Rajit K Basu1, Katja Gist, Derek S Wheeler. 1. aDivision of Critical Care Medicine bDepartment of Pediatrics, Center for Acute Care Nephrology, Cincinnati Children's Hospital and Medical Center, University of Cincinnati, Cincinnati, Ohio cDivision of Cardiology, Department of Pediatrics, Children's Hospital Colorado University of Colorado, Aurora, Colorado, USA.
Abstract
PURPOSE OF REVIEW: Acute kidney injury (AKI) is a multifactorial syndrome affecting an alarming proportion of hospitalized patients. Although early recognition may expedite management, the ability to identify patients at-risk and those suffering real-time injury is inconsistent. The review will summarize the recent reports describing advancements in the area of AKI epidemiology, specifically focusing on risk scoring and predictive analytics. RECENT FINDINGS: In the critical care population, the primary underlying factors limiting prediction models include an inability to properly account for patient heterogeneity and underperforming metrics used to assess kidney function. Severity of illness scores demonstrate limited AKI predictive performance. Recent evidence suggests traditional methods for detecting AKI may be leveraged and ultimately replaced by newer, more sophisticated analytical tools capable of prediction and identification: risk stratification, novel AKI biomarkers, and clinical information systems. Additionally, the utility of novel biomarkers may be optimized through targeting using patient context, and may provide more granular information about the injury phenotype. Finally, manipulation of the electronic health record allows for real-time recognition of injury. SUMMARY: Integrating a high-functioning clinical information system with risk stratification methodology and novel biomarker yields a predictive analytic model for AKI diagnostics.
PURPOSE OF REVIEW: Acute kidney injury (AKI) is a multifactorial syndrome affecting an alarming proportion of hospitalized patients. Although early recognition may expedite management, the ability to identify patients at-risk and those suffering real-time injury is inconsistent. The review will summarize the recent reports describing advancements in the area of AKI epidemiology, specifically focusing on risk scoring and predictive analytics. RECENT FINDINGS: In the critical care population, the primary underlying factors limiting prediction models include an inability to properly account for patient heterogeneity and underperforming metrics used to assess kidney function. Severity of illness scores demonstrate limited AKI predictive performance. Recent evidence suggests traditional methods for detecting AKI may be leveraged and ultimately replaced by newer, more sophisticated analytical tools capable of prediction and identification: risk stratification, novel AKI biomarkers, and clinical information systems. Additionally, the utility of novel biomarkers may be optimized through targeting using patient context, and may provide more granular information about the injury phenotype. Finally, manipulation of the electronic health record allows for real-time recognition of injury. SUMMARY: Integrating a high-functioning clinical information system with risk stratification methodology and novel biomarker yields a predictive analytic model for AKI diagnostics.
Authors: Kathleen D Liu; Stuart L Goldstein; Anitha Vijayan; Chirag R Parikh; Kianoush Kashani; Mark D Okusa; Anupam Agarwal; Jorge Cerdá Journal: Clin J Am Soc Nephrol Date: 2020-04-21 Impact factor: 8.237
Authors: Toni Jäntti; Tuukka Tarvasmäki; Veli-Pekka Harjola; Kari Pulkki; Heidi Turkia; Tuija Sabell; Heli Tolppanen; Raija Jurkko; Mari Hongisto; Anu Kataja; Alessandro Sionis; Jose Silva-Cardoso; Marek Banaszewski; Salvatore DiSomma; Alexandre Mebazaa; Mikko Haapio; Johan Lassus Journal: Ann Intensive Care Date: 2021-02-05 Impact factor: 6.925