Dataset Information

Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy.

ABSTRACT:

Purpose

The accuracy of predicting conversion from early-stage age-related macular degeneration (AMD) to the advanced stages of choroidal neovascularization (CNV) or geographic atrophy (GA) was evaluated to determine whether inclusion of clinically relevant genetic markers improved accuracy beyond prediction using phenotypic risk factors alone.

Design

Cohort study.

Participants

White, non-Hispanic subjects participating in the Age-Related Eye Disease Study (AREDS) sponsored by the National Eye Institute consented to provide a genetic specimen. Of 2415 DNA specimens available, 940 were from disease-free subjects and 1475 were from subjects with early or intermediate AMD.

Methods

DNA specimens from study subjects were genotyped for 14 single nucleotide polymorphisms (SNPs) in genes shown previously to associate with CNV: ARMS2, CFH, C3, C2, FB, CFHR4, CFHR5, and F13B. Clinical demographics and established disease associations, including age, sex, smoking status, body mass index (BMI), AREDS treatment category, and educational level, were evaluated. Four multivariate logistic models (phenotype; genotype; phenotype + genotype; and phenotype + genotype + demographic + environmental factors) were tested using 2 end points (CNV, GA). Models were fitted using Cox proportional hazards regression to use time-to-disease onset data.

Main outcome measures

Brier score (measure of accuracy) was used to identify the model with the lowest prediction error in the training set. The most accurate model was subjected to independent statistical validation, and final model performance was described using area under the receiver operator curve (AUC) or C-statistic.

Results

The CNV prediction models that combined genotype with phenotype with or without age and smoking revealed superior performance (C-statistic = 0.96) compared with the phenotype model based on the simplified severity scale and the presence of CNV in the nonstudy eye (C-statistic = 0.89; P<0.01). For GA, the model that combined genotype with phenotype demonstrated the highest performance (AUC = 0.94). Smoking status and ARMS2 genotype had less of an impact on the prediction of GA compared with CNV.

Conclusions

Inclusion of genotype assessment improves CNV prediction beyond that achievable with phenotype alone and may improve patient management. Separate assessments should be used to predict progression to CNV and GA because genetic markers and smoking status do not equally predict both end points.

Financial disclosure(s)

Proprietary or commercial disclosure may be found after the references.

SUBMITTER: Perlee LT

PROVIDER: S-EPMC3695024 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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Publications

Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy.

Perlee Lorah T LT Bansal Aruna T AT Gehrs Karen K Heier Jeffrey S JS Csaky Karl K Allikmets Rando R Oeth Paul P Paladino Toni T Farkas Daniel H DH Rawlings P Lyle PL Hageman Gregory S GS

Ophthalmology 20130321 9

<h4>Purpose</h4>The accuracy of predicting conversion from early-stage age-related macular degeneration (AMD) to the advanced stages of choroidal neovascularization (CNV) or geographic atrophy (GA) was evaluated to determine whether inclusion of clinically relevant genetic markers improved accuracy beyond prediction using phenotypic risk factors alone.<h4>Design</h4>Cohort study.<h4>Participants</h4>White, non-Hispanic subjects participating in the Age-Related Eye Disease Study (AREDS) sponsored ...[more]

PMID: 23523162

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Project description:PURPOSE:To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA). DESIGN:A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios. PARTICIPANTS:A total of 59?812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol. METHODS:A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD. A second model was trained to predict CGA presence from the same population. A third model was trained to predict CGA presence from the subset of eyes with GA. For training and testing, 5-fold cross-validation was used. For comparison with human clinician performance, model performance was compared with that of 88 retinal specialists. MAIN OUTCOME MEASURES:Area under the curve (AUC), accuracy, sensitivity, specificity, and precision. RESULTS:The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUCs of 0.933-0.976, 0.939-0.976, and 0.827-0.888, respectively. The GA detection model had accuracy, sensitivity, specificity, and precision of 0.965 (95% confidence interval [CI], 0.959-0.971), 0.692 (0.560-0.825), 0.978 (0.970-0.985), and 0.584 (0.491-0.676), respectively, compared with 0.975 (0.971-0.980), 0.588 (0.468-0.707), 0.982 (0.978-0.985), and 0.368 (0.230-0.505) for the retinal specialists. The CGA detection model had values of 0.966 (0.957-0.975), 0.763 (0.641-0.885), 0.971 (0.960-0.982), and 0.394 (0.341-0.448). The centrality detection model had values of 0.762 (0.725-0.799), 0.782 (0.618-0.945), 0.729 (0.543-0.916), and 0.799 (0.710-0.888). CONCLUSIONS:A deep learning model demonstrated high accuracy for the automated detection of GA. The AUC was noninferior to that of human retinal specialists. Deep learning approaches may also be applied to the identification of CGA. The code and pretrained models are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.

Project description:PurposeWe sought to determine whether genotype is associated with rate of growth of geographic atrophy (GA) in eyes with age-related macular degeneration (AMD).DesignProspective analysis of participants in a randomized controlled clinical trial.ParticipantsWe included 114 eyes of 114 participants in the Age-Related Eye Disease Study (AREDS).MethodsFundus photographs from AREDS participants with GA from whom a DNA specimen had been obtained and serial photographs had been taken over a minimum of 2 years were evaluated for progression as determined by change in cumulative area of GA. All fundus photographs were scanned, digitized, and centrally graded longitudinally for area of GA. The relationship of GA progression with previously identified genetic variants associated with AMD was assessed.Main outcome measuresGenotype frequencies and change in cumulative area of GA.ResultsThe mean growth rate of GA for the 114 eyes was 1.79 mm(2)/year (range, 0.17-4.76). No association between growth rate and genotype was present for variants in the CFH, C2, C3, APOE, and TLR3 genes. For the single nucleotide polymorphism rs10490924 in LOC387715/ARMS2, there was a significant association of GA growth rate, both adjusted and unadjusted for initial lesion size, with the homozygous risk genotype as compared with the homozygous nonrisk genotype (unadjusted P = 0.002; Bonferroni-corrected P = 0.014) and for allelic association (Bonferroni-corrected P value = 0.011). Analyses of other measures of GA progression (progression to central GA from extrafoveal GA and development of bilateral GA in those initially with unilateral GA) showed no statistically significant association between progression and the LOC387715/ARMS2/HTRA1 genotype.ConclusionsGrowth rates of GA calculated from digitized serial fundus photographs showed no association with variants in the CFH, C2, C3, APOE, or TLR3 genes. There was a nominally significant association with the LOC387715/ARMS2/HTRA1 genotype, although this finding was not supported by analyses of secondary measures of GA progression. Replication in other populations is needed to establish the existence of an association.

Dataset Information

Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy.

Purpose

Design

Participants

Methods

Main outcome measures

Results

Conclusions

Financial disclosure(s)

Publications

Inclusion of genotype with fundus phenotype improves accuracy of predicting choroidal neovascularization and geographic atrophy.

Similar Datasets

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