Development and Validation of a Predictive Model for Intracranial Haemorrhage in Patients on Direct Oral Anticoagulants
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Published on: January 17, 2013
Yuanyuan Liu1,2, Linjie Li1, Jingge Li1
1Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China.
Insights
Direct Oral Anticoagulants (DOACs) increase intracranial haemorrhage (ICH) risk. A new XGBoost model using six factors accurately predicts ICH in DOAC patients, improving risk assessment for better anticoagulation therapy.
Area of Science:
- Cardiology
- Neurology
- Pharmacology
Background:
- Intracranial haemorrhage (ICH) is a serious complication for patients on Direct Oral Anticoagulants (DOACs).
- Current risk scores do not adequately predict ICH risk in this patient population.
- There is a need for improved risk stratification tools for DOAC users.
Purpose of the Study:
- To develop and validate a predictive model for ICH risk in patients treated with DOACs.
- To identify key predictors of ICH in this cohort.
- To compare the performance of the new model against existing DOAC risk scores.
Main Methods:
- A cohort of 24,794 DOAC-treated patients from Tianjin, China, was analyzed.
- The cohort was randomly split for model development (80%) and validation (20%).
- Predictors were selected using forward stepwise selection, LASSO, and XGBoost; model performance was assessed via AUC and NRI.
Main Results:
- The XGBoost model identified six independent predictors: history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, and LDL cholesterol.
- The model demonstrated moderate discrimination (AUC: 0.68) and outperformed existing DOAC scores (ΔAUC = 0.063, P = 0.003; NRI = 0.374, P < 0.001).
- Risk categories effectively stratified ICH risk (low: 0.26%, moderate: 0.74%, high: 5.51%) with consistent validation performance.
Conclusions:
- A reliable XGBoost-based predictive model for ICH risk was developed and validated in a real-world Chinese DOAC-treated population.
- The model, incorporating six key risk factors, provides a valuable tool for individualized risk assessment.
- This model can aid in optimizing oral anticoagulation therapy and patient management.
Background:
Intracranial haemorrhage (ICH) poses a significant threat to patients on Direct Oral Anticoagulants (DOACs), with existing risk scores inadequately predicting ICH risk in these patients. We aim to develop and validate a predictive model for ICH risk in DOAC-treated patients.
Methods:
24,794 patients treated with a DOAC were identified in a province-wide electronic medical and health data platform in Tianjin, China. The cohort was randomly split into a 4:1 ratio for model development and validation. We utilized forward stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), and eXtreme Gradient Boosting (XGBoost) to select predictors. Model performance was compared using the area under the curve (AUC) and net reclassification index (NRI). The optimal model was stratified and compared with the DOAC model.
Results:
The median age is 68.0 years, and 50.4% of participants are male. The XGBoost model, incorporating six independent factors (history of hemorrhagic stroke, peripheral artery disease, venous thromboembolism, hypertension, age, low-density lipoprotein cholesterol levels), demonstrated superior performance in the development dateset. It showed moderate discrimination (AUC: 0.68, 95% CI: 0.64-0.73), outperforming existing DOAC scores (ΔAUC = 0.063, P = 0.003; NRI = 0.374, P < 0.001). Risk categories significantly stratified ICH risk (low risk: 0.26%, moderate risk: 0.74%, high risk: 5.51%). Finally, the model demonstrated consistent predictive performance in the internal validation.
Conclusion:
In a real-world Chinese population using DOAC therapy, this study presents a reliable predictive model for ICH risk. The XGBoost model, integrating six key risk factors, offers a valuable tool for individualized risk assessment in the context of oral anticoagulation therapy.
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