Background

Few reports have evaluated prognostic modelling studies of tools used for surgical decision-making. This systematic review aimed to describe and critically appraise studies that have developed or validated multivariable prognostic models for post-operative liver decompensation following partial hepatectomy.

Methods

This study was designed using the CHARMS checklist. Following a comprehensive literature search, two reviewers independently screened candidate references for inclusion and abstracted relevant study details. Qualitative assessment was performed using the PROBAST tool.

Results

We identified 36 prognostic modelling studies; 25 focused on development only, 3 developed and validated models, and 8 validated pre-existing models. None compared routine use of a prognostic model against standard clinical practice. Most studies used single-institution, retrospective cohort designs, conducted in Eastern populations. In total, 15 different outcome definitions for post-operative liver decompensation events were used. Statistical concerns surrounding model overfitting, performance assessment, and internal validation led to high risk of bias for all studies.

Conclusions

Current prognostic models for post-operative liver decompensation following partial hepatectomy may not be valid for routine clinical use due to design and methodologic concerns. Landmark resources and reporting guidelines such as the TRIPOD statement may assist researchers, and additionally, model impact assessment studies represent opportunities for future research.

中文翻译：

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部分肝切除术后肝功能失代偿的多变量预后模型评价：系统评价

背景

很少有报告评估用于手术决策的工具的预后建模研究。本系统评价旨在描述和批判性评价已经开发或验证了部分肝切除术后肝失代偿的多变量预后模型的研究。

方法

本研究是使用 CHARMS 清单设计的。在进行了全面的文献检索后，两名审稿人独立筛选了候选参考文献，并提取了相关的研究细节。使用 PROBAST 工具进行定性评估。

结果

我们确定了 36 项预后模型研究；25 个仅专注于开发，3 个已开发和验证的模型，以及 8 个经过验证的现有模型。没有人将预后模型的常规使用与标准临床实践进行比较。大多数研究使用在东方人群中进行的单机构、回顾性队列设计。总共使用了 15 种不同的术后肝脏失代偿事件结果定义。围绕模型过度拟合、性能评估和内部验证的统计问题导致所有研究存在高偏倚风险。

结论

由于设计和方法学问题，目前部分肝切除术后肝失代偿的预后模型可能不适用于常规临床使用。具有里程碑意义的资源和报告指南（例如 TRIPOD 声明）可以帮助研究人员，此外，模型影响评估研究代表了未来研究的机会。