• 1. Clinical Medical College of Nanjing Medical University, Nanjing, 210029, P.R.China;
  • 2. Department of Cardiovascular Surgery, Dongfang Hospital Affiliated to Shanghai Tongji University, Shanghai, 200120, P.R.China;
ZHANG Yangyang, Email: zhangyangyang_md@sina.com
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Acute kidney injury (AKI) is a complication with high morbidity and mortality after cardiac surgery. In order to predict the incidence of AKI after cardiac surgery, many risk prediction models have been established worldwide. We made a detailed introduction to the composing features, clinical application and predictive capability of 14 commonly used models. Among the 14 risk prediction models, age, congestive heart failure, hypertension, left ventricular ejection fraction, diabetes, cardiac valve surgery, coronary artery bypass grafting (CABG) combined with cardiac valve surgery, emergency surgery, preoperative creatinine, preoperative estimated glomerular filtration rate (eGFR), preoperative New York Heart Association (NYHA) score>Ⅱ, previous cardiac surgery, cadiopulmonary bypass (CPB) time and low cardiac output syndrome (LCOS) are included in many risks prediction models (>3 times). In comparison to Mehta and SRI models, Cleveland risk prediction model shows the best discrimination for the prediction of renal replacement therapy (RRT)-AKI and AKI in the European. However, in Chinese population, the predictive ability of the above three risk prediction models for RRT-AKI and AKI is poor.

Citation: WU Dongchen, WANG Qi, ZHANG Yangyang. Current status of research on models for predicting acute kidney injury following cardiac surgery. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2018, 25(3): 237-248. doi: 10.7507/1007-4848.201612020 Copy

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