Neesh Pannu1, Michelle Graham2, Scott Klarenbach2, Steven Meyer2, Teresa Kieser2, Brenda Hemmelgarn2, Feng Ye2, Matthew James2. 1. Department of Medicine (Pannu, Graham, Klarenbach, Ye), Division of Critical Care Medicine (Pannu), Division of Cardiac Surgery, Department of Surgery (Meyer), University of Alberta, Edmonton, Alta.; Division of Cardiac Surgery, Department of Surgery (Kieser), Department of Medicine (Hemmelgarn, James), Department of Community Health Sciences (Hemmelgarn, James), University of Calgary, Calgary, Alta.; Institute of Health Economics (Klarenbach), Edmonton, Alta. npannu@ualberta.ca. 2. Department of Medicine (Pannu, Graham, Klarenbach, Ye), Division of Critical Care Medicine (Pannu), Division of Cardiac Surgery, Department of Surgery (Meyer), University of Alberta, Edmonton, Alta.; Division of Cardiac Surgery, Department of Surgery (Kieser), Department of Medicine (Hemmelgarn, James), Department of Community Health Sciences (Hemmelgarn, James), University of Calgary, Calgary, Alta.; Institute of Health Economics (Klarenbach), Edmonton, Alta.
Abstract
BACKGROUND: Acute kidney injury after cardiac surgery is associated with adverse in-hospital and long-term outcomes. Novel risk factors for acute kidney injury have been identified, but it is unknown whether their incorporation into risk models substantially improves prediction of postoperative acute kidney injury requiring renal replacement therapy. METHODS: We developed and validated a risk prediction model for acute kidney injury requiring renal replacement therapy within 14 days after cardiac surgery. We used demographic, and preoperative clinical and laboratory data from 2 independent cohorts of adults who underwent cardiac surgery (excluding transplantation) between Jan. 1, 2004, and Mar. 31, 2009. We developed the risk prediction model using multivariable logistic regression and compared it with existing models based on the C statistic, Hosmer-Lemeshow goodness-of-fit test and Net Reclassification Improvement index. RESULTS: We identified 8 independent predictors of acute kidney injury requiring renal replacement therapy in the derivation model (adjusted odds ratio, 95% confidence interval [CI]): congestive heart failure (3.03, 2.00-4.58), Canadian Cardiovascular Society angina class III or higher (1.66, 1.15-2.40), diabetes mellitus (1.61, 1.12-2.31), baseline estimated glomerular filtration rate (0.96, 0.95-0.97), increasing hemoglobin concentration (0.85, 0.77-0.93), proteinuria (1.65, 1.07-2.54), coronary artery bypass graft (CABG) plus valve surgery (v. CABG only, 1.25, 0.64-2.43), other cardiac procedure (v. CABG only, 3.11, 2.12-4.58) and emergent status for surgery booking (4.63, 2.61-8.21). The 8-variable risk prediction model had excellent performance characteristics in the validation cohort (C statistic 0.83, 95% CI 0.79-0.86). The net reclassification improvement with the prediction model was 13.9% (p < 0.001) compared with the best existing risk prediction model (Cleveland Clinic Score). INTERPRETATION: We have developed and validated a practical and accurate risk prediction model for acute kidney injury requiring renal replacement therapy after cardiac surgery based on routinely available preoperative clinical and laboratory data. The prediction model can be easily applied at the bedside and provides a simple and interpretable estimation of risk.
BACKGROUND:Acute kidney injury after cardiac surgery is associated with adverse in-hospital and long-term outcomes. Novel risk factors for acute kidney injury have been identified, but it is unknown whether their incorporation into risk models substantially improves prediction of postoperative acute kidney injury requiring renal replacement therapy. METHODS: We developed and validated a risk prediction model for acute kidney injury requiring renal replacement therapy within 14 days after cardiac surgery. We used demographic, and preoperative clinical and laboratory data from 2 independent cohorts of adults who underwent cardiac surgery (excluding transplantation) between Jan. 1, 2004, and Mar. 31, 2009. We developed the risk prediction model using multivariable logistic regression and compared it with existing models based on the C statistic, Hosmer-Lemeshow goodness-of-fit test and Net Reclassification Improvement index. RESULTS: We identified 8 independent predictors of acute kidney injury requiring renal replacement therapy in the derivation model (adjusted odds ratio, 95% confidence interval [CI]): congestive heart failure (3.03, 2.00-4.58), Canadian Cardiovascular Society angina class III or higher (1.66, 1.15-2.40), diabetes mellitus (1.61, 1.12-2.31), baseline estimated glomerular filtration rate (0.96, 0.95-0.97), increasing hemoglobin concentration (0.85, 0.77-0.93), proteinuria (1.65, 1.07-2.54), coronary artery bypass graft (CABG) plus valve surgery (v. CABG only, 1.25, 0.64-2.43), other cardiac procedure (v. CABG only, 3.11, 2.12-4.58) and emergent status for surgery booking (4.63, 2.61-8.21). The 8-variable risk prediction model had excellent performance characteristics in the validation cohort (C statistic 0.83, 95% CI 0.79-0.86). The net reclassification improvement with the prediction model was 13.9% (p < 0.001) compared with the best existing risk prediction model (Cleveland Clinic Score). INTERPRETATION: We have developed and validated a practical and accurate risk prediction model for acute kidney injury requiring renal replacement therapy after cardiac surgery based on routinely available preoperative clinical and laboratory data. The prediction model can be easily applied at the bedside and provides a simple and interpretable estimation of risk.
Authors: Sushrut S Waikar; Ron Wald; Glenn M Chertow; Gary C Curhan; Wolfgang C Winkelmayer; Orfeas Liangos; Marie-Anne Sosa; Bertrand L Jaber Journal: J Am Soc Nephrol Date: 2006-04-26 Impact factor: 10.121
Authors: Steven G Coca; Divakar Jammalamadaka; Kyaw Sint; Heather Thiessen Philbrook; Michael G Shlipak; Michael Zappitelli; Prasad Devarajan; Sabet Hashim; Amit X Garg; Chirag R Parikh Journal: J Thorac Cardiovasc Surg Date: 2011-11-03 Impact factor: 5.209
Authors: Charuhas V Thakar; Susana Arrigain; Sarah Worley; Jean-Pierre Yared; Emil P Paganini Journal: J Am Soc Nephrol Date: 2004-11-24 Impact factor: 10.121
Authors: Rajendra H Mehta; Joshua D Grab; Sean M O'Brien; Charles R Bridges; James S Gammie; Constance K Haan; T Bruce Ferguson; Eric D Peterson Journal: Circulation Date: 2006-11-06 Impact factor: 29.690
Authors: Harry Hemingway; Natalie K Fitzpatrick; Shamini Gnani; Gene Feder; Neil Walker; Angela M Crook; Patrick Magee; Adam Timmis Journal: Can J Cardiol Date: 2004-03-01 Impact factor: 5.223
Authors: Andrew S Levey; Lesley A Stevens; Christopher H Schmid; Yaping Lucy Zhang; Alejandro F Castro; Harold I Feldman; John W Kusek; Paul Eggers; Frederick Van Lente; Tom Greene; Josef Coresh Journal: Ann Intern Med Date: 2009-05-05 Impact factor: 25.391
Authors: Yaron Arbel; Valentin Fuster; Usman Baber; Taye H Hamza; F S Siami; Michael E Farkouh Journal: Int J Cardiol Date: 2019-06-13 Impact factor: 4.164
Authors: Mitra K Nadim; Lui G Forni; Azra Bihorac; Charles Hobson; Jay L Koyner; Andrew Shaw; George J Arnaoutakis; Xiaoqiang Ding; Daniel T Engelman; Hrvoje Gasparovic; Vladimir Gasparovic; Charles A Herzog; Kianoush Kashani; Nevin Katz; Kathleen D Liu; Ravindra L Mehta; Marlies Ostermann; Neesh Pannu; Peter Pickkers; Susanna Price; Zaccaria Ricci; Jeffrey B Rich; Lokeswara R Sajja; Fred A Weaver; Alexander Zarbock; Claudio Ronco; John A Kellum Journal: J Am Heart Assoc Date: 2018-06-01 Impact factor: 5.501
Authors: Sebastian Wiberg; Jesper Kjaergaard; Rasmus Møgelvang; Christian Holdflod Møller; Kristian Kandler; Hanne Ravn; Christian Hassager; Lars Køber; Jens Christian Nilsson Journal: BMJ Open Date: 2021-11-05 Impact factor: 2.692