利用血栓形成和出血的风险评估来优化非膜性心房的抗凝策略
Yue Zhao1, Li-Ya Cao1, Ying-Xin Zhao2
1Department of Pharmacy, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing, P. R. China.
Thrombosis and haemostasis
|August 13, 2024
概括
机器学习模型可以预测非膜性心房动 (NVAF) 患者进行导管切除 (CA) 的血栓形成和出血风险. 这些工具的目的是准确的抗凝策略,改善患者的治疗结果和安全.
科学领域:
- 计算医学和心脏病学.
- 临床数据科学和机器学习应用程序
背景情况:
- 口服抗凝剂 (OAC) 在导管切除 (CA) 后对于非膜性心房动 (NVAF) 来预防血栓形成至关重要.
- 不适当使用OAC不提供中风预防益处,并增加出血风险.
- 需要精确的抗凝策略来平衡NVAF患者的疗效和安全.
研究的目的:
- 开发和比较机器学习 (ML) 模型,用于预测CA.后NVAF患者的血栓形成和出血风险.
- 建立基于数据的,精确的抗凝血策略.
- 确定影响血栓形成和出血事件的关键风险因素.
主要方法:
- 从2015年至2023年接受CA的1,055名NVAF患者的回顾性分析.
- 八个ML算法的比较,用于预测血栓和出血.
- 使用特征重要性和夏普利添加式解释 (SHAP) 的模型解释.
主要成果:
- XGBoost模型显示了最高的性能:血栓形成的精度为0.740,出血的精度为0.781.
- 血栓形成的主要预测因素:年龄,BNP,肝素持续时间.
- 主要出血预测因素:抗凝血策略,BNP,脂质.
- 优化模型 (血栓形成的RF-T,出血的Xw-B) 实现了高回忆率 (分别为0.774和0.780).
结论:
- 优化的ML模型显示,在CA后的NVAF患者中预测血栓和出血具有显著的潜力.
- 这些模型可以指导个性化的抗凝策略,增强患者护理.
- 这些发现可能成为未来临床风险分层分数的基础.
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