预测最终阶段心力衰竭患者接受HeartMate-3的存活率:比较机器学习方法
Renzo Y Loyaga-Rendon1, Deepak Acharya2, Milena Jani1
1From the Advanced Heart Failure and Transplant Cardiology Section, Spectrum Health, Grand Rapids, Michigan.
ASAIO journal (American Society for Artificial Internal Organs : 1992)
|November 1, 2023
概括
标准的统计方法有效地预测了HeartMate 3左心室辅助器件 (LVAD) 患者的1年死亡率. 这些传统技术的性能相当于机器学习算法,这表明它们在预测LVAD接受者的结果方面仍然有用.
科学领域:
- 心脏病学 心脏病学
- 医疗器械 医疗器械
- 数据科学数据科学数据科学
背景情况:
- 该HeartMate 3是目前在美国植入的唯一耐用的左心室辅助装置 (LVAD).
- 预测建模对于在LVAD植入后管理患者结果至关重要.
研究的目的:
- 在HeartMate 3 LVAD患者中开发和比较1年死亡率的预测模型.
- 评估标准统计技术与机器学习算法的性能,以预测结果.
主要方法:
- 从STS-INTERMACS注册表中分析了接受初级HeartMate 3植入物 (2017-2019) 的成年患者.
- 包括流行病学,临床,血液动力学和心声回声学数据.
- 标准后勤回归与机器学习模型 (弹性网,神经网络) 的比较,用于预测1年生存率.
主要成果:
- 包括3,853名患者;12.8%的患者经历了一年死亡率.
- 逻辑回归确定了年龄,MELD-XI得分,RA压力,INTERMACS概况,心率和HF病因作为关键预测因素 (AUC:0.72).
- 标准的物流回归模型显示,它与弹性网和神经网络模型相比没有劣势.
结论:
- 标准的统计技术与机器学习算法一样有效,用于预测HeartMate 3植入后的一年生存率.
- 机器学习的实用性可能取决于数据集,用于预测LVAD结果.
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