A validated predictive model incorporating nine factors has been developed for assessing ICH risk in patients on antiplatelet drugs.
- This model shows excellent predictive value and may serve as an effective clinical tool for reducing ICH incidence.
- Further application of this model can aid in personalized risk management and prevention strategies for ICH.
Abstract:
Antiplatelet drugs in patients increase the risk of intracranial hemorrhage (ICH), which can seriously affect patients' quality of life and even endanger their lives. Currently, there is no specific score for predicting the risk of ICH caused by antiplatelet drugs. We aimed to identify factors associated with ICH in patients on antiplatelet drugs and to construct and validate a predictive model that would provide a validated tool for the clinic. Data were obtained from the patient medical records inpatient system. Prediction models were built by logistic regression, the area under the curve (AUC), and column line plots. Internal validation, analytical identification and calibration of the model using AUC, calibration curves and Hosmer-Lemeshow test. The registration number of this study is ChiCTR2000031909, and the ethical review number is 2020KY087. This single-center retrospective study enrolled 753 patients treated with antiplatelet drugs, including 527 in the development cohort. Multifactorial analysis showed that male, headache or vomiting, hypertension, cerebrovascular disease, CT-defined white matter hypodensity, abnormal GCS, fibrinogen and D-dimer were independent risk factors for ICH, and lipid-lowering drugs was a protective factor. The model was constructed using these nine factors with an AUC value of 0.949. In the validation cohort, the model showed good discriminatory power with an AUC value of 0.943 and good calibration (Hosmer-Lemeshow test P value of 0.818). Based on 9 factors, we derived and validated a predictive model for ICH with antiplatelet drugs in patients. The model has good predictive value and may be an effective tool to reduce the occurrence of ICH.
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