A novel risk model based on white blood cell-related biomarkers for acute kidney injury prediction in patients with ischemic stroke admitted to the intensive care unit

Abstract

BackgroundConventional systemic inflammatory biomarkers could predict prognosis in patients with ischemic stroke (IS) admitted to the intensive care unit (ICU). Acute kidney injury (AKI) is common in patients with IS admitted to ICU, but few studies have used systemic inflammatory biomarkers to predict AKI in critically ill patients with IS. This study aimed to establish a risk model based on white blood cell (WBC)-related biomarkers to predict AKI in critically ill patients with IS.MethodsData were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort, and data were extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) for a validation cohort. Logistic regression analysis was used to determine the significant predictors of WBC-related biomarkers on AKI prediction, and a risk model was established based on those significant indicators in multivariate logistic regression. The receiver operating characteristics (ROC) curve was utilized to obtain the best cut-off value of the risk model. The Kaplan–Meier curve was used to evaluate the prognosis-predictive ability of the risk model.ResultsThe overall incidence of AKI was 28.4% in the training cohort and 33.2% in the validation cohort. WBC to lymphocyte ratio (WLR), WBC to basophils ratio (WBR), WBC to hemoglobin ratio (WHR), and neutrophil to lymphocyte ratio (NLR) could independently predict AKI, and a novel risk model was established based on WLR, WBR, WHR, and NLR. This risk model depicted good prediction performance both in AKI and other clinical outcomes including hemorrhage, persistent AKI, AKI progression, ICU mortality, and in-hospital mortality both in the training set and in the validation set.ConclusionA risk model based on WBC-related indicators exhibited good AKI prediction performance in critically ill patients with IS which could provide a risk stratification tool for clinicians in the ICU.