Dataset Information

Predicting vision gains with anti-VEGF therapy in neovascular age-related macular degeneration patients by using low-luminance vision.

ABSTRACT: BACKGROUND/AIMS:To evaluate baseline low-luminance visual acuity (LLVA) as a predictor of visual acuity improvement in patients with neovascular (wet) age-related macular degeneration (wAMD) receiving antivascular endothelial growth factor A (anti-VEGF) therapy. METHODS:In the HARBOR trial, 1084 treatment-naïve patients ?50?years of age with subfoveal wAMD received intravitreal ranibizumab 0.5 or 2.0?mg monthly or as needed. To measure LLVA, patients read a normally illuminated ETDRS (Early Treatment Diabetic Retinopathy Study) chart with a neutral density filter placed in front of the study eye. Patients were assigned into quartiles based on the magnitude of the difference between best-corrected visual acuity under optimal luminance (BCVA) and LLVA (BCVA-LLVA gap). The association between mean change in BCVA from baseline and BCVA-LLVA gap at baseline was analysed using a general linear model. RESULTS:A smaller baseline BCVA-LLVA gap predicted significantly higher BCVA gains over 24?months (p<0.0001 at each month; Pearson correlation), even after controlling for baseline BCVA or stratifying by treatment arm. Patients in the smallest baseline BCVA-LLVA gap quartile gained an average of +13.4 letters compared with +2.4 letters for patients in the widest baseline BCVA-LLVA gap quartile. At months 12 and 24, the smallest baseline BCVA-LLVA gap quartile had the highest proportion of ?15-?30-letter gain, and the widest baseline BCVA-LLVA gap quartile had the highest proportion of ?15-/?30-letter loss (p<0.0001; Fisher's exact test). CONCLUSIONS:The baseline BCVA-LLVA gap is a significant predictor of visual acuity response to anti-VEGF treatment in patients with wAMD. TRIAL REGISTRATION NUMBER:NCT00891735; Post-results.

SUBMITTER: Frenkel RE

PROVIDER: S-EPMC4975848 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Predicting vision gains with anti-VEGF therapy in neovascular age-related macular degeneration patients by using low-luminance vision.

Frenkel Ronald E P RE Shapiro Howard H Stoilov Ivaylo I

The British journal of ophthalmology 20151105 8

<h4>Background/aims</h4>To evaluate baseline low-luminance visual acuity (LLVA) as a predictor of visual acuity improvement in patients with neovascular (wet) age-related macular degeneration (wAMD) receiving antivascular endothelial growth factor A (anti-VEGF) therapy.<h4>Methods</h4>In the HARBOR trial, 1084 treatment-naïve patients ≥50 years of age with subfoveal wAMD received intravitreal ranibizumab 0.5 or 2.0 mg monthly or as needed. To measure LLVA, patients read a normally illuminated ET ...[more]

PMID: 26541435

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Project description:PurposeTo probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.MethodsSD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.ResultsIn a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.ConclusionsThe proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.Translational relevancePrediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Project description:PurposeNo established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.DesignAssessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.ParticipantsEighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).MethodsA total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.Main outcome measuresThe cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.ResultsThe cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.ConclusionsRadiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Project description:ImportanceMost neovascular age-related macular degeneration (nAMD) treatments involve long-term follow-up of disease activity. Home-monitoring would reduce the burden on patients and their caregivers and release clinic capacity.ObjectiveTo evaluate 3 vision home-monitoring tests for patients to use to detect active nAMD compared with diagnosing active nAMD at hospital follow-up during the after-treatment monitoring phase.Design, setting, and participantsThis was a diagnostic test accuracy study wherein the reference standard was detection of active nAMD by an ophthalmologist at hospital follow-up. The 3 home-monitoring tests evaluated included the following: (1) the KeepSight Journal (KSJ [International Macular and Retinal Foundation]), which contains paper-based near-vision tests presented as word puzzles, (2) the MyVisionTrack (mVT [Genentech]) vision-monitoring mobile app, viewed on an Apple mobile operating system-based device, and (3) the MultiBit (MBT [Visumetrics]) app, viewed on an Apple mobile operating system-based device. Participants were asked to test weekly; mVT and MBT scores were transmitted automatically, and KSJ scores were returned to the research office every 6 months. Raw scores between hospital follow-ups were summarized as averages. Patients were recruited from 6 UK hospital eye clinics and were 50 years and older with at least 1 eye first treated for active nAMD for at least 6 months or longer to a maximum of 42 months before approach. Participants were stratified by time since starting treatment. Study data were analyzed from May to September 2021.ExposuresThe KSJ, mVT, and MBT were compared with the reference standard (in-hospital ophthalmologist examination).Main outcomes and measuresEstimated area under receiver operating characteristic curve (AUROC). The study had 90% power to detect a difference of 0.06, or 80% power to detect a difference of 0.05, if the AUROC for 2 tests was 0.75.ResultsA total of 297 patients (mean [SD] age, 74.9 [6.6] years; 174 female [58.6%]) were included in the study. At least 1 hospital follow-up was available for 312 study eyes in 259 participants (1549 complete visits). Median (IQR) home-monitoring testing frequency was 3 (1-4) times per month. Estimated AUROC was less than 0.6 for all home-monitoring tests, and only the KSJ summary score was associated with lesion activity (odds ratio, 3.48; 95% CI, 1.09-11.13; P = .04).Conclusions and relevanceResults suggest that no home-monitoring vision test evaluated provided satisfactory diagnostic accuracy to identify active nAMD diagnosed in hospital eye service follow-up clinics. Implementing any of these evaluated tests, with ophthalmologists only reviewing test positives, would mean most active lesions were missed, risking unnecessary sight loss.

Dataset Information

Predicting vision gains with anti-VEGF therapy in neovascular age-related macular degeneration patients by using low-luminance vision.

Publications

Predicting vision gains with anti-VEGF therapy in neovascular age-related macular degeneration patients by using low-luminance vision.

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