Külli Kuningas, Stephanie Stringer, Paul Cockwell, Aurangzaib Khawaja, Nicholas Inston
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引用次数: 1
Abstract
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.
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.