Evidence map›Paper›PMID 39357878›Full record

Trial reportApplied clinical informatics2024

An Advanced Cardiac Life Support Application Improves Performance during Simulated Cardiac Arrest.

Michael Senter-Zapata, Dylan V Neel, Isabella Colocci, Afaf Alblooshi, Faten Abdullah M AlRadini, Brian Quach, Samuel Lyon, Maxwell Coll, Andrew Chu, Katharine W Rainer and 8 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Applied clinical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

18 authors.

Michael Senter-ZapataHarvard Medical School, Boston, Massachusetts, United States.
Dylan V NeelHarvard Medical School, Boston, Massachusetts, United States.
Isabella ColocciHarvard Medical School, Boston, Massachusetts, United States.
Afaf AlblooshiSTRATUS Center for Medical Simulation, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Faten Abdullah M AlRadiniSTRATUS Center for Medical Simulation, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Brian QuachBrigham and Women's Hospital, Boston, Massachusetts, United States.
Samuel LyonHarvard Medical School, Boston, Massachusetts, United States.
Maxwell CollHarvard Medical School, Boston, Massachusetts, United States.
Andrew ChuHealthcare Transformation Lab, Massachusetts General Hospital, Boston, Massachusetts, United States.
Katharine W RainerBeth Israel Deaconess Medical Center, Boston, Massachusetts, United States.
Beth WatersBrigham and Women's Faulkner Hospital, Jamaica Plain, Massachusetts, United States.
Christopher W BaughHarvard Medical School, Boston, Massachusetts, United States.
Roger D DiasHarvard Medical School, Boston, Massachusetts, United States.
Haipeng ZhangHarvard Medical School, Boston, Massachusetts, United States.
Andrew EyreHarvard Medical School, Boston, Massachusetts, United States.
Eric IsselbacherHarvard Medical School, Boston, Massachusetts, United States.
Jared ConleyHarvard Medical School, Boston, Massachusetts, United States.
Narath CarlileHarvard Medical School, Boston, Massachusetts, United States.

Funding

Medical Scientist Training ProgramT32GM144273 · NIGMS · HARVARD MEDICAL SCHOOL · PI David Shumway Jones, Jacqueline A. Lees · 2022 to 2026
$14.7M
NIGMS NIH HHS T32 GM144273
6 · The paper itself

Abstract

objectivesVariability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial.

methodsForty-six resident physicians were randomized to lead a simulated code team using the AHA pockets cards (

resultsApp users showed significantly higher ROSC rate (50 vs. 18%;

conclusionOur guided ACLS app shows potential to improve user confidence and adherence to the AHA ACLS guidelines and may help to standardize in-hospital cardiac arrest management. Further validation studies are essential to confirm its efficacy in clinical practice.

Indexed as

Advanced Cardiac Life SupportHeart ArrestFemaleHumansMaleMobile Applications

Identifiers

PMID39357878
PMCPMC11446628

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.