Literature DB >> 29097621

Clinical Decision Support for In-Hospital AKI.

Mohammed Al-Jaghbeer1, Dilhari Dealmeida2, Andrew Bilderback3, Richard Ambrosino4,5, John A Kellum6.   

Abstract

AKI carries a significant mortality and morbidity risk. Use of a clinical decision support system (CDSS) might improve outcomes. We conducted a multicenter, sequential period analysis of 528,108 patients without ESRD before admission, from October of 2012 to September of 2015, to determine whether use of a CDSS reduces hospital length of stay and in-hospital mortality for patients with AKI. We compared patients treated 12 months before (181,696) and 24 months after (346,412) implementation of the CDSS. Coprimary outcomes were hospital mortality and length of stay adjusted by demographics and comorbidities. AKI was diagnosed in 64,512 patients (12.2%). Crude mortality rate fell from 10.2% before to 9.4% after CDSS implementation (odds ratio, 0.91; 95% confidence interval [95% CI], 0.86 to 0.96; P=0.001) for patients with AKI but did not change in patients without AKI (from 1.5% to 1.4%). Mean hospital duration decreased from 9.3 to 9.0 days (P<0.001) for patients with AKI, with no change for patients without AKI. In multivariate mixed-effects models, the adjusted odds ratio (95% CI) was 0.76 (0.70 to 0.83) for mortality and 0.66 (0.61 to 0.72) for dialysis (P<0.001). Change in adjusted hospital length of stay was also significant (incidence rate ratio, 0.91; 95% CI, 0.89 to 0.92), decreasing from 7.2 to 6.0 days for patients with AKI. Results were robust to sensitivity analyses and were sustained for the duration of follow-up. Hence, implementation of a CDSS for AKI resulted in a small but sustained decrease in hospital mortality, dialysis use, and length of stay.
Copyright © 2018 by the American Society of Nephrology.

Entities:  

Keywords:  Electronic medical record; acute renal failure; electronic alerts; electronic health record; nephrotoxicity; radio contrast

Mesh:

Year:  2017        PMID: 29097621      PMCID: PMC5791078          DOI: 10.1681/ASN.2017070765

Source DB:  PubMed          Journal:  J Am Soc Nephrol        ISSN: 1046-6673            Impact factor:   10.121


  25 in total

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2.  A comparison of three methods to estimate baseline creatinine for RIFLE classification.

Authors:  Jakub Závada; Eric Hoste; Rodrigo Cartin-Ceba; Paolo Calzavacca; Ognjen Gajic; Gilles Clermont; Rinaldo Bellomo; John A Kellum
Journal:  Nephrol Dial Transplant       Date:  2010-01-25       Impact factor: 5.992

3.  Acute kidney injury, mortality, length of stay, and costs in hospitalized patients.

Authors:  Glenn M Chertow; Elisabeth Burdick; Melissa Honour; Joseph V Bonventre; David W Bates
Journal:  J Am Soc Nephrol       Date:  2005-09-21       Impact factor: 10.121

4.  Early nephrologist involvement in hospital-acquired acute kidney injury: a pilot study.

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5.  An Official ATS/ERS/ESICM/SCCM/SRLF Statement: Prevention and Management of Acute Renal Failure in the ICU Patient: an international consensus conference in intensive care medicine.

Authors:  Laurent Brochard; Fekri Abroug; Matthew Brenner; Alain F Broccard; Robert L Danner; Miquel Ferrer; Franco Laghi; Sheldon Magder; Laurent Papazian; Paolo Pelosi; Kees H Polderman
Journal:  Am J Respir Crit Care Med       Date:  2010-05-15       Impact factor: 21.405

6.  An assessment of the RIFLE criteria for acute renal failure in hospitalized patients.

Authors:  Shigehiko Uchino; Rinaldo Bellomo; Donna Goldsmith; Samantha Bates; Claudio Ronco
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7.  Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study.

Authors:  Eric A J Hoste; Sean M Bagshaw; Rinaldo Bellomo; Cynthia M Cely; Roos Colman; Dinna N Cruz; Kyriakos Edipidis; Lui G Forni; Charles D Gomersall; Deepak Govil; Patrick M Honoré; Olivier Joannes-Boyau; Michael Joannidis; Anna-Maija Korhonen; Athina Lavrentieva; Ravindra L Mehta; Paul Palevsky; Eric Roessler; Claudio Ronco; Shigehiko Uchino; Jorge A Vazquez; Erick Vidal Andrade; Steve Webb; John A Kellum
Journal:  Intensive Care Med       Date:  2015-07-11       Impact factor: 17.440

8.  A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital.

Authors:  Christine J Porter; Irene Juurlink; Linda H Bisset; Riaz Bavakunji; Rajnikant L Mehta; Mark A J Devonald
Journal:  Nephrol Dial Transplant       Date:  2014-04-16       Impact factor: 5.992

9.  Validation of a combined comorbidity index.

Authors:  M Charlson; T P Szatrowski; J Peterson; J Gold
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10.  Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury.

Authors:  Edward D Siew; Michael E Matheny; T Alp Ikizler; Julie B Lewis; Randolph A Miller; Lemuel R Waitman; Alan S Go; Chirag R Parikh; Josh F Peterson
Journal:  Kidney Int       Date:  2009-12-30       Impact factor: 10.612

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  77 in total

1.  Making the Right Decision: Do Clinical Decision Support Systems for AKI Improve Patient Outcomes?

Authors:  Nicholas M Selby; Richard J Fluck
Journal:  J Am Soc Nephrol       Date:  2018-01-15       Impact factor: 10.121

2.  Sustained effects of a clinical decision support system for acute kidney injury.

Authors:  Ayham Bataineh; Dilhari Dealmeida; Andrew Bilderback; Richard Ambrosino; Mohammed J Al-Jaghbeer; Dana Y Fuhrman; John A Kellum
Journal:  Nephrol Dial Transplant       Date:  2020-10-01       Impact factor: 5.992

3.  Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

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5.  An Organizational-Level Program of Intervention for AKI: A Pragmatic Stepped Wedge Cluster Randomized Trial.

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Journal:  J Am Soc Nephrol       Date:  2019-02-21       Impact factor: 10.121

6.  Which risk predictors are more likely to indicate severe AKI in hospitalized patients?

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Review 7.  eResearch in acute kidney injury: a primer for electronic health record research.

Authors:  Emily L Joyce; Dilhari R DeAlmeida; Dana Y Fuhrman; Priyanka Priyanka; John A Kellum
Journal:  Nephrol Dial Transplant       Date:  2019-03-01       Impact factor: 5.992

Review 8.  Acute kidney injury.

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Journal:  Nat Rev Dis Primers       Date:  2021-07-15       Impact factor: 52.329

Review 9.  Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models.

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Journal:  Adv Chronic Kidney Dis       Date:  2021-01       Impact factor: 3.620

Review 10.  Conceptual advances and evolving terminology in acute kidney disease.

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