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Analyzing and Predicting Visual Acuity Outcomes of Anti-VEGF Therapy by a Longitudinal Mixed Effects Model of Imaging and Clinical Data.

Abstract 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.
PMID
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Authors

Mayor MeshTerms

Models, Statistical

Keywords
Journal Title investigative ophthalmology & visual science
Publication Year Start




PMID- 28837729
OWN - NLM
STAT- MEDLINE
DA  - 20170824
DCOM- 20170904
LR  - 20170904
IS  - 1552-5783 (Electronic)
IS  - 0146-0404 (Linking)
VI  - 58
IP  - 10
DP  - 2017 Aug 01
TI  - Analyzing and Predicting Visual Acuity Outcomes of Anti-VEGF Therapy by a
      Longitudinal Mixed Effects Model of Imaging and Clinical Data.
PG  - 4173-4181
LID - 10.1167/iovs.17-21878 [doi]
AB  - 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.
FAU - Vogl, Wolf-Dieter
AU  - Vogl WD
AD  - Computational Imaging Research Lab, Department of Biomedical Imaging and
      Image-guided Therapy, Medical University Vienna, Austria.
AD  - Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of
      Ophthalmology and Optometry, Medical University Vienna, Austria.
FAU - Waldstein, Sebastian M
AU  - Waldstein SM
AD  - Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of
      Ophthalmology and Optometry, Medical University Vienna, Austria.
FAU - Gerendas, Bianca S
AU  - Gerendas BS
AD  - Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of
      Ophthalmology and Optometry, Medical University Vienna, Austria.
FAU - Schlegl, Thomas
AU  - Schlegl T
AD  - Computational Imaging Research Lab, Department of Biomedical Imaging and
      Image-guided Therapy, Medical University Vienna, Austria.
AD  - Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of
      Ophthalmology and Optometry, Medical University Vienna, Austria.
FAU - Langs, Georg
AU  - Langs G
AD  - Computational Imaging Research Lab, Department of Biomedical Imaging and
      Image-guided Therapy, Medical University Vienna, Austria.
FAU - Schmidt-Erfurth, Ursula
AU  - Schmidt-Erfurth U
AD  - Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of
      Ophthalmology and Optometry, Medical University Vienna, Austria.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Invest Ophthalmol Vis Sci
JT  - Investigative ophthalmology & visual science
JID - 7703701
RN  - 0 (Angiogenesis Inhibitors)
RN  - 0 (VEGFA protein, human)
RN  - 0 (Vascular Endothelial Growth Factor A)
RN  - ZL1R02VT79 (Ranibizumab)
SB  - IM
MH  - Aged
MH  - Angiogenesis Inhibitors/*therapeutic use
MH  - Clinical Trials as Topic
MH  - Databases, Factual
MH  - Female
MH  - Follow-Up Studies
MH  - Humans
MH  - Intravitreal Injections
MH  - Male
MH  - Middle Aged
MH  - *Models, Statistical
MH  - Ranibizumab/*therapeutic use
MH  - Retinal Vein Occlusion/diagnosis/*drug therapy/physiopathology
MH  - Subretinal Fluid/physiology
MH  - Tomography, Optical Coherence
MH  - Treatment Outcome
MH  - Vascular Endothelial Growth Factor A/*antagonists & inhibitors
MH  - Visual Acuity/*physiology
EDAT- 2017/08/25 06:00
MHDA- 2017/09/05 06:00
CRDT- 2017/08/25 06:00
AID - 2650869 [pii]
AID - 10.1167/iovs.17-21878 [doi]
PST - ppublish
SO  - Invest Ophthalmol Vis Sci. 2017 Aug 1;58(10):4173-4181. doi:
      10.1167/iovs.17-21878.