使用机器学习预测小儿心脏手术相关的急性损伤
Matthew Nagy1, Ali Mirza Onder2, David Rosen3
1Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, USA.
Pediatric nephrology (Berlin, Germany)
|November 7, 2023
概括
一个机器学习模型可以预测儿科患者中度至重度的心脏手术相关的急性损伤 (CS-AKI). 该工具使用患者数据来识别手术后早期CS-AKI高风险患者.
科学领域:
- 儿科脏病学 儿科脏病学
- 心血管外科心血管外科
- 人工智能在医学中的应用
背景情况:
- 与心脏手术相关的急性损伤 (CS-AKI) 对儿科患者构成重大风险.
- 早期预测CS-AKI对于及时干预和改善患者结果至关重要.
- 开发准确的预测模型是儿科心脏护理的一个关键目标.
研究的目的:
- 开发和验证一个监督机器学习 (ML) 模型,用于预测中度至严重的CS-AKI.
- 在接受心脏手术的儿科患者中,在术后第二天 (POD2) 确定CS-AKI风险.
- 用各种统计指标来评估ML模型的性能.
主要方法:
- 分析了402名接受心脏手术的儿科患者的回顾性队列.
- 使用80%-20%的列车测试分割来开发和验证ML模型.
- 该模型结合了人口统计,手术前,手术内以及POD0临床和实验室数据.
主要成果:
- ML模型表现出强大的预测性能,准确度为0.91和AUROC为0.88.
- 关键预测因素包括手术前血清肌素,手术持续时间和POD0乳酸盐水平.
- 该模型在CS-AKI预测中实现了0.92的精度和0.63的回忆.
结论:
- 监督的ML模型有效地预测了儿科心脏手术患者中适度至严重的CS-AKI.
- 该模型利用现有的临床和实验室数据进行早期风险识别.
- 这种ML方法显示了改善临床决策和患者管理的前景.
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