Department of Cardiology, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin 300052, China.
Department of Cardiology, Qingzhou People's Hospital, Weifang, Shandong 262500, China.
Clin Appl Thromb Hemost. 2024 Jan-Dec;30:10760296241271338. doi: 10.1177/10760296241271338.
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.
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.
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, = 0.003; NRI = 0.374, < 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.
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.
颅内出血（ICH）对直接口服抗凝剂（DOAC）治疗的患者构成重大威胁，现有的风险评分无法充分预测这些患者的 ICH 风险。我们旨在开发和验证 DOAC 治疗患者 ICH 风险的预测模型。
在中国天津市的全省电子医疗和健康数据平台中确定了 24794 例接受 DOAC 治疗的患者。该队列按 4：1 的比例随机分为模型开发和验证组。我们利用向前逐步选择、最小绝对值收缩和选择算子（LASSO）和极端梯度提升（XGBoost）来选择预测因子。使用曲线下面积（AUC）和净重新分类指数（NRI）比较模型性能。最优模型进行分层并与 DOAC 模型进行比较。
中位年龄为 68.0 岁，50.4%的参与者为男性。XGBoost 模型纳入了 6 个独立因素（既往出血性卒中史、外周动脉疾病、静脉血栓栓塞、高血压、年龄、低密度脂蛋白胆固醇水平），在开发数据集的表现更为出色。其显示出中等程度的区分能力（AUC：0.68，95%CI：0.64-0.73），优于现有的 DOAC 评分（ΔAUC=0.063，=0.003；NRI=0.374，<0.001）。风险类别显著分层了 ICH 风险（低风险：0.26%，中风险：0.74%，高风险：5.51%）。最后，该模型在内部验证中表现出一致的预测性能。
在使用 DOAC 治疗的中国真实世界人群中，本研究提出了一种可靠的 ICH 风险预测模型。XGBoost 模型整合了 6 个关键风险因素，为口服抗凝治疗背景下的个体化风险评估提供了有价值的工具。
