动态肌素累积暴露的预测值和严重急性胰腺炎死亡率的轨迹分类:一个多中心的回顾性队列研究
Jianhua Wan1, Yaoyu Zou1, Maobin Kuang1
1Department of Gastroenterology, Jiangxi Provincial Key Laboratory of Digestive Diseases, Jiangxi Clinical Research Center for Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
累积的肌素暴露和动态轨迹预测严重急性胰腺炎 (SAP) 死亡率. 机器学习模型增强了高风险SAP患者的早期识别,改善了预后.
科学领域:
- 腎病學和重症醫療醫學 腎病學和重症醫學
- 生物统计学和机器学习
- 临床预后 临床预后
背景情况:
- 严重的急性胰腺炎 (SAP) 具有显著的死亡风险.
- 早期和准确的预后对于有效的患者管理至关重要.
- 当前的预后工具在识别高风险SAP患者时可能缺乏准确性.
研究的目的:
- 评估累积肌素暴露 (CumCr) 和SAP预后的动态肌素轨迹的预测值.
- 为SAP开发基于机器学习的风险分层模型.
主要方法:
- 国际多中心回顾性队列研究 (南队列和MIMIC-IV数据库).
- 在前7天内计算累积肌素暴露量 (CumCr).
- 潜在类生长建模 (LCGM) 对于肌素轨迹模式.
- 波鲁塔算法和LASSO回归用于变量选择和名ogram构造.
主要成果:
- 确定了CumCr和死亡率之间的非线性关联,具有值效应.
- 通过LCGM观察到四种不同的肌素轨迹模式.
- 机器学习名录 (综合年龄,心率,,肌素轨迹) 的AUC为0.79,超过了APACHE II (0.68) 和SIRS (0.58) 的AUC.
结论:
- 动态肌素监测,包括累积暴露和轨迹模式,显著提高高风险SAP患者的早期识别.
- 开发的机器学习模型为SAP提供了更好的预测准确性.
- 这种方法促进了及时的临床干预,并可能改善SAP结果.
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