Literature DB >> 27736815

Acute Kidney Injury Electronic Alert for Nephrologist: Reactive versus Proactive?

Kianoush Kashani1, Claudio Ronco.   

Abstract

Acute kidney injury (AKI) is a common complication of acute illnesses with significant impact on the mortality and morbidity. Early recognition of AKI allows clinicians to provide prophylactic interventions and to improve the outcomes of this deadly syndrome. Growing utilization and capabilities of electronic health records allow AKI risk stratification and early recognition with a potential effect on the processes of care and outcomes. We evaluate the current level of evidence on the impact of the AKI e-alert on the processes of care and outcomes. We then discuss the impacts of e-alerting in the design of the future of nephrology service within the hospital practices with suggestions regarding the role of nephrologists in management of AKI e-alert systems in order to improve the quality of care provided to AKI patients. Video Journal Club 'Cappuccino with Claudio Ronco' at http://www.karger.com/?doi=450722.
© 2016 S. Karger AG, Basel.

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Year:  2016        PMID: 27736815     DOI: 10.1159/000450722

Source DB:  PubMed          Journal:  Blood Purif        ISSN: 0253-5068            Impact factor:   2.614


  8 in total

1.  Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department.

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Journal:  Sci Rep       Date:  2022-05-02       Impact factor: 4.996

2.  Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction.

Authors:  Sidney Le; Angier Allen; Jacob Calvert; Paul M Palevsky; Gregory Braden; Sharad Patel; Emily Pellegrini; Abigail Green-Saxena; Jana Hoffman; Ritankar Das
Journal:  Kidney Int Rep       Date:  2021-02-26

3.  Nomogram to predict the risk of septic acute kidney injury in the first 24 h of admission: an analysis of intensive care unit data.

Authors:  Fuxing Deng; Milin Peng; Jing Li; Yana Chen; Buyao Zhang; Shuangping Zhao
Journal:  Ren Fail       Date:  2020-11       Impact factor: 2.606

4.  Impact of Daily Electronic Laboratory Alerting on Early Detection and Clinical Documentation of Acute Kidney Injury in Hospital Settings.

Authors:  Tarush Kothari; Kendal Jensen; Debbie Mallon; Gerard Brogan; James Crawford
Journal:  Acad Pathol       Date:  2018-12-04

5.  Routine adoption of TIMP2 and IGFBP7 biomarkers in cardiac surgery for early identification of acute kidney injury.

Authors:  Chiara Levante; Fiorenza Ferrari; Chiara Manenti; Faeq Husain-Syed; Marta Scarpa; Tommaso Hinna Danesi; Massimo De Cal; Valentina Corradi; Grazia M Virzì; Alessandra Brendolan; Federico Nalesso; Pércia Bezerra; Salvador Lopez-Giacoman; Sara Samoni; Mara Senzolo; Davide Giavarina; Loris Salvador; Raffaele Bonato; Silvia De Rosa; Enrico Rettore; Claudio Ronco
Journal:  Int J Artif Organs       Date:  2017-11-16       Impact factor: 1.595

6.  Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning.

Authors:  Khaled Shawwa; Erina Ghosh; Stephanie Lanius; Emma Schwager; Larry Eshelman; Kianoush B Kashani
Journal:  Clin Kidney J       Date:  2020-09-30

7.  Nomogram to predict the risk of acute kidney injury in patients with diabetic ketoacidosis: an analysis of the MIMIC-III database.

Authors:  Tingting Fan; Haosheng Wang; Jiaxin Wang; Wenrui Wang; Haifei Guan; Chuan Zhang
Journal:  BMC Endocr Disord       Date:  2021-03-04       Impact factor: 2.763

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

Authors:  Ganggui Zhu; Zaixiang Fu; Taian Jin; Xiaohui Xu; Jie Wei; Lingxin Cai; Wenhua Yu
Journal:  Front Neurol       Date:  2022-09-13       Impact factor: 4.086

  8 in total

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