RISK FACTORS FOR ACUTE KIDNEY INJURY IN PATIENTS WITH ACUTE ISCHEMIC STROKE AND CONSTRUCTION OF A NOMOGRAM PREDICTIVE MODEL

Abstract

Objective To investigate the risk factors for acute kidney injury (AKI) in patients with acute ischemic stroke (AIS), and to construct a Nomogram predictive model. Methods A total of 1 633 patients with AIS who were hospitalized in our hospital from January 2020 to June 2021 were enrolled as subjects, and according to the presence or absence of AKI, they were divided into AKI group and non-AKI group. Lasso-Logistic regression analysis was used to investigate the risk factors for AKI in patients with AIS. A Nomogram predictive model was constructed, and the receiver operating characteristic (ROC) curve, index of concordance (C-Index), calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive value of this model. Results Among the 1 633 patients with AIS patients, 238 (14.57%) developed AKI. The Lasso-Logistic regression analysis showed that the increase in neutrophils, prolongation of prothrombin time, the increase in lactate dehydrogenase, the reduction in estimated glomerular filtration rate, history of blood transfusion, comorbidity of chronic kidney disease, and use of antibiotics, dipyridamole, diuretics, and β-receptor blocker were independent risk factors for AKI in AIS patients (P<0.05). The Nomogram predictive model had an area under the ROC curve of 0.797 (95%CI=0.769-0.866,P<0.01), and the C-Index of internal validation was 0.762 (95%CI=0.761-0.762,P<0.01). The calibration curve showed that the model had good consistency, and DCA showed that the model had certain clinical practicability. Conclusion This study clarifies the risk factors for AKI in AIS patients and constructs a Nomogram predictive model, which may facilitate the early identification, prevention, and treatment of AKI and improve patient prognosis.

Keywords

• acute kidney injury|ischemic stroke|logistic models|forecasting|risk factors