经阴道自然孔腔内窥镜手术在卵巢囊肿切除术中的学习曲线分析。

IF 3.1 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Frontiers in Medicine Pub Date : 2025-06-13 eCollection Date: 2025-01-01 DOI:10.3389/fmed.2025.1629418
Qiang Zhang, Wenting Dong, Biao Huang, Aijie Xie, Zhaolin Gong, Dan Feng, Li He, Yonghong Lin
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引用次数: 0

摘要

目的:本研究旨在评估经阴道自然孔腔内窥镜手术(vNOTES)在卵巢囊肿切除术中的学习曲线,并确定影响手术时间的围手术期因素。方法:本前瞻性观察研究纳入了2022年6月至2024年6月在成都市妇女儿童中心医院行vNOTES卵巢囊肿切除术的39例患者。根据手术小组自我评估的熟练程度,将患者分为两个手术阶段。使用手术时间累积和分析(CUSUMOT)来建模学习曲线并定义不同的学习阶段。采用多元线性回归来确定手术时间的独立预测因素。结果:患者平均年龄35.14 ± 9.73 岁,平均手术时间74.01 ± 30.09 min。术中转经脐腹腔镜3例(7.7%),围手术期并发症2例(5.1%)。CUSUMOT分析显示学习阶段分为学习阶段(9例)、平台阶段(10例)、挑战阶段(12例)和成熟阶段(8例)。成熟期的手术时间明显短于早期。多元回归发现盆腔粘连(β = 6.92,p = 0.027),双边囊肿(β = 6.38,p = 0.019),囊肿直径(β = 每厘米,2.85 p = 0.026),和学习阶段(β = -17.10 二期,p = 0.035),手术时间的独立预测指标。结论:vNOTES是一种安全可行的卵巢囊肿切除术方法,学习曲线可测量。大约20例后即可熟练使用。盆腔粘连、囊肿特征和手术经验显著影响手术时间。CUSUM分析是评估手术能力和指导vNOTES程序临床培训的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the learning curve for transvaginal natural orifice transluminal endoscopic surgery in ovarian cystectomy.

Objective: This study aimed to evaluate the learning curve for transvaginal natural orifice transluminal endoscopic surgery (vNOTES) in ovarian cystectomy and to identify perioperative factors influencing operative time.

Methods: This prospective observational study included 39 patients who underwent vNOTES ovarian cystectomy at Chengdu Women's and Children's Central Hospital between June 2022 and June 2024. Patients were grouped into two surgical phases based on the operating team's self-assessed proficiency. Cumulative sum analysis of operative time (CUSUMOT) was used to model the learning curve and define distinct learning stages. Multivariate linear regression was performed to identify independent predictors of operative time.

Results: The mean patient age was 35.14 ± 9.73 years, and the mean operative time was 74.01 ± 30.09 min. Three cases (7.7%) required intraoperative conversion to transumbilical laparoscopy, and two patients (5.1%) experienced perioperative complications. CUSUMOT analysis revealed four distinct learning phases: learning (9 cases), plateau (10 cases), challenging (12 cases), and mature (8 cases). Operative time during the mature phase was significantly shorter than in earlier phases. Multivariate regression identified pelvic adhesions (β = 6.92, p = 0.027), bilateral cysts (β = 6.38, p = 0.019), cyst diameter (β = 2.85 per cm, p = 0.026), and learning curve phase (β = -17.10 for Phase II, p = 0.035) as independent predictors of operative time.

Conclusion: vNOTES is a safe and feasible approach for ovarian cystectomy with a measurable learning curve. Proficiency can be achieved after approximately 20 cases. Pelvic adhesions, cyst characteristics, and surgical experience significantly impact operative time. CUSUM analysis is a useful tool for evaluating surgical competency and guiding clinical training in vNOTES procedures.

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来源期刊
Frontiers in Medicine
Frontiers in Medicine Medicine-General Medicine
CiteScore
5.10
自引率
5.10%
发文量
3710
审稿时长
12 weeks
期刊介绍: Frontiers in Medicine publishes rigorously peer-reviewed research linking basic research to clinical practice and patient care, as well as translating scientific advances into new therapies and diagnostic tools. Led by an outstanding Editorial Board of international experts, this multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. In addition to papers that provide a link between basic research and clinical practice, a particular emphasis is given to studies that are directly relevant to patient care. In this spirit, the journal publishes the latest research results and medical knowledge that facilitate the translation of scientific advances into new therapies or diagnostic tools. The full listing of the Specialty Sections represented by Frontiers in Medicine is as listed below. As well as the established medical disciplines, Frontiers in Medicine is launching new sections that together will facilitate - the use of patient-reported outcomes under real world conditions - the exploitation of big data and the use of novel information and communication tools in the assessment of new medicines - the scientific bases for guidelines and decisions from regulatory authorities - access to medicinal products and medical devices worldwide - addressing the grand health challenges around the world
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