AUTHOR=Zhu Ganggui , Fu Zaixiang , Jin Taian , Xu Xiaohui , Wei Jie , Cai Lingxin , Yu Wenhua 

TITLE=Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: A retrospective study

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.987684

DOI=10.3389/fneur.2022.987684

ISSN=1664-2295

ABSTRACT=Background: This study sought to develop and validate a dynamic nomogram chart to assess the risk of AKI in patients with AIS.
Methods: This data was drawn from the Medical Information Mart for Intensive Care III (MIMIC-III) database,which collects 46 clinical indicators of patients after admission to the hospital. The primary outcome indicator was the occurrence of AKI within 48 hours of ICU admission. Independent risk factors for AKI were screened from the training set using univariate and multifactorial logistic regression analysis. Multiple logistic regression models were developed, Nomograms were plotted and validated in an internal validation set.Based on the received operating curve (ROC), calibration curve and decision curve analysis (DCA) to estimate the performance of this nomogram.
Results: Nomogram indicators include: blood urea nitrogen (BUN), creatinine, red blood cell distribution width (RDW), heart rate (HR), Resand Oxford Acute Severity of Illness Score (OASIS), the history of congestive heart failure, the use of vancomycin, contrast agent and mannitol. The predictive model displayed well discrimination with the area under the ROC curve of 0.8529 and 0.8598 for the training set and the validator, respectively. Calibration curves revealed favorable concordance between the actual and predicted incidence of AKI (P> 0.05). DCA indicates excellent net clinical benefit of nomogram in predicting AKI.
Conclusion: In summary, we explored the incidence of AKI in patients with AIS during ICU stay and developed a predictive model to help clinical decision making.