预测酒精相关肝硬化严重功能障碍:肝功能评分和机器学习模型的比较性能
Julian Müller-Kühnle1,2,3, Moritz Schanz1, Severin Schricker1
1Department of General Internal Medicine and Nephrology, Robert Bosch Hospital, Stuttgart, Germany.
PloS one
|September 17, 2025
概括
在酒精性肝硬化患者中,MELD得分有效地识别了晚期功能障碍. 使用临床数据的机器学习模型进一步提高了预测准确度,从而实现更好的风险分层.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 腎臟病學 (nephrology) 是一種醫學.
- 医疗信息学 医疗信息学
背景情况:
- 功能障碍和慢性病 (CKD) 在肝硬化中很常见,但往往被诊断不足.
- 早期识别患有严重功能障碍风险的患者对于及时干预至关重要.
- 像MELD,Child-Pugh,APRI和FIB-4这样的常规得分对肝硬化中晚期功能障碍的预测价值不确定.
研究的目的:
- 评估MELD,Child-Pugh评分 (CPS),APRI和FIB-4在酒精性肝硬化患者中严重功能障碍 (CKD阶段≥3) 的预测能力.
- 开发和评估机器学习 (ML) 模型,以确定该人群中晚期CKD的非脏预测因素.
主要方法:
- 对131名酒精性肝硬化患者 (2014-2021) 的回顾性队列研究.
- 对MELD,CPS,APRI和FIB-4进行分析,以预测CKD阶段≥3 (KDIGO分类).
- 使用了逻辑/线性回归和机器学习 (随机森林) 模型.
主要成果:
- 33%的患者患有CKD阶段≥3.3.
- MELD得分显著预测了晚期CKD (OR=1.379,p<0.001),患病率从17% (MELD ≤9) 增加到80% (MELD ≥20).
- 一个优化的随机森林ML模型实现了75.7%的AUC,76%的精度,82%的灵敏度和63%的特异性.
结论:
- MELD是检测酒精性肝硬化中晚期功能障碍的最可靠的常规评分.
- 使用常规临床参数的ML模型提高了CKD风险分层的预测性能.
- 这些发现支持在高风险肝硬化患者中改善功能障碍的识别和管理.
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