Screening of Influencing Factors for AKI Based on Lasso Regression

Abstract

Background:  Acute kidney injury (AKI) is one of the common critical diseases in hospitalized patients. There are many factors affecting acute kidney injury, difficult to obtain disease data, and short treatment time window. Early detection of AKI is critical to reducing mortality.  Methods: MIMIC-IV data were used to establish the prediction model of AKI in critical patients after operation. 1585 intensive care unit (ICU) patients were randomly divided into a training set (n=1109) and a prediction set (n=476) on a 7:3 basis. LASSO regression analysis was performed on the data, area under the operating characteristic curve (AUROC) was used to evaluate the model, and Hosmer-Lemeshow (HL) was used to test the degree of fit.  Results: Spinal injury, implantation of temporary cardiac pacemaker, esophageal disease, contrast media, diuretic, epinephrine, aminoglycoside antibiotics, tumor chemotherapy drugs, age, gender and red blood cells were determined to be independent risk factors for acute kidney injury. The AUROC of the model in the train set was 0.901 (95% confidence interval: 0.856~0.946). In the validation set, AUROC was 0.851 (95% confidence interval: 0.808~0.894). AUROC of the above two groups indicated that the model had a good degree of differentiation, which was greater than 70% in both groups. Hosmer-Lemeshow (HL) test showed P>0.05, indicating a good fit of the model. Conclusion: This prediction model can be used for the identification of post-operative AKI patients and provide a basis for clinicians or inpatients to more quickly self-measure the risk.