Evidence map›Paper›PMID 39792307›Full record

ArticleSurgery today2025

Efficacy of robot-assisted double-flap techniques with refinements to minimize anastomosis-related complications after proximal gastrectomy.

Eiichiro Nakao, Masataka Igeta, Motoki Murakami, Shugo Kohno, Yudai Hojo, Tatsuro Nakamura, Yasunori Kurahashi, Yoshinori Ishida, Hisashi Shinohara

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Article in Surgery today, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Eiichiro NakaoDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Masataka IgetaDepartment of Biostatistics Center for Clinical Research and Education, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Motoki MurakamiDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Shugo KohnoDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Yudai HojoDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Tatsuro NakamuraDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Yasunori KurahashiDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Yoshinori IshidaDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan.
Hisashi ShinoharaDepartment of Gastroenterological Surgery, Hyogo Medical University, 1-1 Mukogawa, Nishinomiya, Hyogo, 663-8501, Japan. shinohara@hyo-med.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe double-flap technique (DFT) is an anti-reflux reconstruction procedure performed after proximal gastrectomy (PG), but its complexity and high incidence of anastomotic stenosis are problematic. We conducted this study to demonstrate the efficacy of robot-assisted DFT, with refinements, to address these issues.

methodsSurgical outcomes were compared between the following procedures modified over time at our institution: conventional open DFT (group O, n = 16); early robotic DFT (group RE, n = 19), which follows the conventional open PG approach; and late robotic DFT (group RL, n = 21), which incorporates refinements to the early robotic DFT technique by exploiting more of the robotic capabilities available. These robotic capabilities include pre-anchoring the stomach to the diaphragmatic crus and connecting it to the esophagus, placing interrupted sutures for anastomosis, and maximally embedding the esophagus through the entire flap.

resultsAnastomotic stenosis requiring balloon dilatation developed in two (11.8%) patients from group O and three (14.3%) from group RE, but not in any patients from group RL. Reflux esophagitis, classified as Los Angeles Classification grade ≥ B, decreased from 25.0% in group O to 10.5% in group RE and was completely controlled in group RL (group RL vs group O, odds ratio 0.065, 95% confidence interval < 0.001-0.684, P = 0.007).

conclusionRobotic surgery helps minimize anastomotic stenosis, while enhancing the benefits of anti-reflux reconstruction with DFT.

Indexed as

Anastomosis, SurgicalEsophagusGastrectomyPlastic Surgery ProceduresPostoperative ComplicationsRobotic Surgical ProceduresStomachSurgical FlapsAgedConstriction, PathologicEsophageal StenosisEsophagitis, PepticFemaleHumansMaleMiddle AgedComplicationDouble-flap techniqueGastric cancerProximal gastrectomyRobot

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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.