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经导管主动脉瓣置换术后急性肾损伤预测的床边风险评分

Bedside risk score for prediction of acute kidney injury after transcatheter aortic valve replacement.

作者信息

Zivkovic Nevena, Elbaz-Greener Gabby, Qiu Feng, Arbel Yaron, Cheema Asim N, Dvir Danny, Fefer Paul, Finkelstein Ariel, Fremes Stephen E, Radhakrishnan Sam, Rodés-Cabau Josep, Shuvy Mony, Wijeysundera Harindra C

机构信息

Schulich Heart Centre, Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada.

出版信息

Open Heart. 2018 May 30;5(1):e000777. doi: 10.1136/openhrt-2018-000777. eCollection 2018.

Abstract

BACKGROUND

Acute kidney injury (AKI) is a common post-transcatheter aortic valve replacement (TAVR) complication associated with a poor prognosis. We sought to create a risk calculator using information that would be available during the work-up period.

METHODS

Data were obtained from a multicentre TAVR registry (n=1993) with cases from 1 January 2012 to 31 December 2015. We used logistic regression to create a risk calculator to predict AKI as defined by the Valve Academic Research Consortium Guidelines. We internally validated our risk calculator using bootstrapping, and evaluated model discrimination and calibration.

RESULTS

A simple risk score was derived with six variables, including New York Heart Association functional classification class 4, non-femoral access site, valve-in-valve procedure, haemoglobin, creatinine clearance and weight in kilograms. The score was able to predict the absolute risk of AKI from 1% to 72%. The model showed good discrimination with c-statistic 0.713, with good agreement between predicted and observed AKI rates across quintiles of risk.

CONCLUSIONS

This is the first risk calculator to assess post-TAVR risk of AKI. We found that information known pre-procedurally can be used to predict AKI. This may allow for more informed decision-making as well as identifying high-risk patients.

摘要

背景

急性肾损伤(AKI)是经导管主动脉瓣置换术(TAVR)后常见的并发症,与预后不良相关。我们试图利用检查期间可获得的信息创建一个风险计算器。

方法

数据来自一个多中心TAVR注册库(n = 1993),病例时间为2012年1月1日至2015年12月31日。我们使用逻辑回归创建一个风险计算器,以预测瓣膜学术研究联盟指南所定义的AKI。我们使用自抽样法对风险计算器进行内部验证,并评估模型的辨别力和校准情况。

结果

得出了一个包含六个变量的简单风险评分,包括纽约心脏协会功能分级4级、非股动脉入路部位、瓣中瓣手术、血红蛋白、肌酐清除率和体重(千克)。该评分能够预测AKI的绝对风险,范围从1%到72%。该模型显示出良好的辨别力,c统计量为0.713,在风险五分位数中预测的和观察到的AKI发生率之间具有良好的一致性。

结论

这是第一个评估TAVR术后AKI风险的风险计算器。我们发现术前已知的信息可用于预测AKI。这可能有助于做出更明智的决策,并识别高危患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7122/5976119/2a717454e8ab/openhrt-2018-000777f01.jpg

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