开发一个机器学习模型的术后急性损伤使用非侵入性时间敏感的内科预测器的开发
Siavash Zamirpour1, Alan E Hubbard2, Jean Feng3
1School of Medicine, University of California, San Francisco, CA 94143, USA.
Bioengineering (Basel, Switzerland)
|August 26, 2023
概括
这项研究开发了一种模型,使用手术前和手术内数据预测手术后急性损伤 (AKI). 该模型利用尿量和输液指数等关键变量来识别有风险的患者,改善手术结果.
科学领域:
- 麻醉学和外科手术期间的医学.
- 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:
- 急性损伤 (AKI) 是一个严重的术后并发症,在有限的术内预测工具.
- 早期识别患有AKI风险的患者对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和验证使用术前和高频内科手术数据进行术后AKI的预测模型.
- 确定AKI风险分层的关键手术内变量,包括新的指标,如 perfusion index,用于AKI风险分层.
主要方法:
- 11,212例手术病例的回顾性队列研究与血清肌素数据.
- 利用弹性网和等级组 LASSO 回归来从14个手术前和20个手术内因素中对变量进行选择.
- 模型性能使用接收机操作员特征曲线 (AUC) 下的面积进行评估.
主要成果:
- 最终的模型包含了手术前血清肌素,会议记录,尿液输出切断,心率, perfusion 指数和估计的血液损失.
- 达到0.81的AUC (95% CI,0.77-0.85),在0.767.7的值下,具有77%的特异性和74%的灵敏性.
- 证明了手术内时间序列数据和 perfusion 指数在预测术后 AKI 的有用性.
结论:
- 通过使用易于获得的手术内数据,开发了一个强大的术后AKI预测模型.
- perfusion 指数已经成为 AKI 的新且有价值的预测指标.
- 该模型可以通过Web应用程序访问,显示出临床应用的希望,等待进一步验证.
相关概念视频
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