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一个机器学习算法用于使用生物标记数据进行外周动脉疾病预后.

Ben Li^1,2,3,4, Farah Shaikh², Abdelrahman Zamzam²

¹Department of Surgery, University of Toronto, Toronto, ON, Canada.

|February 16, 2024

概括

此摘要是机器生成的。

使用六种特定蛋白质的新算法可以在两年内预测周围动脉疾病 (PAD) 患者的主要不良四肢事件 (MALE). 该工具有助于风险分层和PAD管理的临床决策.

关键词:

人工智能的人工智能是人工智能.心血管医学心血管医学机器学习机器学习

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科学领域:

心血管研究研究心血管研究
生物标志物发现发现
机器学习在医学中的应用

背景情况:

周围动脉疾病 (PAD) 的诊断和预后通常依赖于孤立的生物标志物.
使用蛋白质面板的综合方法可以提高PAD结果的预测准确性.
目前PAD的风险分层可以通过先进的预测建模来改进.

研究的目的:

开发和验证PAD患者主要不良四肢事件 (MALE) 的预测算法.
确定一个可准确预测2年MALE的血蛋白质面板.
评估机器学习模型对PAD风险分层的有用性.

主要方法:

使用了一个模型开发队列 (n=270) 和一个前性验证队列 (n=277).
测量了37种蛋白质的血度,并对患者进行了2年的随访.
使用6种蛋白质组 (ADAMTS13,ICAM-1,ANGPTL3,Alpha 1-microglobulin,GDF15,endostatin) 开发了一个随机森林机器学习模型.

主要成果:

六种蛋白质在PAD患者中被发现具有差异性表达.
开发的随机森林模型在验证队列中实现了0.84的AUROC,用于预测2年的MALE.
六蛋白小组显示了强大的预测准确性MALE.

结论:

集成到机器学习模型中的6蛋白质面板可以有效地预测PAD患者的MALE.
这个算法为PAD风险分层提供了一个新的工具.
这些发现支持改善PAD血管评估和管理的临床决策.