Suppr超能文献

欧洲泌尿外科学会(EAU)专门并发症指南小组制定的术中不良事件分类(EAUiaiC)。

Intraoperative Adverse Incident Classification (EAUiaiC) by the European Association of Urology ad hoc Complications Guidelines Panel.

机构信息

Department of Urology, St. James's University Hospital, Leeds, UK.

Medical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands.

出版信息

Eur Urol. 2020 May;77(5):601-610. doi: 10.1016/j.eururo.2019.11.015. Epub 2019 Nov 29.

Abstract

BACKGROUND

A surgical adverse incident (AI) is defined as any deviation from the normal operative course. Current complication-grading systems mostly focus on postoperative events.

OBJECTIVE

To propose an intraoperative AI classification (EAUiaiC) to facilitate reporting.

DESIGN, SETTING, AND PARTICIPANTS: The classification was developed using a modified Delphi process in which experts answered two rounds of survey questionnaires organised by the European Association of Urology ad hoc Complications Guidelines Panel. Experts evaluated AI terminology using a 5-point Likert scale for clarity, exhaustiveness, hierarchical order, mutual exclusivity, clinical utility, and quality improvement.

OUTCOME MEASURES AND STATISTICAL ANALYSIS

We considered ≥70% agreement for inclusion or exclusion. The resultant EAUiaiC was evaluated using ten sample clinical scenarios. Numerical and graphical statistical techniques were used to report the results.

RESULTS AND LIMITATIONS

In total, 343 respondents participated. The proposed EAUiaiC system comprises eight AI grades ranging from grade 0 (no deviation and no consequence to the patient) to grade 5B (wrong surgery site or intraoperative death). In the validation stage, EAUiaiC was rated highly favourably in terms of relevance and reliability (consistency) by 126 experts. Ratings for self-reported ease of use were at acceptable levels.

CONCLUSIONS

We propose a novel intraoperative AI classification (EAUiaiC) for use for urological procedures. Both the initial assessment of feasibility and the subsequent assessment of reliability suggest that it is a simple and effective tool for classifying intraoperative complications.

PATIENT SUMMARY

Complications in surgery are common. It is helpful to classify complications in a uniform and objective manner so that surgeons can easily compare outcomes and learn from complications. Here we propose a new classification system for complications that occur during urological surgical procedures. An abstract of this work was presented at the 2018 congress of the European Association of Urology.

摘要

背景

手术不良事件 (AI) 被定义为任何偏离正常手术过程的情况。目前的并发症分级系统大多侧重于术后事件。

目的

提出一种术中 AI 分类 (EAUiaiC) 以方便报告。

设计、设置和参与者:该分类使用改良 Delphi 过程开发,专家通过欧洲泌尿外科学会 (EAU) 并发症指南特别委员会组织的两轮调查问卷回答问题。专家使用 5 分李克特量表评估 AI 术语的清晰度、全面性、层次顺序、互斥性、临床实用性和质量改进。

测量和统计分析

我们认为≥70%的专家同意纳入或排除。使用十个临床案例评估最终的 EAUiaiC。使用数值和图形统计技术报告结果。

结果和局限性

共有 343 名受访者参与。拟议的 EAUiaiC 系统包括 8 个 AI 等级,从 0 级(无偏差且对患者无影响)到 5B 级(手术部位错误或术中死亡)。在验证阶段,126 名专家认为 EAUiaiC 在相关性和可靠性(一致性)方面评价很高。自我报告的易用性评分处于可接受水平。

结论

我们提出了一种新的用于泌尿外科手术的术中 AI 分类 (EAUiaiC)。初步评估可行性和随后评估可靠性均表明,它是一种简单有效的分类术中并发症的工具。

患者总结

手术并发症很常见。以统一和客观的方式对并发症进行分类有助于外科医生轻松比较结果并从并发症中吸取经验。这里我们提出了一种用于泌尿外科手术并发症的新分类系统。该工作的摘要在 2018 年欧洲泌尿外科学会大会上发表。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验