RESUMO
Acute kidney injury (AKI) is a common serious complication after acute ischemic stroke (AIS), which is associated with an increased mortality and disability. However, the clinical prevalence is often underestimated or overlooked. This article reviews the pathogenesis, risk factors, predictive factors, and predictive models of AKI in patients with AIS, in order to provide a basis for early clinical identification and diagnosis of AKI in patients with AIS.
RESUMO
Objective:To develop a risk prediction model for acute kidney injury (AKI) in young, middle-aged and elderly patients with acute ischemic stroke (AIS), and verify the predictive ability of the model.Methods:Patients admitted to the Department of Neurology, Lianyungang Hospital Affiliated to Xuzhou Medical University from January 2018 to August 2022 were retrospectively included as a modeling cohort, and patients with AIS from September 2022 to September 2023 were prospectively included as a validation cohort. Independent risk factors for AKI were determined by multivariate logistic regression analysis, and risk prediction models for AKI in young AIS patients group and middle-aged and elderly AIS patients group were developed. The predictive power of the model was tested using the receiver operating characteristic (ROC) curve. Results:The young group included 760 patients with AIS, of which 584 (76.84%) were in the modeling cohort, and 146 (25.00%) had AKI. Multivariate logistic regression analysis showed that anemia, systolic blood pressure, homocysteine, alcohol consumption, blood urea nitrogen, and National Institutes of Health Stroke Scale (NIHSS) score were independent risk factors for AKI (all P<0.05). ROC analysis showed that the area under the curve of the predictive model was 0.938 (95% confidence interval 0.912-0.963), the Youden index was 0.747, and the optimal cut-off value was 0.249. The sensitivity and specificity of predicting AKI were 84.8% and 89.9%, respectively. A total of 1 417 patients with AIS were included in the middle-aged and elderly group, of which 833 patients (58.79%) were in the modeling cohort and 230 (27.61%) had AKI. Multivariate logistic regression analysis showed that hypertension, atrial fibrillation, previous stroke history, smoking, infection, triglycerides, NIHSS score, use of antihypertensive drugs, use of loop diuretics, serum creatinine, and blood urea nitrogen were the independent risk factors for AKI ( P<0.05). ROC analysis showed that the area under the curve of the predictive model was 0.838 (95% confidence interval 0.808-0.868), the Youden index was 0.539, the optimal cut-off value was 0.242, and the predictive sensitivity and specificity were 78.3% and 75.6%, respectively. The Hosmer-Lemeshow goodness of fit test showed the predictive accuracy of the model was in good agreement with the actual occurrence of risk (the young group: χ2=8.968, P=0.345; the middle-aged and elderly group: χ2=11.250, P=0.188). The validation cohort analysis shows that the model had high prediction accuracy and credibility in two groups. Conclusion:The model can specifically predict the risk of AKI in young, middle-aged and elderly patients with AIS, with high sensitivity and specificity.