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在无抗凝剂连续脏替代疗法中预测凝血风险

Liang Liu¹, Dashuang Liu², Ting He²

¹Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China, liuliang728@tmmu.edu.cn.

Blood purification

|August 12, 2024

概括

人工智能准确地预测了无抗凝剂连续脏替代疗法 (CRRT) 中的电路凝血风险. 机器学习模型识别了血小板和甘油三等关键因素,使个性化治疗能够改善结果.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

持续置换疗法 (CRRT) 对急性损伤至关重要.
无抗凝剂的CRRT对于出血风险患者是必要的,但会增加电路凝血.
预测和预防CRRT电路凝血对于治疗疗效和资源管理至关重要.

研究的目的:

开发和验证人工智能驱动的机器学习模型,用于预测无抗凝剂CRRT期间的电路凝血风险.
确定关键的临床指标,表明CRRT电路凝血的高风险.
为了为接受无抗凝固剂CRRT的患者提供个性化治疗策略.

主要方法:

对212名接受无抗凝血剂CRRT的患者进行了回顾性分析.
开发和验证八种机器学习模型,以在24小时内预测电路凝血.
使用曲线下的面积 (AUC) 和5倍交叉验证的性能评估;使用SHAP图表的特征重要性分析.

主要成果:

机器学习模型显示出出色的预测性能,集体学习实现了0.863.3的AUC.
随机森林是最好的单个算法模型,AUC为0.819.
电路凝血的关键预测因素包括血小板计数,过分数 (FF) 和甘油三.

结论:

一个基于人工智能的模型有效地预测了无抗凝剂的CRRT中高精度的电路凝血风险 (AUC 0.863).
血小板,过率和甘油三是凝血风险的重要指标.
该模型支持个性化治疗策略,以减轻CRRT电路凝血,改善患者的治疗结果并降低成本.

关键词:

抗凝剂是一种抗凝剂.电路凝结电路的凝结方式持续的替代疗法持续的替代疗法机器学习机器学习

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