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