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预测慢性病进展的机器学习:在CANVAS计划和CREDENCE试验中验证Klinrisk模型
Navdeep Tangri1,2, Thomas W Ferguson1,2, Ryan J Bamforth1
1Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada.
Diabetes, obesity & metabolism
|May 29, 2024
概括
克林里斯克的机器学习模型准确地预测了2型糖尿病患者慢性病 (CKD) 的进展. 该工具使用常规实验室数据,可以改善基于风险的CKD护理.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 慢性病 (CKD) 对健康造成重大负担,特别是在2型糖尿病患者中.
- 预测CKD进展对于及时干预和管理至关重要.
- 现有的风险分层工具可能在准确性和可访问性方面存在局限性.
研究的目的:
- 通过外部验证Klinrisk机器学习模型来预测CKD进展.
- 从CANVAS/CREDENCE试验中评估模型在大量2型糖尿病患者队列中的性能.
- 将Klinrisk的预测准确度与标准临床风险分层方法进行比较.
主要方法:
- 使用CANVAS/CREDENCE试验的汇总数据对Klinrisk模型进行外部验证.
- 慢性病的进展定义为估计膜过率 (eGFR) 或功能衰竭的40%下降.
- 模型性能使用接收器运行特征曲线 (AUC) 下面的面积和Brier得分进行评估,最高可达3年.
- 与病改善全球结果 (KDIGO) 热图类别的比较.
主要成果:
- 克林里斯克模型表现出高预测准确度,AUC为0.81在1年和0.88在3年.
- 低布赖尔评分 (0.020在1年,0.056在3年) 表明了优秀的校准.
- 在预测CKD进展方面,Klinrisk显著超过KDIGO热图类别 (P < .01).
结论:
- 克林里斯克的机器学习模型使用例行收集的实验室数据准确预测2型糖尿病患者的CKD进展情况.
- 该模型的高性能表明它对于基于风险的患者管理具有实用性.
- 集成到电子病历可以促进临床决策和改善患者的结果.
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