Author:
Jing Huan,Liao Meijuan,Tang Simin,Lin Sen,Ye Li,Zhong Jiying,Wang Hanbin,Zhou Jun
Abstract
Abstract
Background
Acute kidney injury (AKI) is a common and severe complication of cardiac surgery with cardiopulmonary bypass (CPB). This study aimed to establish a model to predict the probability of postoperative AKI in patients undergoing cardiac surgery with CPB.
Methods
We conducted a retrospective, multicenter study to analyze 1082 patients undergoing cardiac surgery under CPB. The least absolute shrinkage and selection operator regression model was used to optimize feature selection for the AKI model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the previously mentioned model. Finally, we used multiple methods to evaluate the accuracy and clinical applicability of the model.
Results
Age, gender, hypertension, CPB duration, intraoperative 5% bicarbonate solution and red blood cell transfusion, urine volume were identified as important factors. Then, these risk factors were created into nomogram to predict the incidence of AKI after cardiac surgery under CPB.
Conclusion
We developed a nomogram to predict the incidence of AKI after cardiac surgery. This model can be used as a reference tool for evaluating early medical intervention to prevent postoperative AKI.
Funder
Natural Science Foundation of Guangdong Province
Publisher
Springer Science and Business Media LLC
Subject
Anesthesiology and Pain Medicine
Cited by
1 articles.
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