一个新的可解释的在线计算器用于糖尿病患者的对比诱导的AKI:一个多中心验证和前性评估研究
Mengqing Ma1, Xin Wan2, Yuyang Chen1
1Department of Nephrology, Sir Run Run Hospital, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
Journal of translational medicine
|July 31, 2023
概括
一个新的机器学习模型预测了经皮冠状动脉干预的糖尿病患者的对比诱导急性损伤 (CIAKI). 该工具帮助临床医生实时进行风险评估和决策.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
背景情况:
- 对比诱导的急性损伤 (CIAKI) 是皮肤穿冠状动脉干预 (PCI) 后的严重并发症,特别是在糖尿病患者中.
- CIAKI与短期和长期死亡率和发病率的增加有关.
研究的目的:
- 开发和验证在接受PCI的糖尿病患者中CIAKI的预测模型.
- 为实时风险评估创建一个可解释的机器学习 (ML) 模型.
主要方法:
- 在四家医院的3514名糖尿病患者的数据上训练和验证了6个ML算法.
- 极端梯度增强树木 (XGBT) 模型表现出卓越的性能,并被用于开发简要的CIAKI糖尿病预测模型 (BCPMD).
- 为了模型的可解释性,采用了SHAP (SHapley Additive Explanations) 方法,并开发了一个在线风险计算器.
主要成果:
- 来自XGBT的BCPMD实现了高预测准确性,曲线下的面积 (AUC) 值在内部,外部和前性验证队列中从0.801到0.819不等.
- 确定的主要预测因素包括急性冠状动脉综合征,尿蛋白,利尿剂,LVEF,血红蛋白,CHF,心痛,尿酸,DBP,对比度体积,专蛋白,基线肌,冠状动脉疾病血管,葡萄糖和糖尿病持续时间.
- BCPMD已成功验证并集成到在线网络风险计算器中.
结论:
- 开发了一种基于ML的新可解释的糖尿病患者CIAKI风险计算器.
- 该工具为临床医生提供实时预测能力,并为管理CIAKI风险提供了明智的临床决策.
相关概念视频
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