Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.
Purpose: 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. Methods: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. Results: 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. Conclusions: 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.
Authors: Philipp K Roberts; Wolf-Dieter Vogl; Bianca S Gerendas; Adam R Glassman; Hrvoje Bogunovic; Lee M Jampol; Ursula M Schmidt-Erfurth Journal: JAMA Ophthalmol Date: 2020-09-01 Impact factor: 7.389
Authors: Cynthia A Toth; Vincent Tai; Maxwell Pistilli; Stephanie J Chiu; Katrina P Winter; Ebenezer Daniel; Juan E Grunwald; Glenn J Jaffe; Daniel F Martin; Gui-Shuang Ying; Sina Farsiu; Maureen G Maguire Journal: Ophthalmol Retina Date: 2018-12-03
Authors: Rene Y Choi; Aaron S Coyner; Jayashree Kalpathy-Cramer; Michael F Chiang; J Peter Campbell Journal: Transl Vis Sci Technol Date: 2020-02-27 Impact factor: 3.283
Authors: Thomas Kurmann; Siqing Yu; Pablo Márquez-Neila; Andreas Ebneter; Martin Zinkernagel; Marion R Munk; Sebastian Wolf; Raphael Sznitman Journal: Sci Rep Date: 2019-09-19 Impact factor: 4.379
Authors: Yan Gao; Yi Chong Kelvin Teo; Roger W Beuerman; Tien Yin Wong; Lei Zhou; Chui Ming Gemmy Cheung Journal: Sci Rep Date: 2020-01-28 Impact factor: 4.379
Authors: Marion R Munk; Thomas Kurmann; Pablo Márquez-Neila; Martin S Zinkernagel; Sebastian Wolf; Raphael Sznitman Journal: Sci Rep Date: 2021-04-21 Impact factor: 4.379