Vogl Wolf-Dieter, Waldstein Sebastian M, Gerendas Bianca S, Schlegl Thomas, Langs Georg, Schmidt-Erfurth Ursula
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Austria.
Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Austria.
Invest Ophthalmol Vis Sci. 2017 Aug 1;58(10):4173-4181. doi: 10.1167/iovs.17-21878.
We develop a longitudinal statistical model describing best-corrected visual acuity (BCVA) changes in anti-VEGF therapy in relation to imaging data, and predict the future BCVA outcome for individual patients by combining population-wide trends and initial subject-specific time points.
Automatic segmentation algorithms were used to measure intraretinal (IRF) and subretinal (SRF) fluid volume on monthly spectral-domain optical coherence tomography scans of eyes with central retinal vein occlusion (CRVO) receiving standardized anti-VEGF treatment. The trajectory of BCVA over time was modeled as a multivariable repeated-measure mixed-effects regression model including fluid volumes as covariates. Subject-specific BCVA trajectories and final treatment outcomes were predicted using a population-wide model and individual observations from early follow-up.
A total of 193 eyes (one per patient, 12-month follow-up, 2420 visits) were analyzed. The population-wide mixed model revealed that the impact of fluid on BCVA is highest for IRF in the central millimeter around the fovea, with -31.17 letters/mm3 (95% confidence interval [CI], -39.70 to -23.32), followed by SRF in the central millimeter, with -17.50 letters/mm3 (-31.17 to -4.60) and by IRF in the parafovea, with -2.87 letters/mm3 (-4.71 to -0.44). The influence of SRF in the parafoveal area was -1.24 letters/mm3 (-3.37-1.05). The conditional R2 of the model, including subject-specific deviations, was 0.887. The marginal R2 considering the population-wide trend and fluid changes was 0.109. BCVA at 1 year could be predicted for an individual patient after three visits with a mean absolute error of six letters and a predicted R2 of 0.658 using imaging information.
The mixed-effects model revealed that retinal fluid volumes and population-wide trend only explains a small proportion of the variation in BCVA. Individual BCVA outcomes after 1 year could be predicted from initial BCVA and fluid measurements combined with the population-wide model. Accounting for fluid in the predictive model increased prediction accuracy.
我们开发一种纵向统计模型，描述抗血管内皮生长因子（VEGF）治疗中最佳矫正视力（BCVA）的变化与成像数据的关系，并通过结合全人群趋势和个体特定的初始时间点来预测个体患者未来的BCVA结果。
使用自动分割算法，对接受标准化抗VEGF治疗的视网膜中央静脉阻塞（CRVO）患者的眼睛进行每月一次的光谱域光学相干断层扫描，测量视网膜内（IRF）和视网膜下（SRF）液体积聚。将BCVA随时间的变化轨迹建模为多变量重复测量混合效应回归模型，将液体积聚作为协变量。使用全人群模型和早期随访的个体观察结果预测个体特定的BCVA轨迹和最终治疗结果。
共分析了193只眼睛（每位患者一只眼睛，随访12个月，2420次就诊）。全人群混合模型显示，在黄斑中心周围1毫米范围内，IRF对BCVA的影响最大，为-31.17字母/立方毫米（95%置信区间[CI]，-39.70至-23.32），其次是黄斑中心1毫米范围内的SRF，为-17.50字母/立方毫米（-31.17至-4.60），以及黄斑旁的IRF，为-2.87字母/立方毫米（-4.71至-0.44）。黄斑旁区域SRF的影响为-1.24字母/立方毫米（-3.37至1.05）。包括个体特定偏差在内的模型条件R2为0.887。考虑全人群趋势和液体变化的边际R2为0.109。在三次就诊后，利用成像信息可以预测个体患者1年时的BCVA，平均绝对误差为6个字母，预测R2为0.658。
混合效应模型显示，视网膜液体积聚和全人群趋势仅解释了BCVA变化的一小部分。通过初始BCVA和液体测量结果结合全人群模型，可以预测个体1年后的BCVA结果。在预测模型中考虑液体因素可提高预测准确性。
