在开始CRRT后,使用机器学习预测急性损伤患者的短期和长期死亡风险
Menglei Gu1, Yalan Liu1, Hongbin Sun1
1Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
BMC nephrology
|July 30, 2024
概括
机器学习模型可以预测急性损伤 (AKI) 患者的短期和长期死亡率,这些患者接受连续置换疗法 (CRRT). 随机森林模型显示了预测10天和90天死亡的最佳性能.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 关键护理医学 关键护理医学
- 数据科学数据科学数据科学
背景情况:
- 接受连续置换疗法 (CRRT) 的急性损伤 (AKI) 患者的死亡率和预后各不相同.
- 对短期和长期死亡率进行准确的风险分层对于接受CRRT的AKI患者的临床决策至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测接受CRRT的AKI患者的短期 (10天) 和长期 (90天) 死亡率.
- 确定影响该患者队列死亡率的关键预后因素.
主要方法:
- 使用了1535名接受CRRT的AKI患者的数据集,其中1144人来自Dryad数据库 (培训) 和391人来自MIMIC IV (验证).
- 开发并比较了9个ML算法用于死亡率预测.
- 使用AUROC,F1得分,精度,灵敏度,特异性和校准曲线评估模型性能.
主要成果:
- 10天内总死亡率为31.1% (420人死亡),90天内为68.8% (1080人死亡).
- 随机森林 (RF) 模型在10天 (AUROC:0.80) 和90天死亡率 (AUROC:0.78) 两方面都表现出优异的预测性能.
- SOFA评分被确定为短期和长期死亡率的最重要的风险因素.
结论:
- 机器学习模型可以有效地估计CRRT的AKI患者的短期和长期死亡风险.
- 预后因素在短期和长期死亡率之间有所不同.
- 射频模型显示了临床应用在风险分层中的重大潜力.
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