Abstract：
UNASSIGNED: Failure-to-mature and early stenosis remains the Achille\'s heel of hemodialysis arteriovenous fistula (AVF) creation. The maturation and patency of an AVF can be influenced by a variety of demographic, comorbidity, and anatomical factors. This study aims to review the prediction models of AVF maturation and patency with various risk scores and machine learning models.
UNASSIGNED: Literature search was performed on PubMed, Scopus, and Embase to identify eligible articles. The quality of the studies was assessed using the Prediction model Risk Of Bias ASsessment (PROBAST) Tool. The performance (discrimination and calibration) of the included studies were extracted.
UNASSIGNED: Fourteen studies (seven studies used risk score approaches; seven studies used machine learning approaches) were included in the review. Among them, 12 studies were rated as high or unclear \"risk of bias.\" Six studies were rated as high concern or unclear for \"applicability.\" C-statistics (Model discrimination metric) was reported in five studies using risk score approach (0.70-0.886) and three utilized machine learning methods (0.80-0.85). Model calibration was reported in three studies. Failure-to-mature risk score developed by one of the studies has been externally validated in three different patient populations, however the model discrimination degraded significantly (C-statistics: 0.519-0.53).
UNASSIGNED: The performance of existing predictive models for AVF maturation/patency is underreported. They showed satisfactory performance in their own study population. However, there was high risk of bias in methodology used to build some of the models. The reviewed models also lack external validation or had reduced performance in external cohort.
UNASSIGNED: Literature search was performed on PubMed, Scopus, and Embase to identify eligible articles. The quality of the studies was assessed using the Prediction model Risk Of Bias ASsessment (PROBAST) Tool. The performance (discrimination and calibration) of the included studies were extracted.
UNASSIGNED: Fourteen studies (seven studies used risk score approaches; seven studies used machine learning approaches) were included in the review. Among them, 12 studies were rated as high or unclear \"risk of bias.\" Six studies were rated as high concern or unclear for \"applicability.\" C-statistics (Model discrimination metric) was reported in five studies using risk score approach (0.70-0.886) and three utilized machine learning methods (0.80-0.85). Model calibration was reported in three studies. Failure-to-mature risk score developed by one of the studies has been externally validated in three different patient populations, however the model discrimination degraded significantly (C-statistics: 0.519-0.53).
UNASSIGNED: The performance of existing predictive models for AVF maturation/patency is underreported. They showed satisfactory performance in their own study population. However, there was high risk of bias in methodology used to build some of the models. The reviewed models also lack external validation or had reduced performance in external cohort.
摘要：
■未能成熟和早期狭窄仍然是Achille血液透析动静脉瘘（AVF）产生的足跟。AVF的成熟和通畅性可能受到各种人口统计学的影响，合并症，和解剖学因素。本研究旨在回顾具有各种风险评分和机器学习模型的AVF成熟和通畅的预测模型。
■文献检索在PubMed上进行，Scopus,和Embase来识别合格的文章。使用偏见风险评估预测模型(PROBAST)工具评估研究的质量。提取了纳入研究的性能(辨别和校准)。
■14项研究（7项研究使用风险评分方法；7项研究使用机器学习方法）被纳入综述。其中,12项研究被评为偏倚风险高或不清楚。“六项研究被评为高度关注或适用性不明确。在5项使用风险评分方法（0.70-0.886）和3项使用机器学习方法（0.80-0.85）的研究中报告了C统计（模型判别度量）。在三项研究中报告了模型校准。其中一项研究开发的失败到成熟的风险评分已在三个不同的患者人群中进行了外部验证，然而，模型判别显着下降(C统计量：0.519-0.53)。
■AVF成熟/通畅性的现有预测模型的性能被低估。他们在自己的研究人群中表现出令人满意的表现。然而,用于建立一些模型的方法存在较高的偏差风险.审查的模型也缺乏外部验证或在外部队列中表现降低。
■文献检索在PubMed上进行，Scopus,和Embase来识别合格的文章。使用偏见风险评估预测模型(PROBAST)工具评估研究的质量。提取了纳入研究的性能(辨别和校准)。
■14项研究（7项研究使用风险评分方法；7项研究使用机器学习方法）被纳入综述。其中,12项研究被评为偏倚风险高或不清楚。“六项研究被评为高度关注或适用性不明确。在5项使用风险评分方法（0.70-0.886）和3项使用机器学习方法（0.80-0.85）的研究中报告了C统计（模型判别度量）。在三项研究中报告了模型校准。其中一项研究开发的失败到成熟的风险评分已在三个不同的患者人群中进行了外部验证，然而，模型判别显着下降(C统计量：0.519-0.53)。
■AVF成熟/通畅性的现有预测模型的性能被低估。他们在自己的研究人群中表现出令人满意的表现。然而,用于建立一些模型的方法存在较高的偏差风险.审查的模型也缺乏外部验证或在外部队列中表现降低。
