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使用人工智能来预测轻度大动脉狭窄症的结果

Raghav R Julakanti¹, Ratnasari Padang¹, Christopher G Scott²

¹Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.

European heart journal. Digital health

|January 23, 2025

概括

机器学习模型可以预测轻度大动脉狭窄 (AS) 的疾病进展和死亡率. 这些工具有助于识别高风险患者进行个性化的随访,改善这种常见疾病的结果.

科学领域:

心脏病学心脏病学
人工智能在医学中的应用
医疗信息学医疗信息学

背景情况:

大动脉狭窄 (AS) 是一种进展性疾病,如果不治疗,会导致显著的发病率和死亡率.
在AS中疾病的进展是高度可变的,使监测和后续护理复杂化.
需要预测工具来有效管理轻度AS患者.

研究的目的:

开发和验证用于预测AS进展和死亡率的机器学习 (ML) 模型.
为了识别患有轻度AS的高风险不良结果的患者.
为了实现针对轻度AS的个性化后续策略.

主要方法:

利用了来自三个临床场所的9611名患者的大量数据集,进行了连续的胸前外心回声图.
在心声图,心电图,实验室和诊断代码数据上训练有素的ML模型.
在独立测试组上验证的模型用于2年和5年的严重AS进展和所有原因死亡率的预测.

主要成果:

该AS进展模型实现了0.86的AUC,用于预测2年和5年内严重的AS.
对AS进展的关键预测因素包括大动脉的平均梯度和其他血液动力学测量.
死亡率模型显示AUC为0.84 (2年) 和0.87 (5年) 预测所有原因死亡率.

结论:

ML模型可以有效地识别患有轻度AS的患者,患有进展和死亡的高风险.
这些模型为实时,针对患者的随访建议提供了一条途径.
实施ML可以提高临床决策在管理轻度AS.

关键词:

大动脉狭窄症大动脉狭窄症人工智能的人工智能是人工智能.心声回声扫描 (Echocardiography) 是一种心声回声扫描.机器学习是机器学习.门疾病是一种门疾病.

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