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机器学习用于识别经过腹膜透析的患者中短期全因和心血管死亡的情况

Xiao Xu¹, Zhiyuan Xu², Tiantian Ma¹

¹Renal Division, Department of Medicine, Peking University First Hospital; Institute of Nephrology, Peking University; Key Laboratory of Renal Disease, Ministry of Health; Key Laboratory of Renal Disease, Ministry of Education; Beijing, China.

Clinical kidney journal

|March 5, 2025

概括

机器学习模型准确地预测腹腔透析 (PD) 患者的近期死亡. 这些CVDformer模型具有高灵敏度,可在三个月内预测所有原因和心血管死亡率.

科学领域:

腎臟病學 (nephrology) 是一種醫學.
人工智能的人工智能
心血管医学心血管医学

背景情况:

心血管风险因素在腹腔透析 (PD) 患者中越来越多地被认可.
对于临床医生来说,准确预测近期死亡率仍然是这个人群中的挑战.

研究的目的:

开发和验证机器学习模型,用于预测近期所有原因和心血管疾病死亡的PD患者.
改进PD患者个性化风险评估.

主要方法:

开发了机器学习模型 (CVDformer),使用来自7539名PD患者的数据.
使用了一套培训和内部测试,并进行了5倍的交叉验证.
包括人口统计,临床特征,实验室数据和透析变量.
使用AUROC和精度回忆曲线下的面积来评估预测性能.

主要成果:

在测试组中,CVDformer模型表现出高的预测性能.
所有原因死亡的AUROC值为0.8767和心血管死亡的0.9026.
精确回忆曲线下的面积值为所有原因死亡的0.9338和心血管死亡的0.9073.
模型显示高灵敏度和对3个月死亡率的积极预测值.

结论:

开发的CVDformer机器学习模型在预测腹膜透析患者近期全因和心血管死亡方面显示出显著的前景.
这些模型为临床医生提供了有价值的工具,以评估个体患者的风险.
为了未来的临床应用,建议进行进一步的校准.

关键词:

在CVD中,CVD是非常重要的.变压器变压器变压器慢性脏疾病慢性脏疾病机器学习是机器学习.死亡率死亡率腹膜透析是指腹膜透析.

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