Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury

Acute kidney injury (AKI) is a potentially fatal complication of cardiac surgery. The inability to predict cardiac surgery-associated AKI is a major barrier to prevention and early treatment. Current clinical risk models for the prediction of cardiac surgery-associated AKI are insufficient, particul...

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Main Authors: Jordan Crosina, Jordyn Lerner, Julie Ho, Navdeep Tangri, Paul Komenda, Brett Hiebert, Nora Choi, Rakesh C. Arora, Claudio Rigatto
Format: Article
Language:English
Published: Elsevier 2017-03-01
Series:Kidney International Reports
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2468024916301486
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spelling doaj-817ec5e939fa4e06afba2ba8be306a4a2020-11-24T23:06:37ZengElsevierKidney International Reports2468-02492017-03-012217217910.1016/j.ekir.2016.10.003Improving the Prediction of Cardiac Surgery–Associated Acute Kidney InjuryJordan Crosina0Jordyn Lerner1Julie Ho2Navdeep Tangri3Paul Komenda4Brett Hiebert5Nora Choi6Rakesh C. Arora7Claudio Rigatto8Department of Medicine, University of Manitoba, Winnipeg, CanadaDepartment of Medicine, University of Manitoba, Winnipeg, CanadaDepartment of Medicine, University of Manitoba, Winnipeg, CanadaDepartment of Medicine, University of Manitoba, Winnipeg, CanadaDepartment of Medicine, University of Manitoba, Winnipeg, CanadaCardiac Sciences Program, St. Boniface Hospital Research Centre, Winnipeg, CanadaDepartment of Immunology, University of Manitoba, Winnipeg, CanadaCardiac Sciences Program, St. Boniface Hospital Research Centre, Winnipeg, CanadaDepartment of Medicine, University of Manitoba, Winnipeg, CanadaAcute kidney injury (AKI) is a potentially fatal complication of cardiac surgery. The inability to predict cardiac surgery-associated AKI is a major barrier to prevention and early treatment. Current clinical risk models for the prediction of cardiac surgery-associated AKI are insufficient, particularly in patients with preexisting kidney dysfunction. Methods: To identify intraoperative variables that might improve the performance of a validated clinical risk score (Cleveland Clinic Score, CCS) for the prediction of cardiac surgery-associated AKI, we conducted a prospective cohort study in 289 consecutive elective cardiac surgery patients at a tertiary care center. We compared the area under the receiver operator characteristic curve (AUC) of a base model including only the CCS with models containing additional selected intraoperative variables including mean arterial pressure, hematocrit, duration of procedure, blood transfusions, and fluid balance. AKI was defined by the Kidney Disease Improving Global Outcomes 2012 criteria. Results: The CCS alone gave an AUC of 0.72 (95% confidence interval, 0.62–0.82) for postoperative AKI. Nadir intraoperative hematocrit was the only variable that improved AUC for postoperative AKI when added to the CCS (AUC = 0.78; 95% confidence interval, 0.70–0.87; P = 0.002). In the subcohort of patients without preexisting chronic kidney disease (n = 214), where the CCS underperformed (AUC, 0.60 [0.43–0.76]), the improvement with the addition of nadir hematocrit was more marked (AUC, 0.74 [0.62–0.86]). Other variables did not improve discrimination. Discussion: Nadir intraoperative hematocrit is useful in improving discrimination of clinical risk scores for AKI, and may provide a target for intervention.http://www.sciencedirect.com/science/article/pii/S2468024916301486chronic kidney diseasehematocrithemolysisprediction models
collection DOAJ
language English
format Article
sources DOAJ
author Jordan Crosina
Jordyn Lerner
Julie Ho
Navdeep Tangri
Paul Komenda
Brett Hiebert
Nora Choi
Rakesh C. Arora
Claudio Rigatto
spellingShingle Jordan Crosina
Jordyn Lerner
Julie Ho
Navdeep Tangri
Paul Komenda
Brett Hiebert
Nora Choi
Rakesh C. Arora
Claudio Rigatto
Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
Kidney International Reports
chronic kidney disease
hematocrit
hemolysis
prediction models
author_facet Jordan Crosina
Jordyn Lerner
Julie Ho
Navdeep Tangri
Paul Komenda
Brett Hiebert
Nora Choi
Rakesh C. Arora
Claudio Rigatto
author_sort Jordan Crosina
title Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
title_short Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
title_full Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
title_fullStr Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
title_full_unstemmed Improving the Prediction of Cardiac Surgery–Associated Acute Kidney Injury
title_sort improving the prediction of cardiac surgery–associated acute kidney injury
publisher Elsevier
series Kidney International Reports
issn 2468-0249
publishDate 2017-03-01
description Acute kidney injury (AKI) is a potentially fatal complication of cardiac surgery. The inability to predict cardiac surgery-associated AKI is a major barrier to prevention and early treatment. Current clinical risk models for the prediction of cardiac surgery-associated AKI are insufficient, particularly in patients with preexisting kidney dysfunction. Methods: To identify intraoperative variables that might improve the performance of a validated clinical risk score (Cleveland Clinic Score, CCS) for the prediction of cardiac surgery-associated AKI, we conducted a prospective cohort study in 289 consecutive elective cardiac surgery patients at a tertiary care center. We compared the area under the receiver operator characteristic curve (AUC) of a base model including only the CCS with models containing additional selected intraoperative variables including mean arterial pressure, hematocrit, duration of procedure, blood transfusions, and fluid balance. AKI was defined by the Kidney Disease Improving Global Outcomes 2012 criteria. Results: The CCS alone gave an AUC of 0.72 (95% confidence interval, 0.62–0.82) for postoperative AKI. Nadir intraoperative hematocrit was the only variable that improved AUC for postoperative AKI when added to the CCS (AUC = 0.78; 95% confidence interval, 0.70–0.87; P = 0.002). In the subcohort of patients without preexisting chronic kidney disease (n = 214), where the CCS underperformed (AUC, 0.60 [0.43–0.76]), the improvement with the addition of nadir hematocrit was more marked (AUC, 0.74 [0.62–0.86]). Other variables did not improve discrimination. Discussion: Nadir intraoperative hematocrit is useful in improving discrimination of clinical risk scores for AKI, and may provide a target for intervention.
topic chronic kidney disease
hematocrit
hemolysis
prediction models
url http://www.sciencedirect.com/science/article/pii/S2468024916301486
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