肾衰竭风险方程对透析前患者建立血管通路的最佳时机是否有作用?

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY Big Data & Society Pub Date : 2023-11-01 Epub Date: 2022-03-27 DOI:10.1177/11297298221084799
Külli Kuningas, Stephanie Stringer, Paul Cockwell, Aurangzaib Khawaja, Nicholas Inston
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引用次数: 1

摘要

背景:本研究旨在评估肾衰竭风险方程(KFRE)作为指导透析前患者建立血管通路时机的指标的实用性:本研究旨在评估使用肾衰竭风险方程(KFRE)作为指标来指导透析前患者建立血管通路时机的实用性:评估时计算转诊患者的 KFRE,并与转诊标准进行比较。为每个参数绘制了接收者操作特征曲线。以 3 个月、6 个月和 1 年的结果为时间点进行分析:共对 23 名患者进行了评估,其中 190 名患者(94.6%)获得了完整的数据集。156名患者(82.1%)建立了通路,153名患者(98.7%)建立了瘘管。只有 65.7% 的患者在随访期间开始透析。建立了动静脉通路的患者(156 人)中,有 37 人(23.7%)在整个随访期间没有达到末期阶段。其余达到终末期的患者(n = 119)中,72.2%(n = 86)开始使用 AVF/AVG,27.7%(n = 33)开始使用 CVC。通过对转诊 eGFR、ACR 和 KFRE 预测透析启动预测因子进行 ROC 分析,得出 eGFR、ACR 和 KFRE2 的 C 统计量分别为 0.68 (0.58-0.79)、0.75 (0.65-0.84) 和 0.72(0.62-0.81);6个月时分别为0.73(0.65-0.81)、0.70(0.62-0.78)和0.75(0.67-0.81);12个月时分别为0.65(0.57-0.72)、0.67(0.59-0.75)和0.68(0.61-0.77):结论:在一组因血管通路创建而转诊的患者中,应用于开始透析的预测模型相对较差。将目前的指南应用于建立瘘管似乎会导致不必要的瘘管形成率和未使用率居高不下。这项研究需要在一组测试患者中进行进一步评估,以确认这些发现,并确定在哪些方面需要修改这种基于风险的方法。
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Is there a role of the kidney failure risk equation in optimizing timing of vascular access creation in pre-dialysis patients?

Background: The aims of this study were to assess the utility of using the Kidney Failure Risk Equation (KFRE) as an indicator to guide timing of vascular access creation in pre-dialysis patients.

Materials and methods: Patients referred for vascular access creation had KFRE calculated at the time of assessment and compared to standard criteria for referral. Receiver operating characteristic curves were produced for each parameter. The outcomes at 3 months, 6 months, and 1 year were used as time points for analysis.

Results: Two hundred and three patients were assessed, and full data sets were available on 190 (94.6%). Access was created in 156 patients (82.1%) with a fistula in 153 (98.7%). Only 65.7% initiated dialysis within the follow up period. Those patients with an AV access created (n = 156) 37 (23.7%) did not reach end stage over the entire follow up period. Of the remaining patients (n = 119) that reached end stage 72.2% (n = 86) started on an AVF/AVG and 27.7% (n = 33) on a CVC. Using ROC analysis for referral eGFR, ACR and KFRE predicting dialysis initiation predictors resulted in C statistics for eGFR, ACR, and KFRE2 of 0.68 (0.58-0.79), 0.75 (0.65-0.84), and 0.72 (0.62-0.81) at 3 months; 0.73 (0.65-0.81), 0.70 (0.62-0.78), and 0.75 (0.67-0.81) at 6 months; and 0.65 (0.57-0.72); 0.67 (0.59-0.75), and 0.68 (0.61-0.77) at 12 months.

Conclusions: In a group of patients referred for vascular access creation the predictive models are relatively poor when applied to initiation of dialysis. The application of current guidelines to fistula creation appears to result in a high rate of unnecessary fistula formation and non-use. The study requires further evaluation in a test set of patients to confirm these findings and also identify where such risk based approaches may need modification.

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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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