Prediction models for liver decompensation in compensated advanced chronic liver disease: A systematic review.

However, predicting decompensation is challenging, and multiple models have been developed.

然而，预测失代偿是具有挑战性的，已经开发了多种模型。

This study systematically appraises the performance and clinical applications of published multivariable models predicting first decompensation in patients with cACLD or compensated cirrhosis.

本研究系统评估了已发表的多变量模型在预测cACLD或代偿期肝硬化患者首次失代偿方面的性能和临床应用。

approach_and_results

We searched MEDLINE for liver decompensation prediction models from inception to December 2023.

我们检索了MEDLINE数据库，从建库至2023年12月，寻找预测肝失代偿的模型。

The research was registered with PROSPERO (CRD42023488395).

该研究已在PROSPERO注册（CRD42023488395）。

Model risk of bias and applicability were assessed using the Prediction study Risk of Bias Assessment Tool (PROBAST), with results summarized via narrative synthesis.

使用预测研究风险偏倚评估工具（PROBAST）评估了模型的风险偏倚和适用性，并通过叙述性综合对结果进行了总结。

Reporting followed Preferred Reporting Items for Systematic Reviews and Meta-Analysisand Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies guidelines.

报告遵循了系统评价和荟萃分析的首选报告项目以及预测模型系统评价的批判性评价和数据提取清单指南。

Sixteen studies (retrospective and prospective) were included.

纳入了16项研究（包括回顾性和前瞻性研究）。

Seven focused on a single etiology.

七项研究集中在单一病因上。

No study specifically predicted outcomes in persons with alcohol-associated liver disease.

没有研究专门预测了患有酒精相关性肝病的人群的结果。

Outcome definitions varied, with some models predicting HCC together with decompensation.

结局定义各不相同，一些模型预测了肝细胞癌（HCC）与失代偿性肝病的共同发生。

In total, 27 predictors were included in the models.

总的来说，模型中包含了27个预测因素。

The most frequent predictors were albumin, platelets, age, liver stiffness, bilirubin, international normalized ratio, and the presence of portal hypertension-related findings during upper gastrointestinal endoscopy.

最常见的预测因素包括白蛋白、血小板、年龄、肝硬度、胆红素、国际标准化比率以及上消化道内镜检查时门静脉高压相关发现的存在。

All studies reported discrimination measures, but only 10/16 evaluated calibration.

所有研究都报告了区分度指标，但只有10/16评估了校准。

External validation was conducted in 9/16 studies.

在16项研究中有9项进行了外部验证。

Thirteen studies were rated as having a high overall risk of bias.

有13项研究被评为具有高总体偏倚风险。

Conclusions

For clinical utility, a predictive model must accurately describe future risks.

为了临床实用性，预测模型必须准确描述未来的风险。

Models for predicting decompensation in cACLD are often poorly described, infrequently include patients with ArLD, and lack external validation.

预测慢性失代偿性肝病（cACLD）恶化的模型通常描述不充分，很少包括酒精性肝病（ArLD）患者，并且缺乏外部验证。

These factors are barriers to the clinical utility and uptake of predictive models for first decompensation in patients with cACLD.

这些因素是阻碍预测模型在cACLD患者首次失代偿中临床应用和采纳的障碍。