Bergerot Jean-François, Crombé Amandine, Seux Mylène, Porta Basile, Fyon Vanessa, Nivet Samuel Le, Lippa Nicolas, Peyre Rémi, Etchart Paul, Gay Frédérique, Gorincour Guillaume
IMADIS Groupe, 48 rue Quivogne, Lyon, 69002, France.
Ramsay Générale de Santé, Clinique Convert, Bourg-en-Bresse, 01000, France.
BMC Med Imaging. 2025 May 1;25(1):143. doi: 10.1186/s12880-025-01670-9.
A specific grid for analyzing and grading adverse events in diagnostic radiology is lacking. In France, the standard HAS grid, a generic 5-point scale adapted from the Common Terminology Criteria for Adverse Events (CTCAEs), is criticized for limited applicability in radiology. Our aim was to develop and evaluate a radiology-specific AE grid (AE-RADS) tailored to diagnostic and teleradiological practices and to compare its performance against the CTCAEs-based HAS grid regarding inter-observer reproducibility and agreement with expert consensus.
AE-RADS, structured as a decision tree with 90 items, was developed by four senior radiologists with extensive AE experience. To assess it, 100 AE cases from early 2022 were reviewed by two radiologists and two non-physician support members, all blinded to the initial AE grading. Observers rated AEs using both the HAS and AE-RADS grids, comparing severity, AE frequency per patient, sources, and types for inter-observer reproducibility and expert agreement. Tests included intra-class correlation coefficient (ICC), Fleiss Kappa and Krippendorff alpha for reproducibility and McNemar test for comparing agreement with consensus.
Among 100 patients (49 women, median age 66.9 years), 104 AEs were identified. AE-RADS achieved higher inter-observer reproducibility for AE frequency (ICC = 0.690 vs. 0.642 with HAS) and for grading the most serious AE (Krippendorff alpha = 0.519 vs. 0.506 with HAS). Agreement with expert consensus was significantly greater with AE-RADS (63-81%) than with HAS (25-47%; P-value range: 0.0001-0.0051).
AE-RADS shows improved, though still imperfect, agreement between evaluators and experts, supporting its potential for more precise AE assessment in diagnostic imaging.
目前缺乏用于分析和分级诊断放射学不良事件的特定网格。在法国，标准的HAS网格是一种从不良事件通用术语标准（CTCAE）改编而来的通用5级量表，因其在放射学中的适用性有限而受到批评。我们的目的是开发和评估一种针对诊断和远程放射学实践量身定制的放射学特定不良事件网格（AE-RADS），并就观察者间的可重复性以及与专家共识的一致性，将其性能与基于CTCAE的HAS网格进行比较。
由四名具有丰富不良事件经验的资深放射科医生开发了AE-RADS，它被构建为一个包含90项内容的决策树。为了对其进行评估，两名放射科医生和两名非医生支持人员对2022年初的100例不良事件病例进行了回顾，所有人员均对初始不良事件分级不知情。观察者使用HAS和AE-RADS网格对不良事件进行评级，比较严重程度、每位患者的不良事件频率、来源和类型，以评估观察者间的可重复性和专家一致性。测试包括用于可重复性的组内相关系数（ICC）、Fleiss Kappa和Krippendorff alpha，以及用于比较与共识一致性的McNemar检验。
在100名患者（49名女性，中位年龄66.9岁）中，共识别出104例不良事件。AE-RADS在不良事件频率的观察者间可重复性方面（ICC = 0.690，而HAS为0.642）以及对最严重不良事件的分级方面（Krippendorff alpha = 0.519，而HAS为0.506）表现更高。与专家共识的一致性在AE-RADS方面（63 - 81%）显著高于HAS（25 - 47%；P值范围：0.0001 - 0.0051）。
AE-RADS显示评估者与专家之间的一致性有所改善，尽管仍不完美，这支持了其在诊断成像中进行更精确不良事件评估的潜力。
