Eye ( IF 2.8 ) Pub Date : 2022-07-05 , DOI: 10.1038/s41433-022-02154-8 Leonard M Coulibaly 1 , Stefan Sacu 1 , Philipp Fuchs 1 , Hrvoje Bogunovic 2 , Georg Faustmann 2 , Christian Unterrainer 3 , Gregor S Reiter 2 , Ursula Schmidt-Erfurth 1, 2
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Introduction
In neovascular age-related macular degeneration (nAMD) the exact amount of fluid and its location on optical coherence tomography (OCT) have been defined as crucial biomarkers for disease activity and therapeutic decisions. Yet in the absence of quantitative evaluation tools, real-world care outcomes are disappointing. Artificial intelligence (AI) offers a practical option for clinicians to enhance point-of-care management by analysing OCT volumes in a short time. In this protocol we present the prospective implementation of an AI-algorithm providing automated real-time fluid quantifications in a clinical real-world setting.
Methods
This is a prospective, multicentre, randomized (1:1) and double masked phase III clinical trial. Two-hundred-ninety patients with active nAMD will be randomized between a study arm using AI-supported fluid quantifications and another arm using conventional qualitative assessments, i.e. state-of-the-art disease management. The primary outcome is defined as the mean number of injections over 1 year. Change in BCVA is defined as a secondary outcome.
Discussion
Automated measurement of fluid volumes in all retinal compartments such as intraretinal fluid (IRF), and subretinal fluid (SRF) will serve as an objective tool for clinical investigators on which to base retreatment decisions. Compared to qualitative fluid assessment, retreatment decisions will be plausible and less prone to error or large variability. The underlying hypothesis is that fluid should be treated, while residual persistent or stable amounts of fluid may not benefit from further therapy. Reducing injection numbers without diminishing the visual benefit will increase overall patient safety and relieve the burden for healthcare providers.
Trial-registration
EudraCT-Number: 2019-003133-42
中文翻译：
活动性新生血管性年龄相关性黄斑变性 (nAMD) 中自动定量液体分析支持的个性化治疗——一项 III 期、前瞻性、多中心、随机研究：设计和方法
介绍
在新生血管性年龄相关性黄斑变性 (nAMD) 中，精确的液体量及其在光学相干断层扫描 (OCT) 上的位置已被定义为疾病活动和治疗决策的关键生物标志物。然而，在缺乏定量评估工具的情况下，现实世界的护理结果令人失望。人工智能 (AI) 为临床医生提供了一种实用的选择，可以通过在短时间内分析 OCT 体积来加强护理点管理。在本协议中，我们介绍了 AI 算法的前瞻性实施，该算法在临床现实环境中提供自动实时流体量化。
方法
这是一项前瞻性、多中心、随机（1:1）和双盲的 III 期临床试验。290 名活动性 nAMD 患者将被随机分配到使用 AI 支持的液体量化的研究组和使用常规定性评估（即最先进的疾病管理）的另一组。主要结果定义为 1 年内的平均注射次数。BCVA 的变化被定义为次要结果。
讨论
自动测量所有视网膜隔室中的液体体积，如视网膜内液体 (IRF) 和视网膜下液体 (SRF) 将作为临床研究人员做出再治疗决定的客观工具。与定性液体评估相比，再治疗决策将是合理的，并且不易出错或出现大的变异性。潜在的假设是液体应该被治疗，而残余的持续或稳定量的液体可能不会从进一步的治疗中获益。在不降低视觉效果的情况下减少注射次数将提高患者的整体安全性并减轻医疗保健提供者的负担。
试用注册
EudraCT-编号：2019-003133-42
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