使用平均左前庭电压的回归树模型进行非侵入性预测
Javier Ibero1,2, Ignacio García-Bolao1, Gabriel Ballesteros3
1Cardiology and Cardiac Surgery Department, Clínica Universidad de Navarra, Avenida Pio XII 36, 31008 Pamplona, Spain.
Biomedicines
|August 28, 2025
概括
机器学习模型现在可以无创地预测心房纤维化. 关键预测因素包括左心房体积和空化分数,为评估心房肌病提供了一种新方法.
科学领域:
- 心脏病学
- 医学成像
- 人工智能
背景情况:
- 前庭纤维化有助于前庭心肌病.
- 通过平均左心房电压 (MLAV) 对心房纤维化的侵入性评估限制了临床使用.
- 机器学习 (ML) 提供了非侵入性MLAV预测的潜力.
研究的目的:
- 开发和验证用于预测MLAV的非侵入性ML模型.
- 确定MLAV的关键临床和心声预测因素.
- 改进心房纤维化作为一个连续性的特征.
主要方法:
- 对113名心房患者进行肺静脉隔离 (PVI) 的前性研究.
- 超高密度电压映射 (uHDvM) 用于MLAV估计.
- 使用临床和心声学数据的回归树模型 (CART算法).
主要成果:
- ML回归树模型显示预测准确度中等 (R2 = 0.63).
- 索引的左心室最小体积和被动空化分数是最有影响力的预测因素.
- 基于MLAV或模型组的AF无复发存活率没有显著差异.
结论:
- 一个基于ML的新型回归树模型可以无创地预测MLAV.
- 最小的LA体积和被动排空分数是关键预测因素.
- 这种模型为表心肌病的特征和指导治疗提供了一个可访问的工具.
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