在急性至慢性肝衰竭和两个或更多器官衰竭的ICU患者中预测机器学习模型
Yee Hui Yeo1, Mengyi Zhang2, Martin S McCoy3
1Karsh Division of Gastroenterology and Hepatology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Clinical and molecular hepatology
|September 1, 2025
概括
机器学习模型准确地预测了多器官衰竭的急性至慢性肝衰竭患者的短期死亡率. 这些可解释的模型优于现有的评分系统,有助于临床管理.
科学领域:
- 危急护理医学
- 在医疗保健中的机器学习
- 肝衰竭研究
背景情况:
- 急性至慢性肝衰竭 (ACLF) 是一种具有高短期死亡率的危急疾病.
- 在重症监护室 (ICU) 中有效管理ACLF患者需要准确的预后工具.
- 预测两种或多种器官衰竭的ACLF患者的死亡率对于及时干预至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测两个或两个以上OF的ACLF患者的30天死亡率.
- 使用Shapley值来解释开发的ML模型,以提高可解释性.
- 将ML模型的性能与已建立的评分系统进行比较.
主要方法:
- 使用大量的ICU队列和详细的临床数据来识别符合NACSELD和EASL- CLIF标准的ACLF患者.
- 开发和训练了CatBoost和随机森林ML模型来预测30天的死亡率.
- 采用SHAP分析来解释模型和确定关键预测因素. 这些模型在内部和外部进行了验证.
主要成果:
- 在NACSELD队列中,CatBoost ACLF (CBA) 模型获得了0. 87的AUC,在EASL-CLIF队列中,随机森林ACLF (RFA) 模型获得了0. 83的AUC.
- 与CLIF- C ACLF和MELD 3.0相比,简化的模型显示出强大的性能 (CBA:AUC 0.89,RFA:AUC 0.81) 和更高的准确性.
- 模型进行了精确校准,在外部队伍中进行了验证,并开发了一个在线预测工具.
结论:
- 开发了可解释,验证和校准的ML模型,用于预测多次OF的ACLF患者的死亡率.
- 这些新型ML模型显著优于现有的评分系统.
- 这些模型和相关的在线工具可以帮助临床医生管理高风险的ACLF患者.
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