Immunology, Eli Lilly and Company, Indianapolis, Indiana, USA
A.O. Ordine Mauriziano di Torino, Torino, Italy.
Gut. 2021 Feb;70(2):418-426. doi: 10.1136/gutjnl-2020-320690. Epub 2020 Jul 22.
Central reading, that is, independent, off-site, blinded review or reading of imaging endpoints, has been identified as a crucial component in the conduct and analysis of inflammatory bowel disease clinical trials. Central reading is the final step in a workflow that has many parts, all of which can be improved. Furthermore, the best reading algorithm and the most intensive central reader training cannot make up for deficiencies in the acquisition stage (clinical trial endoscopy) or improve on the limitations of the underlying score (outcome instrument). In this review, academic and industry experts review scoring systems, and propose a theoretical framework for central reading that predicts when improvements in statistical power, affecting trial size and chances of success, can be expected: Multireader models can be conceptualised as statistical or non-statistical (social). Important organisational and operational factors, such as training and retraining of readers, optimal bowel preparation for colonoscopy, video quality, optimal or at least acceptable read duration times and other quality control matters, are addressed as well. The theory and practice of central reading and the conduct of endoscopy in clinical trials are interdisciplinary topics that should be of interest to many, regulators, clinical trial experts, gastroenterology societies and those in the academic community who endeavour to develop new scoring systems using traditional and machine learning approaches.
中心读片,即独立的、场外的、盲法的影像学终点评估或读片,已被确定为炎症性肠病临床试验开展和分析的关键组成部分。中心读片是工作流程的最后一步,该流程有许多环节,所有环节都可以改进。此外,最佳的读片算法和最密集的中心读片者培训都无法弥补采集阶段(临床试验内镜)的缺陷,也无法改善潜在评分(结局评估工具)的局限性。在这篇综述中,学术和行业专家回顾了评分系统,并提出了一个中心读片的理论框架,预测何时可以提高统计效能,影响试验规模和成功率:多读者模型可以被概念化为统计或非统计(社会性)。还讨论了重要的组织和操作因素,如读者的培训和再培训、结肠镜检查的最佳肠道准备、视频质量、最佳或至少可接受的读片时间以及其他质量控制事项。中心读片的理论和实践以及临床试验中的内镜操作是跨学科的主题,应该引起许多人的兴趣,包括监管机构、临床试验专家、胃肠病学会以及那些致力于使用传统和机器学习方法开发新评分系统的学术界人士。