预测腹腔大动脉动脉瘤患者的ICU死亡率:基于MIMIC-IV和eICU-CRD的诺莫图
Mengwei He1, Xiang Zhang1, Weixue Huo1
1Department of Vascular Surgery, Intervention Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China.
International journal of medical sciences
|December 16, 2025
概括
这项研究开发了一种机器学习名录,用于预测腹腔大动脉动脉瘤 (AAA) 严重病患者的早期死亡率. 该工具在7天内准确识别高风险患者,帮助做出关键护理决策.
科学领域:
- 关键护理医学 关键护理医学
- 心血管外科心血管外科
- 医疗保健中的机器学习
背景情况:
- 腹腔大动脉瘤 (AAA) 是重症监护室 (ICU) 的一个危及生命的疾病.
- 现有的ICU评分系统无法预测AAA患者的死亡风险.
- 需要改进预后工具,以便在AAA中进行早期风险分层.
研究的目的:
- 开发和验证一种基于机器学习的名谱,用于预测重症AAA患者的短期死亡率 (≤7天).
- 整合AAA特定的预测因素,克服传统评分系统的局限性.
- 通过使用独立数据集进行外部验证来确保可通用性.
主要方法:
- 利用两个大型ICU数据库 (MIMIC-IV和eICU-CRD) 进行模型开发和外部验证.
- 采用LASSO回归和SVM-RFE进行关键预测器选择.
- 构建并验证了基于考克斯回归的7日,14日和28日死亡率预测的诺姆图.
主要成果:
- 确定了六个关键预测因素:年龄,败血症,BUN,抗高血压药的使用,SpO2和离子间隙.
- 诺米图表显示了7天死亡率的最佳预测准确性 (AUC: 0.730培训,0.718验证).
- 外部验证证实了模型的概括性 (7天AUC:0.723),具有强大的校准和临床效用.
结论:
- 一种新型的诺米图有效地预测了重症AAA患者的早期死亡率 (≤7天).
- 该工具提供了一种可靠的方法,用于在重症监护机构的风险分层.
- 诺米图可以指导及时干预,以改善患者的治疗结果.
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