Evidence map›Paper›PMID 36581011›Full record

ArticleJournal of vascular surgery2023

Use of surgical augmented intelligence maps can reduce radiation and improve safety in the endovascular treatment of complex aortic aneurysms.

Rohini J Patel, Arielle M Lee, John Hallsten, John S Lane, Andrew R Barleben, Mahmoud B Malas

Open access · greenAbstract read
In one paragraph

Article in Journal of vascular surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 16% of its field
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

7 citing papers in PubMed, 10 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Rohini J PatelDivision of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
Arielle M LeeDivision of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
John HallstenDivision of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
John S LaneDivision of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
Andrew R BarlebenDivision of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA.
Mahmoud B MalasDivision of Vascular and Endovascular Surgery, Department of Surgery, University of California San Diego, San Diego, CA. Electronic address: mmalas@health.ucsd.edu.
University of California San Diego · USUC San Diego Health System · US

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

objectiveThe introduction of endovascular procedures has revolutionized the management of complex aortic aneurysms. Although repair has traditionally required longer operative times and increased radiation exposure compared with simple endovascular aneurysm repair, the recent introduction of three-dimensional technology has become an invaluable operative adjunct. Surgical augmented intelligence (AI) is a rapidly evolving tool initiated at our institution in June 2019. In our study, we sought to determine whether this technology improved patient and operator safety.

methodsA retrospective review of patients who had undergone endovascular repair of complex aortic aneurysms (pararenal, juxtarenal, or thoracoabdominal), type B dissection, or infrarenal (endoleak, coil placement, or renal angiography with or without intervention) at a tertiary care center from August 2015 to November 2021 was performed. Patients were stratified according to the findings from intelligent maps, which are patient-specific AI tools used in the operating room in conjunction with real-time fluoroscopic images. The primary outcomes included operative time, radiation exposure, fluoroscopy time, and contrast use. The secondary outcomes included 30-day postoperative complications and long-term follow-up. Linear regression models were used to evaluate the association between AI use and the main outcomes.

resultsDuring the 6-year period, 116 patients were included in the present study, with no significant differences in the baseline characteristics. Of the 116 patients, 76 (65.5%) had undergone procedures using AI and 40 (34.5%) had undergone procedures without AI software. The intraoperative outcomes revealed a significant decrease in radiation exposure (AI group, 1955 mGy; vs non-AI group, 3755 mGy; P = .004), a significant decrease in the fluoroscopy time (AI group, 55.6 minutes; vs non-AI group, 86.9 minutes; P = .007), a decrease in the operative time (AI group, 255 minutes; vs non-AI group, 284 minutes; P = .294), and a significant decrease in contrast use (AI group, 123 mL; vs non-AI group, 199 mL; P < .0001). No differences were found in the 30-day and long-term outcomes.

conclusionsThe results from the present study have demonstrated that the use of AI technology combined with intraoperative imaging can significantly facilitate complex endovascular aneurysm repair by decreasing the operative time, radiation exposure, fluoroscopy time, and contrast use. Overall, evolving technology such as AI has improved radiation safety for both the patient and the entire operating room team.

Indexed as

Aortic AneurysmAortic Aneurysm, AbdominalBlood Vessel Prosthesis ImplantationEndovascular ProceduresBlood Vessel ProsthesisHumansPostoperative ComplicationsRetrospective StudiesTreatment OutcomeAortic aneurysmArtificial intelligenceRadiation

Identifiers

PMID36581011
PMCPMC10257362
OpenAlexW4313252043

What OpenQuestion holds

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Registered trials

None linked

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.