Evidence map›Paper›PMID 31154298›Full record

ArticleBMJ open2019

Electronic Alerts for Acute Kidney Injury Amelioration (ELAIA-1): a completely electronic, multicentre, randomised controlled trial: design and rationale.

Marina Mutter, Melissa Martin, Yu Yamamoto, Aditya Biswas, Boian Etropolski, Harold Feldman, Amit Garg, Noah Gourlie, Stephen Latham, Haiqun Lin and 5 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02753751 (Optimizing Electronic Alerts for Acute Kidney Injury), which is not on this map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02753751 nacompletednot on this map

Optimizing Electronic Alerts for Acute Kidney Injury

TypeinterventionalSponsorYale UniversityRan2018 to 2020Enrolled6,030ConditionsAcute Kidney InjuryArmsAKI Alert
3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2021
    Observational
  10. Variation in Best Practice Measures in Patients With Severe Hospital-Acquired Acute Kidney Injury: A Multicenter Study.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2021
    Article
  11. Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2020
    Observational
  12. The impact of biomarkers of acute kidney injury on individual patient care.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2020
    Review
  13. Machine Learning to Predict Acute Kidney Injury.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Marina MutterProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Melissa MartinProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Yu YamamotoProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Aditya BiswasProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Boian EtropolskiProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Harold FeldmanMedicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Amit GargDepartment of Medcine, University of Western Ontario, London, Ontario, Canada.
Noah GourlieBaim Institute for Clinical Research, Boston, Massachusetts, USA.
Stephen LathamProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Haiqun LinProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Paul M PalevskyDepartment of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Chirag ParikhDepartment of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Erica MoreiraProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Ugochukwu UgwuowoProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.
Francis P WilsonProgram of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0002-2633-2412

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Renal Physiology and Phenotyping CoreP30DK079310 · NIDDK · YALE UNIVERSITY · PI SOMLO, STEFAN · 2008 to 2022
$16.8M
Optimizing Electronic Alerts for Acute Kidney InjuryR01DK113191 · NIDDK · YALE UNIVERSITY · PI WILSON, FRANCIS PERRY · 2018 to 2022
$3.1M
NCATS NIH HHS UL1 TR001863NIDDK NIH HHS P30 DK079310NIDDK NIH HHS R01 DK113191
6 · The paper itself

Abstract

introductionAcute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection. METHODS AND ANALYSIS: The design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5-2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria. ETHICS AND DISSEMINATION: This study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials.gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT02753751; Pre-results.

Indexed as

Acute Kidney InjuryClinical AlarmsCreatinineElectronic Data ProcessingIntensive Care UnitsAdultBiomarkersEarly DiagnosisFemaleHumansMaleMiddle AgedMulticenter Studies as TopicOutcome and Process Assessment, Health CarePilot ProjectsRandomized Controlled Trials as TopicBiomarkersCreatinineAcute kidney injuryacute renal failureclinical decision supportelectronic health recordrandomized, alert

Identifiers

PMID31154298
PMCPMC6549649

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.