预测非危急患者的毒负担的急性损伤:内部和外部验证研究
Esra Adiyeke1,2, Yuanfang Ren1,2, Benjamin Shickel1,2
1Intelligent Clinical Care Center, University of Florida, Gainesville, FL.
Kidney360
|November 12, 2025
概括
深度学习模型可以预测急性损伤 (AKI) 的进展. 多中心数据改善了模型性能,表明预防损伤的潜力.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 急性损伤 (AKI) 影响大约20%的住院患者.
- 如果不及时管理,AKI可能会导致脏不可逆转的损伤.
- 早期预测AKI进展对于及时干预至关重要.
研究的目的:
- 开发和验证用于预测AKI进展的深度学习模型.
- 在48小时内确定AKI进展的关键预测因素.
- 评估在多中心数据上训练的模型的性能.
主要方法:
- 使用两个大型医疗保健系统 (UPMC和UFH) 电子健康记录进行的回顾性研究.
- 开发深度学习模型,利用人口统计,并发症,药物,实验室结果和生命体征.
- 基于KDIGO血清肌素标准预测第二阶段或更高AKI的模型的外部验证.
主要成果:
- 模型表现出强大的预测性能,接收器操作特征曲线 (AUROC) 下面的面积值在0.77到0.84.8之间.
- 在多中心数据 (UFH-UPMC模型) 上训练的模型实现了UFH的AUROC为0.81,UPMC测试队伍的AUROC为0.82.
- 关键预测因素包括动力估计的膜过率,毒药物负担和血液尿素.
结论:
- 一个多中心深度学习模型在预测AKI进展方面表现强.
- 实施这些模型可以帮助预防AKI的进展.
- 这些发现强调了人工智能在改善急性损伤管理方面的潜力.
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