机器学习衍生周期长度可变度指标预测在可植入心脏转换器除器接受者中自发终止腹性心力衰竭
Arunashis Sau1,2, Amar Ahmed1, Jun Yu Chen1
1National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Road, W12 0HS, London, UK.
European heart journal. Digital health
|January 24, 2024
概括
机器学习模型可以使用循环长度 (CL) 变化来预测心室性心力衰竭 (VT) 终止. 这可能有助于最大限度地减少不必要的植入式心脏转换器除器 (ICD) 疗法.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 植入式心脏转换器除器 (ICD) 治疗心室动脉障碍 (VT) 与死亡率的增加有关.
- 尽量减少不必要的ICD治疗对于患者安全至关重要.
- 预测自发VT终止可以帮助避免不适当的冲击.
研究的目的:
- 调查机器学习衍生的VT周期长度 (CL) 变化指标是否可以区分持续和自发终止的VT.
- 利用早期的VT事件数据,开发一个用于VT终止的预测模型.
主要方法:
- 从27名患者的ICD数据回顾分析了69次VT情节.
- 计算VT CL变化和心率变化指标.
- 应用随机森林分类器,使用前10个VT CL的特征来预测终结.
主要成果:
- 与持续性静脉瘤相比,自发终止的静脉瘤发作表现出更大的CL变异性 (前10个CL的SD:20.1 ± 8.9毫秒与11.5 ± 7.8毫秒相比,P < 0.0001).
- 在自发终止静脉治疗 (0.39±0.32对比0.14±0.39,P<0.005) 中观察到更高的自动回归系数.
- 一个随机森林模型在使用最初的10个CL中的6个特征来预测VT终止时达到77%的准确性.
结论:
- 静脉结结合细胞的变化和不稳定性是自发静脉结结合细胞终止的指标.
- 分析早期VT CL动态的机器学习模型可以预测自发终止.
- 将这个模型集成到ICD算法中可能会推迟自我终结的静脉瘤发作治疗,从而减少伤害.
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