Literature DB >> 34045582

The importance of the urinary output criterion for the detection and prognostic meaning of AKI.

Jill Vanmassenhove1, Johan Steen1,2,3,4, Stijn Vansteelandt2,3,5, Pawel Morzywolek2,3, Eric Hoste4, Johan Decruyenaere3,4, Dominique Benoit4, Wim Van Biesen6,7.   

Abstract

Most reports on AKI claim to use KDIGO guidelines but fail to include the urinary output (UO) criterion in their definition of AKI. We postulated that ignoring UO alters the incidence of AKI, may delay diagnosis of AKI, and leads to underestimation of the association between AKI and ICU mortality. Using routinely collected data of adult patients admitted to an intensive care unit (ICU), we retrospectively classified patients according to whether and when they would be diagnosed with KDIGO AKI stage ≥ 2 based on baseline serum creatinine (Screa) and/or urinary output (UO) criterion. As outcomes, we assessed incidence of AKI and association with ICU mortality. In 13,403 ICU admissions (62.2% male, 60.8 ± 16.8 years, SOFA 7.0 ± 4.1), incidence of KDIGO AKI stage ≥ 2 was 13.2% when based only the SCrea criterion, 34.3% when based only the UO criterion, and 38.7% when based on both criteria. By ignoring the UO criterion, 66% of AKI cases were missed and 13% had a delayed diagnosis. The cause-specific hazard ratios of ICU mortality associated with KDIGO AKI stage ≥ 2 diagnosis based on only the SCrea criterion, only the UO criterion and based on both criteria were 2.11 (95% CI 1.85-2.42), 3.21 (2.79-3.69) and 2.85 (95% CI 2.43-3.34), respectively. Ignoring UO in the diagnosis of KDIGO AKI stage ≥ 2 decreases sensitivity, may lead to delayed diagnosis and results in underestimation of KDIGO AKI stage ≥ 2 associated mortality.

Entities:  

Year:  2021        PMID: 34045582     DOI: 10.1038/s41598-021-90646-0

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  34 in total

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Review 3.  Risk Prediction Models for Contrast-Induced Acute Kidney Injury Accompanying Cardiac Catheterization: Systematic Review and Meta-analysis.

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Journal:  Can J Cardiol       Date:  2017-02-01       Impact factor: 5.223

Review 4.  Defining acute renal failure: RIFLE and beyond.

Authors:  Wim Van Biesen; Raymond Vanholder; Norbert Lameire
Journal:  Clin J Am Soc Nephrol       Date:  2006-08-30       Impact factor: 8.237

5.  RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis.

Authors:  Eric A J Hoste; Gilles Clermont; Alexander Kersten; Ramesh Venkataraman; Derek C Angus; Dirk De Bacquer; John A Kellum
Journal:  Crit Care       Date:  2006-05-12       Impact factor: 9.097

Review 6.  Risk prediction models for contrast induced nephropathy: systematic review.

Authors:  Samuel A Silver; Prakesh M Shah; Glenn M Chertow; Shai Harel; Ron Wald; Ziv Harel
Journal:  BMJ       Date:  2015-08-27

7.  A clinically applicable approach to continuous prediction of future acute kidney injury.

Authors:  Trevor Back; Christopher Nielson; Joseph R Ledsam; Shakir Mohamed; Nenad Tomašev; Xavier Glorot; Jack W Rae; Michal Zielinski; Harry Askham; Andre Saraiva; Anne Mottram; Clemens Meyer; Suman Ravuri; Ivan Protsyuk; Alistair Connell; Cían O Hughes; Alan Karthikesalingam; Julien Cornebise; Hugh Montgomery; Geraint Rees; Chris Laing; Clifton R Baker; Kelly Peterson; Ruth Reeves; Demis Hassabis; Dominic King; Mustafa Suleyman
Journal:  Nature       Date:  2019-07-31       Impact factor: 49.962

8.  Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.

Authors:  Xing Song; Alan S L Yu; John A Kellum; Lemuel R Waitman; Michael E Matheny; Steven Q Simpson; Yong Hu; Mei Liu
Journal:  Nat Commun       Date:  2020-11-09       Impact factor: 14.919

Review 9.  Acute renal failure - definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group.

Authors:  Rinaldo Bellomo; Claudio Ronco; John A Kellum; Ravindra L Mehta; Paul Palevsky
Journal:  Crit Care       Date:  2004-05-24       Impact factor: 9.097

Review 10.  Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.

Authors:  Luke Eliot Hodgson; Alexander Sarnowski; Paul J Roderick; Borislav D Dimitrov; Richard M Venn; Lui G Forni
Journal:  BMJ Open       Date:  2017-09-27       Impact factor: 2.692

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  5 in total

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Review 2.  Emerging early diagnostic methods for acute kidney injury.

Authors:  Zuoxiu Xiao; Qiong Huang; Yuqi Yang; Min Liu; Qiaohui Chen; Jia Huang; Yuting Xiang; Xingyu Long; Tianjiao Zhao; Xiaoyuan Wang; Xiaoyu Zhu; Shiqi Tu; Kelong Ai
Journal:  Theranostics       Date:  2022-03-21       Impact factor: 11.600

3.  Prediction of acute kidney injury in patients with femoral neck fracture utilizing machine learning.

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Journal:  Front Surg       Date:  2022-07-26

4.  A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients.

Authors:  Yirui Hu; Kunpeng Liu; Kevin Ho; David Riviello; Jason Brown; Alex R Chang; Gurmukteshwar Singh; H Lester Kirchner
Journal:  J Clin Med       Date:  2022-09-26       Impact factor: 4.964

5.  Impact of Different KDIGO Criteria on Clinical Outcomes for Early Identification of Acute Kidney Injury after Non-Cardiac Surgery.

Authors:  Jingwen Fu; Junko Kosaka; Hiroshi Morimatsu
Journal:  J Clin Med       Date:  2022-09-23       Impact factor: 4.964

  5 in total

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