Abstract
Acute Kidney Injury (AKI) is associated with increased morbidity and mortality in critically ill patients [1]. Early detection and treatment may improve outcome. Previously, we developed a logistic regression (LR) model for early detection of AKI based on routinely collected data available at baseline, ICU admission and at the end of the first day (LR_BAD1) [2]. Continuous monitoring parameters may provide additional predictive power, in particular, urine output and hemodynamic parameters, whose management influences kidney perfusion.
Highlights
Acute Kidney Injury (AKI) is associated with increased morbidity and mortality in critically ill patients [1]
In the LR_BAD1+ model, we have added features extracted from hourly measures of urine and minute-byminute measures of heart frequency (HF) and mean arterial blood pressure (MABP)
Performance of logistic regression (LR)-BAD1 is slightly different than what was reported in [2] as here it is evaluated in a different population
Summary
Acute Kidney Injury (AKI) is associated with increased morbidity and mortality in critically ill patients [1]. We developed a logistic regression (LR) model for early detection of AKI based on routinely collected data available at baseline, ICU admission and at the end of the first day (LR_BAD1) [2]. Continuous monitoring parameters may provide additional predictive power, in particular, urine output and hemodynamic parameters, whose management influences kidney perfusion
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
