深度学习衍生的右心室射出分数预测了经过过导管三管干预的患者的死亡率
Vera Fortmeier1, Márton Tokodi2, Attila Kovács2
1Department of General and Interventional Cardiology, Heart and Diabetes Center Northrhine-Westfalia, Ruhr University Bochum, Bad Oeynhausen, Germany.
深度学习精确评估了经过透导管三管干预 (TTVI) 后的右心室 (RV) 功能. 在手术后的RV喷射率 (RVEF) 低于38%,确定高风险患者的生存率明显降低.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 透导管三管干预 (TTVI) 是严重三管吐的关键治疗方法.
- 在TTVI后评估右心室 (RV) 功能至关重要,但在传统心声回声学中具有挑战性.
研究的目的:
- 用深度学习 (DL) 模型评估TTVI后的RV功能变化.
- 通过基于DL的心声回声学来评估RV射出分数 (RVEF) 轨迹.
主要方法:
- 一个DL模型预测了373名接受TTVI的患者从2D回声心脏图像中预测RVEF.
- 在基线和手术后1-3天测量RVEF.
- 一年全因死亡率是主要终点.
主要成果:
- 在队列水平上,RVEF中位数在TTVI前后相似,但个体轨迹有所不同.
- 在TTVI后的RVEF值为38%,预测了1年死亡率 (58.4%对比85.1%的生存率).
- 患有RVEF<38%的患者在手术后RVEF显著下降.
结论:
- 基于DL的回声心脏图提供了对TTVI后的RV功能进行公正的评估.
- 术后RVEF<38%可以确定患有高死亡风险的患者.
- 用DL增强的心声回声学可以改善风险分层,并为TTVI患者提供个性化的随访.
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