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dc.contributor.authorCoulson, Tim G-
dc.contributor.authorBailey, Michael-
dc.contributor.authorPilcher, Dave-
dc.contributor.authorReid, Christopher M-
dc.contributor.authorSeevanayagam, Siven-
dc.contributor.authorWilliams-Spence, Jenni-
dc.contributor.authorBellomo, Rinaldo-
dc.identifier.citationJournal of Cardiothoracic and Vascular Anesthesia 2021; 35(3): 866-873en_US
dc.description.abstractTo develop a simple model for the prediction of acute kidney injury (AKI) and renal replacement therapy (RRT) that could be used in clinical or research risk stratification. Retrospective analysis. Multi-institutional. All cardiac surgery patients from September 2016 to December 2018. Observational. The study cohort was divided into a development set (75%) and validation set (25%). The following 2 data epochs were used: preoperative data and immediate postoperative data (within 4 h of intensive care unit admission). Univariate statistics were used to identify variables associated with AKI or RRT. Stepwise logistic regression was used to develop a parsimonious model. Model discrimination and calibration were evaluated in the test set. Models were compared with previously published models and with a more comprehensive model developed using the least absolute shrinkage and selection operator. The study included 22,731 patients at 33 hospitals. The incidences of AKI (any stage) and RRT for the present analysis were 5,829 patients (25.6%) and 488 patients (2.1%), respectively. Models were developed for AKI, with an area under the receiver operating curve (AU-ROC) of 0.67 and 0.69 preoperatively and postoperatively, respectively. Models for RRT had an AU-ROC of 0.77 and 0.80 preoperatively and postoperatively, respectively. These models contained between 3 and 5 variables. Comparatively, comprehensive least absolute shrinkage and selection operator models contained between 21 and 26 variables, with an AU-ROC of 0.71 and 0.72 for AKI and 0.84 and 0.87 for RRT respectively. In the present study, simple, clinically applicable models for predicting AKI and RRT preoperatively and immediate postoperatively were developed. Even though AKI prediction remained poor, RRT prediction was good with a parsimonious model.en_US
dc.subjectAcute kidney injuryen_US
dc.subjectcardiac surgeryen_US
dc.subjectrenal replacement therapyen_US
dc.subjectrisk predictionen_US
dc.titlePredicting Acute Kidney Injury After Cardiac Surgery Using a Simpler Model.en_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleJournal of Cardiothoracic and Vascular Anesthesiaen_US
dc.identifier.affiliationCentre for Integrated Critical Care, University of Melbourne, Melbourne, Australiaen_US
dc.identifier.affiliationSchool of Public Health, Curtin University, Perth, Australiaen_US
dc.identifier.affiliationDepartment of Intensive Care, Alfred Health, Melbourne, Australiaen_US
dc.identifier.affiliationDepartment of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australiaen_US
dc.type.austinJournal Article-
item.fulltextNo Fulltext-
item.openairetypeJournal Article-
item.cerifentitytypePublications- Care- Analytics Research and Evaluation (DARE) Centre-
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