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在急性A型大动脉剖析中,在完全门置换后主要不良结果的术前预测,基于机器学习合奏
Hanshen Luo1, Xinyi Liu2, Yuehang Yang1
1Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China.
Scientific reports
|July 2, 2025
概括
一个新的机器学习模型准确地预测了急性A型大动脉剖析 (ATAAD) 患者的术后并发症,这些患者接受了冷象 (TAR+FET) 手术的全弓置换手术. 这种工具有助于外科医生识别高风险患者进行个性化治疗.
科学领域:
- 心血管外科心血管外科
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 急性A型大动脉解剖 (ATAAD) 是一种危及生命的疾病,需要复杂的手术干预.
- 用冷大象尾巴 (TAR+FET) 替换全弓是ATAAD的一种标准程序.
- 预测术后并发症对于改善患者的治疗结果至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于预测接受TAR+FET治疗的ATAAD患者的术后并发症.
- 提高术后生存质量,指导临床治疗决策.
- 确定主要不良结果 (MAO) 的关键预测因素.
主要方法:
- 对635名接受了TAR+FET手术的ATAAD患者进行了回顾性分析.
- 使用66个变量对190个机器学习模型进行培训和验证.
- 利用SHAP方法来识别一个简化的模型的11个强有力的预测因素.
- 使用ROC曲线,PRC,校准图和临床决策曲线评估模型性能.
主要成果:
- 最好的预测模型结合了随机生存森林 (RSF) 和梯度增强机器 (GBM).
- 完整模型和简化模型都实现了ROC曲线下的面积 (AUC) > 0.85,精度回调曲线下的面积 (AUPRC) > 0.703.
- 为临床使用开发了一种基于Web的简化预测模型.
结论:
- RSF-GBM组合模型有效地预测了接受TAR+FET治疗的ATAAD患者的术后并发症.
- 该模型帮助外科医生识别高风险个体,以进行个性化的术后管理.
- 这种预测工具可以改善临床决策和患者护理.
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