From Department of Ophthalmology, University of Washington, Seattle, Washington, USA (A.C, R.L, C.S.L, A.Y.L).
Optometry and Visual Sciences, City, University of London, London, UK (G.M, D.P.C); NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK (G.M).
Am J Ophthalmol. 2022 Nov;243:118-124. doi: 10.1016/j.ajo.2022.07.013. Epub 2022 Jul 28.
To evaluate whether an artificial intelligence (AI) model can better select candidates that would demonstrate visual field (VF) progression, in order to shorten the duration or the number of patients needed for a clinical trial.
Retrospective cohort study.
7428 eyes of 3871 patients from the University of Washington Department of Ophthalmology VF Dataset were included. Progression was defined as at least 5 locations with >7 dB of change compared with baseline on 2 consecutive tests. Progression for all patients, a subgroup of the fastest progressing based on survival curves, and patients selected based on an elastic net Cox regression model were compared. The model was trained on pointwise threshold deviation values of the first VF, age, gender, laterality, and the mean total deviation (MD) at baseline.
A total of 13% of all patients met the criteria for progression at 5 years. Differences in survival were observed when stratified by MD and age (P < .0001). Those at risk of progression included patients aged 60 to 80 years with an initial MD < -5.0. This subgroup decreased the sample size required to detect progression compared with the entire cohort. The AI model-selected patients required the lowest number of patients for all effect sizes and trial lengths. For a trial length of 3 years and effect size of 30%, the number of patients required was 1656 (95% CI, 1638-1674), 903 (95% CI, 884-922), and 636 (95% CI, 625-646) for the entire cohort, the subgroup, and the model-selected patients, respectively.
An AI model can identify high-risk patients to substantially reduce the number of patients needed or study duration required to meet clinical trial endpoints.
评估人工智能 (AI) 模型是否能更好地选择可能出现视野 (VF) 进展的患者，以便缩短临床试验所需的时间或患者数量。
回顾性队列研究。
纳入来自华盛顿大学眼科 VF 数据集的 3871 名患者的 7428 只眼。进展定义为至少 5 个位置的变化值与基线相比大于 7dB，且在连续 2 次检查中均出现这种情况。比较了所有患者、根据生存曲线确定的进展最快的亚组患者以及根据弹性网络 Cox 回归模型选择的患者。该模型基于首次 VF 的点阈值偏差值、年龄、性别、侧别以及基线时的平均总偏差值 (MD) 进行训练。
所有患者中，有 13%在 5 年内符合进展标准。根据 MD 和年龄分层时，生存差异显著 (P <.0001)。有进展风险的患者包括年龄在 60 至 80 岁之间、初始 MD < -5.0 的患者。与整个队列相比，这一亚组减少了检测进展所需的患者数量。对于所有效果大小和试验长度，AI 模型选择的患者所需的患者数量最少。对于 3 年的试验长度和 30%的效果大小，整个队列、亚组和模型选择的患者分别需要 1656 例 (95%CI，1638-1674)、903 例 (95%CI，884-922) 和 636 例 (95%CI，625-646)。
AI 模型可以识别高风险患者，从而大大减少达到临床试验终点所需的患者数量或研究时间。
