Bedside risk score for prediction of acute kidney injury after transcatheter aortic valve replacement. Issue 1 (30th May 2018)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Open heart. Volume 5:Issue 1(2018)
• Publication Date: 2018-05-30
• Subjects: tavr -- aortic stenosis -- aortic regurgitation -- acute kidney injury; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; Heart -- Diseases -- Patients -- Periodicals; 616.12005
• DOI: 10.1136/openhrt-2018-000777
• Languages: English
• ISSNs: 2398-595X

Abstract

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.