风险因素和预测模型后手术中度至重度的甲状腺吐在跨导管大动脉置换后:机器学习方法
Zhenzhen Li1,2, Jianing Fan2, Jiajun Fan3
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
BMC cardiovascular disorders
|May 10, 2025
概括
机器学习模型可以预测透气管大动脉置换 (TAVR) 后中度至重度的甲状腺反 (MR). 识别风险因素,如心声回声和血液检测结果,有助于早期检测和干预,从而改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 经过透气管大动脉置换 (TAVR) 的术后中度至重度的 mitrale regurgitation (MR) 与不良结果有关.
- 导致这种并发症的因素仍然不完全理解.
- 早期检测和干预对于管理TAVR后的MR至关重要.
研究的目的:
- 为了确定术后中度至重度MR的风险因素,TAVR后.
- 开发和验证用于预测这种并发症的机器学习 (ML) 模型.
- 加强早期检测,促进危险患者的及时干预.
主要方法:
- 对TAVR患者数据的回顾性分析 (2014年8月 - 2023年8月).
- 根据严重程度将患者分为术后MR和非MR组.
- 评估各种ML模型使用准确度,精度,回忆,F1得分和AUC.
- 使用Shapley增量解释 (SHAP) 值来解释模型的可解释性.
主要成果:
- 随机森林模型显示了手术后中度至重度MRTAVR后的优异预测性能.
- 通过SHAP分析确定的关键预测因素包括心声学参数,血液测试结果,患者年龄和BMI.
- 这些因素是开发的ML模型预测准确性的组成部分.
结论:
- 机器学习模型在TAVR后预测术后中度至重度MR方面显示出显著的潜力.
- 将临床指标集成到ML模型中可以提高预测准确度.
- 这些发现支持使用ML来改善TAVR后的风险分层和患者管理.
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