Abstract
BACKGROUND We sought to create a model that incorporated ultrasound examinations to predict the risk of acute kidney injury (AKI) after percutaneous coronary intervention (PCI) or cardiopulmonary bypass (CPB) surgery. MATERIAL AND METHODS A total of 292 patients with AKI after PCI or CPB surgery were enrolled for the study. Afterwards, treatment-related information, including data pertaining to ultrasound examination, was collected. A random forest model and multivariate logistic regression analysis were then used to establish a predictive model for the risk of AKI. Finally, the predictive quality and clinical utility of the model were assessed using calibration plots, receiver-operating characteristic curve, C-index, and decision curve analysis. RESULTS Predictive factors were screened and the model was established with a C-index of 0.955 in the overall sample set. Additionally, an area under the curve of 0.967 was obtained in the training group. Moreover, decision curve analysis also revealed that the prediction model had good clinical applicability. CONCLUSIONS The prediction model was efficient in predicting the risk of AKI by incorporating ultrasound examinations and a number of factors. Such included operation methods, age, congestive heart failure, body mass index, heart rate, white blood cell count, platelet count, hemoglobin, uric acid, and peak intensity (kidney cortex as well as kidney medulla).