AUTHOR=Liu Shengyuan , Li Min , Yang Yuxing , Chen Yiguo , Wang Wei , Zheng Xiaoyu 

TITLE=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

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.1043396

DOI=10.3389/fmed.2022.1043396

ISSN=2296-858X

ABSTRACT=Background: Conventional systemic inflammatory biomarkers could predict prognosis in ischemic stroke (IS) patients admitted to the intensive care unit (ICU). And acute kidney injury (AKI) is common in IS patients admitted to ICU, but few studies used systemic inflammatory biomarkers to predict AKI in critically ill IS patients, the purpose of this study intended to establish a risk model based on white blood cell (WBC)-related biomarkers to predict AKI in critically ill IS patients.
Methods: Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database 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. 
Results:  The overall incidence of AKI was 28.4% in the training cohort and 33.2% in the validation cohort. WBC to neutrophil 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.
Conclusion: A risk model based on WBC-related indicators exhibited good AKI prediction performance in critically ill IS patients which could provide a risk stratification tool for clinicians in the ICU.