Evidence map›Paper›PMID 41155024›Full record

ArticleBioengineering (Basel, Switzerland)2025

Can Artificial Intelligence Improve the Appropriate Use and Decrease the Misuse of REBOA?

Mary Bokenkamp, Yu Ma, Ander Dorken-Gallastegi, Jefferson A Proaño-Zamudio, Anthony Gebran, George C Velmahos, Dimitris Bertsimas, Haytham M A Kaafarani

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

8 authors.

Mary BokenkampTrauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA.
Yu MaOperations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.ORCID 0000-0001-8409-7222
Ander Dorken-GallastegiTrauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA.
Jefferson A Proaño-ZamudioTrauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA.
Anthony GebranTrauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA.
George C VelmahosTrauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA.
Dimitris BertsimasOperations Research Center, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
Haytham M A KaafaraniTrauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, Boston, MA 02114, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of resuscitative endovascular balloon occlusion of the aorta (REBOA) for control of noncompressible torso hemorrhage remains controversial. We aimed to utilize a novel and transparent/interpretable artificial intelligence (AI) method called Optimal Policy Trees (OPTs) to improve the appropriate use and decrease the misuse of REBOA in hemodynamically unstable blunt trauma patients.

methodsWe trained and then validated OPTs that "prescribe" REBOA in a 50:50 split on all hemorrhagic shock blunt trauma patients in the 2010-2019 ACS-TQIP database based on rates of survival. Hemorrhagic shock was defined as a systolic blood pressure ≤90 on arrival or a transfusion requirement of ≥4 units of blood in the first 4 h of presentation. The expected 24 h mortality rate following OPT prescription was compared to the observed 24 h mortality rate in patients who were or were not treated with REBOA.

resultsOut of 4.5 million patients, 100,615 were included, and 803 underwent REBOA. REBOA patients had a higher rate of pelvic fracture, femur fracture, hemothorax, pneumothorax, and thoracic aorta injury (

conclusionsThis proof-of-concept study shows that interpretable AI models can improve mortality in unstable blunt trauma patients by optimizing the use and decreasing the misuse of REBOA. To date, these models have been used to predict outcomes, but their groundbreaking use will be in prescribing interventions and changing outcomes.

Indexed as

artificial intelligenceblunt traumaREBOA

Identifiers

PMID41155024
PMCPMC12561464

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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.