在补偿的晚期慢性肝病中,第一个失补偿的非侵入性预测模型:元分析
Angus W Jeffrey1, James Chen2, Andrew Chin3
1Department of Medicine, University of Western Australia, Perth, Australia; Department of Hepatology, Sir Charles Gairdner Hospital, Perth, Australia; Liver Transplant Unit, Austin Hospital, Melbourne, Australia.
概括
非侵入性预测模型 (NIT) 准确地识别了处于脱补偿风险的补偿晚期慢性肝病 (cACLD) 患者. cACLD特定的模型,如SAVE和ABC分数,显示出最好的预测性能.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 内部医学 内部医学
- 临床预测模型临床预测模型
背景情况:
- 补偿性晚期慢性肝病 (cACLD) 具有严重的肝失补偿风险.
- 准确识别高风险患者对于及时干预至关重要.
研究的目的:
- 批判性地评估非侵入性预测模型 (NITs),用于识别患有肝衰竭风险增加的cACLD患者.
- 确定现有NIT的准确性和可靠性.
主要方法:
- 在2025年2月之前,对已发表的文章进行系统审查和元分析.
- 包括的研究评估了结合至少两个非侵入性标志物的预后模型.
- 提取的数据包括C统计,AUC和模型校准.
主要成果:
- 包括30项与47647名参与者的研究,评估了39个预后模型.
- 在cACLD中专门开发和验证的模型,如SAVE (C-统计=0.87) 和ABC分数 (C-统计=0.85),显示出优异的预测.
- 在大多数模型中都注意到有限的验证和校准.
结论:
- cACLD特定的NIT是预测脱补偿的首选选择.
- 未来的研究必须优先考虑强大的验证,校准和外部验证,以标准化临床可靠性的终点.
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