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Aortic and vascular disease
Original research article
Bedside risk score for prediction of acute kidney injury after transcatheter aortic valve replacement
  1. Nevena Zivkovic1,
  2. Gabby Elbaz-Greener1,
  3. Feng Qiu2,
  4. Yaron Arbel3,
  5. Asim N Cheema4,
  6. Danny Dvir5,
  7. Paul Fefer6,
  8. Ariel Finkelstein3,
  9. Stephen E Fremes1,
  10. Sam Radhakrishnan1,
  11. Josep Rodés-Cabau7,
  12. Mony Shuvy8 and
  13. Harindra C Wijeysundera1,2,9
  1. 1 Schulich Heart Centre, Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
  2. 2 Institute for Clinical Evaluative Sciences (ICES), Toronto, Ontario, Canada
  3. 3 Department of Cardiology, Tel Aviv Medical Center, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
  4. 4 Division of Cardiology, St. Michael’s Hospital, Toronto, Ontario, Canada
  5. 5 Division of Cardiology, St Paul’s Hospital, Vancouver, British Columbia, Canada
  6. 6 Heart Institute, Sheba Medical Center, Tel HaShomer, Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
  7. 7 Quebec Heart and Lung Institute, Laval University, Quebec City, Quebec, Canada
  8. 8 Heart Institute, Hadassah Hebrew University Medical Centre, Jerusalem, Israel
  9. 9 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
  1. Correspondence to Dr Harindra C Wijeysundera; Harindra.wijeysundera{at}sunnybrook.ca

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.

  • tavr
  • aortic stenosis
  • aortic regurgitation
  • acute kidney injury

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

https://doi.org/10.1136/openhrt-2018-000777

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    Footnotes

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient consent Not required.

    • Ethics approval Sunnybrook Hospital, St Michael’s Hospital, Quebec Heart and Lung Institute, Tel Aviv Medical Centre, Hadassah Hebrew University Medical Centre and Sheba Medical Center.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement No additional data are available.