Huang Jingying, Chen Jiaojiao, Yang Jin, Han Mengbo, Xue Zihao, Wang Yina, Xu Miaomiao, Qi Haiou, Wang Yuting
Operating Room, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Orthopaedics Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Intensive Crit Care Nurs. 2025 Feb;86:103808. doi: 10.1016/j.iccn.2024.103808. Epub 2024 Aug 28.
This study aims to systematically review and critical evaluation of the risk of bias and the applicability of existing prediction models for acute kidney injury post liver transplantation.
A comprehensive literature search up until February 7, 2024, was conducted across nine databases: PubMed, Web of Science, EBSCO CINAHL Plus, Embase, Cochrane Library, CNKI, Wanfang, CBM, and VIP.
Systematic review of observational studies.
Literature screening and data extraction were independently conducted by two researchers using a standardized checklist designed for the critical appraisal of prediction modelling studies in systematic reviews. The prediction model risk of bias assessment tool was utilized to assess both the risk of bias and the models' applicability.
Thirty studies were included, identifying 34 prediction models. External validation was conducted in seven studies, while internal validation exclusively took place in eight studies. Three models were subjected to both internal and external validation, the area under the curve ranging from 0.610 to 0.921. A meta-analysis of high-frequency predictors identified several statistically significant factors, including recipient body mass index, Model for End-stage Liver Disease score, preoperative albumin levels, international normalized ratio, and surgical-related factors such as cold ischemia time. All studies were demonstrated a high risk of bias, mainly due to the use of unsuitable data sources and inadequate detail in the analysis reporting.
The evaluation with prediction model risk of bias assessment tool indicated a considerable bias risk in current predictive models for acute kidney injury post liver transplantation.
The recognition of high bias in existing models calls for future research to employ rigorous methodologies and robust data sources, aiming to develop and validate more accurate and clinically applicable predictive models for acute kidney injury post liver transplantation.
本研究旨在系统评价和批判性评估肝移植后急性肾损伤现有预测模型的偏倚风险及适用性。
截至2024年2月7日，在九个数据库中进行了全面的文献检索：PubMed、科学网、EBSCO CINAHL Plus、Embase、Cochrane图书馆、中国知网、万方、中国生物医学文献数据库和维普。
观察性研究的系统评价。
由两名研究人员使用为系统评价中预测模型研究的批判性评价设计的标准化清单独立进行文献筛选和数据提取。采用预测模型偏倚风险评估工具评估偏倚风险和模型的适用性。
纳入30项研究，识别出34个预测模型。7项研究进行了外部验证，8项研究仅进行了内部验证。3个模型同时进行了内部和外部验证，曲线下面积范围为0.610至0.921。对高频预测因素的荟萃分析确定了几个具有统计学意义的因素，包括受者体重指数、终末期肝病模型评分、术前白蛋白水平、国际标准化比值以及冷缺血时间等手术相关因素。所有研究均显示出较高的偏倚风险，主要原因是使用了不合适的数据来源以及分析报告细节不足。
使用预测模型偏倚风险评估工具进行评估表明，当前肝移植后急性肾损伤预测模型存在相当大的偏倚风险。
认识到现有模型存在高度偏倚，要求未来研究采用严谨的方法和可靠的数据源，旨在开发和验证更准确且临床适用的肝移植后急性肾损伤预测模型。
