一个基于Klotho的机器学习模型,用于预测慢性病中的脏和心血管结果
Yating Wang1, Yu Shi1, Tangli Xiao1
1Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Kidney Center of PLA, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, PR China.
Kidney diseases (Basel, Switzerland)
|June 5, 2024
概括
使用血清Klotho的机器学习模型有效预测慢性病 (CKD) 患者的末期病 (ESKD) 和心血管疾病 (CVD). 这些经过验证的模型为风险评估提供了重要的临床实用性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 生物标志物研究 生物标志物研究
- 医疗保健中的机器学习
背景情况:
- 慢性病 (CKD) 是一种进展性疾病,与末期病 (ESKD) 和心血管疾病 (CVD) 的高风险有关.
- 血清克洛托是功能和心血管健康的潜在生物标志物.
- 准确预测ESKD和CVD对于及时干预CKD患者至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测CKD患者的ESKD和CVD风险.
- 评估包含血清Klotho水平的模型的预测性能.
- 在这个群体中确定ESKD和CVD的关键风险因素.
主要方法:
- 使用了400名非透析CKD患者的队列.
- 开发了5个ML模型,使用47个临床特征,包括血清Klotho,预测ESKD和CVD在3,5,8年.
- 对30%的数据集进行了内部验证;使用C指数和曲线下面面积 (AUC) 评估模型性能.
主要成果:
- 最少绝对收缩和选择操作员 (LASSO) 回归模型实现了ESKD预测的最高准确性 (C指数=0.71),血清Klotho是关键特征 (AUC=0.930).
- 随机生存森林模型在心血管疾病预测方面表现出最高的准确性 (C指数=0.66),血清Klotho作为一个显著的预测因子 (AUC=0.782).
- 对ESKD的关键预测因素包括eGFR,尿路微专蛋白,血清专蛋白,酸盐和副甲状腺激素,以及血清Klotho.
结论:
- 成功开发并验证了结合血清Klotho的ML模型,用于预测CKD患者的ESKD和CVD.
- 这些模型表现出良好的预测性能,突出了它们潜在的临床实用性.
- 血清克洛托是慢性病患者风险分层的一个有价值的生物标志物.
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