Evidence map›Paper›PMID 36535704›Full record

ArticleApplied clinical informatics2023

Acceptance of Acute Kidney Injury Alert by Providers in Cardiac Surgery Intensive Care Unit.

Connor Nevin, Khaled Shawwa, Jami Pincavitch, Rebecca L Neely, Donnie Goodwin, Paul McCarthy, Nada Mohamed, Charles Mullett, Gordon S Smith, John A Kellum and 1 more

Abstract read
In one paragraph

Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Clinical Informatics in Critical Care Medicine.The Yale journal of biology and medicine · 2023
    Review
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

11 authors.

Connor NevinSchool of Medicine, West Virginia University, Morgantown, West Virginia, United States.
Khaled ShawwaSection of Nephrology, Department of Internal Medicine, West Virginia University, Morgantown, West Virginia, United States.
Jami PincavitchDepartment of Internal Medicine, West Virginia University, Morgantown, West Virginia, United States.
Rebecca L NeelyWest Virginia University, Morgantown, West Virginia, United States.
Donnie GoodwinSection of Cardiovascular Critical Care, Department of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, West Virginia, United States.
Paul McCarthySection of Cardiovascular Critical Care, Department of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, West Virginia, United States.
Nada MohamedSection of Pulmonary and Critical Care Medicine, Department of Internal Medicine, West Virginia University, Morgantown, West Virginia, United States.
Charles MullettDepartment of Pediatrics, West Virginia University, Morgantown, West Virginia, United States.
Gordon S SmithDepartment of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia, United States.
John A KellumDepartment of Critical Care Medicine, Medicine, Bioengineering, and Clinical and Translational Science, University of Pittsburgh, Pittsburgh, Pennsylvania, United States.
Ankit SakhujaSection of Cardiovascular Critical Care, Department of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, West Virginia, United States.

Funding

West Virginia IDEA-CTRU54GM104942 · NIGMS · WEST VIRGINIA UNIVERSITY · PI JUDITH FEINBERG · 2012 to 2026
$81.0M
Using Novel Machine Learning Methods to Personalize Strategies for Prevention of Persistent AKI after Cardiac SurgeryK08DK131286 · NIDDK · WEST VIRGINIA UNIVERSITY · PI Ankit Sakhuja · 2022 to 2026
$774k
NIDDK NIH HHS K08 DK131286NIGMS NIH HHS U54 GM104942
6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is a common complication after cardiac surgery and is associated with worse outcomes. Its management relies on early diagnosis, and therefore, electronic alerts have been used to alert clinicians for development of AKI. Electronic alerts are, however, associated with high rates of alert fatigue.

objectivesWe designed this study to assess the acceptance of user-centered electronic AKI alert by clinicians.

methodsWe developed a user-centered electronic AKI alert that alerted clinicians of development of AKI in a persistent yet noninterruptive fashion. As the goal of the alert was to alert toward new or worsening AKI, it disappeared 48 hours after being activated. We assessed the acceptance of the alert using surveys at 6 and 12 months after the alert went live.

resultsAt 6 months after their implementation, 38.9% providers reported that they would not have recognized AKI as early as they did without this alert. This number increased to 66.7% by 12 months of survey. Most providers also shared that they re-dosed or discontinued medications earlier, provided earlier management of volume status, avoided intravenous contrast use, and evaluated patients by using point-of-care ultrasounds more due to the alert. Overall, 83.3% respondents reported satisfaction with the electronic AKI alerts at 6 months and 94.4% at 12 months.

conclusionThis study showed high rates of acceptance of a user-centered electronic AKI alert over time by clinicians taking care of patients with AKI.

Indexed as

Acute Kidney InjuryClinical AlarmsEarly DiagnosisHumansIntensive Care Units

Identifiers

PMID36535704
PMCPMC9908418

What OpenQuestion holds

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Registered trials

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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.