Acute kidney injury (AKI) is a complex syndrome with a variety of possible etiologies and symptoms. It is characterized by high mortality and poor recovery of renal function. The incidence and mortality rates of patients with AKI in intensive care units are extremely high. It is generally accepted that early identification and prompt treatment of AKI are essential to improve outcomes. This study aimed to develop a model based on risk stratification to identify and diagnose early stage AKI for improved prognosis in critically ill patients.This was a single-center, retrospective, observational study. Based on relevant literature, we selected 13 risk factors (age, sex, hypertension, diabetes, coronary heart disease, chronic kidney disease, total bilirubin, emergency surgery, mechanical ventilation, sepsis, heart failure, cancer, and hypoalbuminemia) for AKI assessment using the Kidney Disease Improving Global Outcomes (KDIGO) diagnostic criteria. Univariate and multivariate analyses were used to determine risk factors for eventual entry into the predictive model. The AKI predictive model was established using binary logistic regression, and the area under the receiver operating characteristic curve (AUROC or AUC) was used to evaluate the predictive ability of the model and to determine critical values.The AKI predictive model was established using binary logistic regression. The AUROC of the predictive model was 0.81, with a sensitivity of 69.8%, specificity of 83.4%, and positive likelihood ratio of 4.2.A predictive model for AKI in critically ill patients was established using 5 related risk factors: heart failure, chronic kidney disease, emergency surgery, sepsis, and total bilirubin; however, the predictive ability requires validation.