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Clinical Sciences |

Risk Assessment Model for Development of Advanced Age-Related Macular Degeneration FREE

Michael L. Klein, MD; Peter J. Francis, MD, PhD; Frederick L. Ferris, MD; Sara C. Hamon, PhD; Traci E. Clemons, PhD
[+] Author Affiliations

Author Affiliations: Macular Degeneration Center, Casey Eye Institute, Oregon Health & Science University and Devers Eye Institute, Legacy Good Samaritan Hospital and Medical Center, Portland (Drs Klein and Francis); National Eye Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda (Dr Ferris), and The EMMES Corp, Rockville (Dr Clemons), Maryland; and Laboratory of Statistical Genetics, Rockefeller University, New York, New York (Dr Hamon).


Arch Ophthalmol. 2011;129(12):1543-1550. doi:10.1001/archophthalmol.2011.216.
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Objective To design a risk assessment model for development of advanced age-related macular degeneration (AMD) incorporating phenotypic, demographic, environmental, and genetic risk factors.

Methods We evaluated longitudinal data from 2846 participants in the Age-Related Eye Disease Study. At baseline, these individuals had all levels of AMD, ranging from none to unilateral advanced AMD (neovascular or geographic atrophy). Follow-up averaged 9.3 years. We performed a Cox proportional hazards analysis with demographic, environmental, phenotypic, and genetic covariates and constructed a risk assessment model for development of advanced AMD. Performance of the model was evaluated using the C statistic and the Brier score and externally validated in participants in the Complications of Age-Related Macular Degeneration Prevention Trial.

Results The final model included the following independent variables: age, smoking history, family history of AMD (first-degree member), phenotype based on a modified Age-Related Eye Disease Study simple scale score, and genetic variants CFH Y402H and ARMS2 A69S. The model did well on performance measures, with very good discrimination (C statistic = 0.872) and excellent calibration and overall performance (Brier score at 5 years = 0.08). Successful external validation was performed, and a risk assessment tool was designed for use with or without the genetic component.

Conclusions We constructed a risk assessment model for development of advanced AMD. The model performed well on measures of discrimination, calibration, and overall performance and was successfully externally validated. This risk assessment tool is available for online use.

Figures in this Article

Although increasingly effective treatments are becoming available for age-related macular degeneration (AMD), it remains a leading cause of blindness in the United States and the Western world. As progress in designing better preventive measures and earlier treatment options accelerates and new gene associations are identified that add to currently known risk factors, the desirability of having a reliable risk assessment model has become of considerable interest and potential value.117

Desirable features of an AMD risk assessment model would include the identification of those individuals with early AMD who are at greatest risk to progress to advanced, vision-threatening AMD (geographic atrophy [GA] or neovascular AMD [NV]) and the capability to predict when progression to advanced AMD might occur. The optimal design might include known demographic and environmental risk factors,1826 phenotypic risk factors derived from large population-based and interventional studies,2731 and established genetic risk variants.115 The purpose of this article is to present a validated predictive model for AMD that incorporates these factors and can be used by the practicing physician.

SUBJECTS

The model was developed from longitudinal data derived from the Age-Related Eye Disease Study (AREDS) population. The AREDS was a long-term longitudinal natural history study of AMD and cataract that included a randomized clinical trial to assess the effect of supplements containing zinc and antioxidant vitamins C, E, and beta carotene on the risk of developing cataract or advanced AMD (defined as central GA or choroidal neovascularization). Study design and procedures were reported in detail previously.32

Samples of DNA from 2962 AREDS participants (2846 white participants) were obtained from the AREDS Genetic Repository. These samples represented individuals from all AREDS categories ranging from no AMD to advanced AMD in 1 eye, and these individuals compose the population for this study. Because of known variation in allele frequencies of AMD susceptibility genes, only white participants were included in this analysis. The project was approved by appropriate institutional review boards, and all individuals described in this article signed informed consent to participate in the AREDS genetic study.

DEMOGRAPHIC AND ENVIRONMENTAL FACTORS

Comprehensive ocular and medical histories and examinations were performed at entrance into the study. Recorded information included age, sex, race, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), education level, cigarette smoking, diet, sunlight exposure, history of skin cancer, arthritis, systemic hypertension, other cardiovascular diseases, diabetes, and history of current and past medications and dietary supplements.

PHENOTYPIC CLASSIFICATION

For this study, the AREDS simplified severity scale was used as a basis to classify participants by their retina phenotype.27 This scale was designed to define risk categories for development of advanced AMD that could be readily determined by either clinical examination or fundus photography. The system uses 2 retinal abnormalities at baseline to determine a risk score: (1) 1 or more large drusen (≥125 μm in the smallest diameter, approximately equivalent to the diameter of a major retinal vein crossing the optic disc margin), or (2) any definite pigment abnormality (hyperpigmentation or hypopigmentation). A sum of these risk factors for both eyes results in a 5-step severity scale ranging from a grade of 0 (no risk factor in either eye) to 4 (both risk factors in each eye). For individuals with no large drusen, the presence of intermediate drusen (63-125 μm) in both eyes counts as 1 risk factor. If advanced AMD is present at baseline in 1 eye, that eye is considered to have 2 risk factors. This established severity scale was augmented with 2 additional significant independent variables: presence of very large drusen (≥250 μm in the smallest diameter, approximately equivalent to twice the diameter of a major retinal vein crossing the optic disc margin) in 1 or both eyes, and the specific inclusion of advanced AMD in 1 eye at baseline as a risk factor in the model. Although for simplicity this risk factor is incorporated into the simple scale by assigning 2 simple scale risk factors to this factor, this method underestimates the importance of this factor and therefore this risk factor adds independently to the overall model.

GENETICS

Genotyping of the AREDS and Complications of Age-Related Macular Degeneration Prevention Trial (CAPT) participants (validation sample, see later) was performed at either Prevention Genetics, Marshfield, Wisconsin, or deCODE Genetics, Reykjavik, Iceland, using the Taqman (Applied Biosystems, Inc, Foster City, California) genotyping platform. The following single-nucleotide polymorphisms were evaluated in genes previously reported to be associated with AMD: rs1061170 in complement factor H (CFH), rs10490924 in LOC387715/ARMS2, rs9332739 in complement component 2 (C2), rs2230199 in complement component 3 (C3), rs7412 and rs429358 in apolipoprotein E (APOE), and rs13117504, rs10033900, and rs2285714 in complement factor I (CFI). Because of evidence for high linkage disequilibrium, we considered that the LOC387715 single-nucleotide polymorphism (rs10490924) served as a surrogate for the rs11200638 single-nucleotide polymorphism in HTRA13335 and that rs9332739 in C2 served as a surrogate for rs415667 in complement factor B (CFB).8

END POINTS

The end points of this study occurred when participants with no advanced AMD in either eye at baseline progressed to advanced AMD in either eye and when those with advanced AMD in 1 eye at baseline developed advanced AMD in the fellow eye. Two forms of advanced AMD were recognized: (1) NV and (2) GA, defined as an area of well-demarcated depigmentation of the pigment epithelium, typically round or oval, and within which choroidal vessels are usually visible.

STATISTICAL ANALYSIS

We performed Cox regression analysis using the survival package in R version 2.9.0 statistical software (R Foundation for Statistical Computing, Vienna, Austria) to determine which baseline variables were associated with progression to advanced AMD at yearly points throughout the duration of the study. Covariates assessed in this analysis were those factors that had reached significance levels of P ≤ .05 in the univariate analysis (Table 1 and Table 2). We determined the cumulative incidence of GA and NV at each point to enable the model to produce an estimated incidence of each of these advanced AMD subtypes. This was determined for each of 3 baseline groups: (1) no advanced AMD in either eye; (2) GA in 1 eye, no advanced AMD in the fellow eye; and (3) NV in 1 eye, no advanced AMD in the fellow eye.

Table Graphic Jump LocationTable 1. Univariate Association of Baseline Demographic, Environmental, Phenotypic, and Genetic Variables and Progression to Advanced Age-Related Macular Degeneration in 2846 Participants
Table Graphic Jump LocationTable 2. Multivariate Association of Baseline Independent Variables Included in Final Model With Hazard Ratios and 95% Confidence Intervals for Progression to Advanced Age-Related Macular Degeneration at 2, 5, and 10 Years in 2602 Participants

We assessed the performance of the risk prediction models and the contribution of their individual components by using measures of discrimination and calibration. To test for discrimination, we calculated the area under the receiver operating characteristic curve, or C statistic.36 A C statistic of 0.5 indicates no discriminative ability, and a C statistic of 1.0 indicates perfect discrimination. To assess overall performance including calibration, we calculated estimated prediction error curves by determining the weighted average of the squared distances between the models' predicted and observed outcomes using the prediction error curve package in R version 2.9.0 statistical software.37 A score of 1 is a total mismatch of the 2 outcomes, and a score of 0 is a perfect match of outcomes. The resultant Brier scores were calculated at yearly intervals.

Both performance evaluation methods were used to compare the influence of individual model components on predictive ability. For this purpose, the following models were assessed: (A) phenotypic, demographic/environmental, and genetic components; (B) phenotypic and demographic/environmental components; and (C) genetic and demographic/environmental components.

External validation of the model was carried out in an independent population of 297 participants derived from the CAPT.38 These patients represent a subset of all CAPT participants (N = 1052) who contributed DNA samples during the course of the study (n = 324) and had genotype results for both CFH Y402H and ARMS2 A69S. Entry criteria for the CAPT included the presence of 10 or more large drusen (≥125 μm in diameter) in both eyes. One eye of each individual had been randomly assigned to receive mild macular laser grid therapy in an attempt to reduce the number progressing to advanced AMD. During the 5-year follow-up period, 1 or both eyes progressed to advanced AMD in 116 individuals, with no difference between treated and untreated eyes. Using this data set, we assessed model calibration using the Hosmer-Lemeshow calibration statistic comparing observed and predicted risk based on categories defined by deciles of the predicted risk.39 A significant P value for this statistic indicates significant deviation between predicted and observed outcomes.

UNIVARIATE AND MULTIVARIATE COX PROPORTIONAL HAZARDS MODELS

Table 1 illustrates the univariate association of baseline factors with progression to advanced AMD (P ≤ .05). These included age, cigarette smoking, family history, BMI, education, simple scale score, very large drusen (≥250 μm), unilateral AMD, and variants in the genes CFH, ARMS2, C3, and CFI. The C2/CFB variant was associated with a decreased risk of progression, as previously reported.14 Each of these variables was then further analyzed as a candidate variable for the final multivariate model (Table 2). Variables not found to be associated with progression and therefore not considered in the multivariate analysis included sex and variants in the APOE gene.

Table 2 shows results of the multivariate proportional hazards analysis for the final risk assessment model. We had performed backward regression and eliminated those variables with an associated P >> .05. This resulted in removal from the model of BMI, education, and the C2, C3, and CFI genotypes. A total of 2602 individuals were measured for the significant baseline variables in the final model, which included simple scale score, genotypes for CFH and ARMS2, very large drusen (≥250 μm), smoking, family history, unilateral advanced AMD, and age. For each of these remaining variables, the proportion progressing to advanced AMD at 2, 5, and 10 years, nonstandardized β coefficient, hazard ratio with associated confidence interval, z value, and P value are shown. The primary phenotypic variable, which was based on simple scale score, had the largest hazard ratios, ranging from 6.38 to 50.65. These were much greater than those for all other variables in the model, which had hazard ratios ranging from 1.03 to 2.00.

Treatment assignment was not considered in this analysis. The observed rates are averages of the rates for those assigned to the 4 different treatment groups at baseline and the larger number of participants receiving the supplements after the results of the clinical trial were published.40 In general, one could expect approximately a 10% to 15% reduction or increase in the calculated risk, depending on whether the individual is receiving treatment with the AREDS formulation of antioxidants and zinc.

PERFORMANCE MEASURES

Performance assessment of the final model and illustration of the relative contribution of its major component variables are illustrated in Table 3. Results are shown for 3 variations of the prediction model: (A) the complete model with all variables (demographic/environmental, phenotypic, and genetic); (B) demographic/environmental and phenotypic variables only; and (C) demographic/environmental and genotypic (CFH, ARMS2, C2, C3, CFI, APOE) variables only. The complete model showed excellent performance results in discrimination (C statistic = 0.872) and overall performance (prediction error curve: Brier score = 0.08 at the 5-year follow-up). Similar performance was achieved by model B, which excluded only the genetic component. However, when the phenotypic component alone was excluded (model C), there was a decline in both performance measures. Figure 1 illustrates the prediction error curves for the 3 model variations during the 10-year period of the study. Also illustrated is the error curve for a model excluding only the demographic/environmental variables.

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Figure 1. Prediction error curves (Brier score) for prediction models of developing advanced age-related macular degeneration.

Table Graphic Jump LocationTable 3. The C Statistic and Brier Score for 3 Predictive Models for Development of Advanced Age-Related Macular Degeneration at 5 Yearsa
EXTERNAL VALIDATION

The computed Hosmer-Lemeshow statistic for the model using the CAPT data was not statistically significant, indicating good calibration (Hosmer-Lemeshow statistic = 15.00; P = .09).

DEVELOPMENT OF AMD SUBTYPES

Of those individuals in the final model with no advanced AMD at baseline (n = 2602), 635 (24%) developed advanced AMD during the follow-up period. In these individuals, the initial occurrence of advanced AMD was NV in 340 cases (54%) and GA in 295 cases (46%). Of those individuals with GA in 1 eye at baseline (n = 56), 46 (82%) developed advanced AMD in the fellow eye. Of these individuals, the initial occurrence of advanced AMD was GA in 36 cases (78%) and NV in 10 cases (22%). Of those with NV in 1 eye at baseline (n = 315), 176 (56%) developed advanced AMD in the fellow eye. Of these individuals, the initial occurrence of advanced AMD was NV in 136 cases (77%) and GA in 40 cases (23%). There was no predictive value for either genotype (CFH or ARMS2) in differentiating progression to GA or to NV after correction for multiple testing.

We constructed a predictive model for development of advanced AMD comprising demographic and environmental, phenotypic, and genetic risk factors. Risk models have been developed for several multifactorial diseases, including cardiovascular disease,4144 diabetes,4549 and cancer.5053 Risk models for ophthalmic diseases have been reported for primary open-angle glaucoma5456 and proliferative vitreoretinopathy.57 With regard to AMD, several articles have discussed the potential value of genetic testing alone or in combination with other factors for predicting development of advanced AMD,9,5862 and predictive models have recently been presented.16,17,63

The prediction model for advanced AMD described by Seddon et al16 was derived from the AREDS population and included demographic, environmental, and genetic risk factors along with baseline ocular phenotypic features using the AREDS categorical scale (categories 2-4). A second model described by Zanke et al17 included age, cigarette smoking, and a panel of genetic risk variants that provided a lifetime risk estimate for developing advanced AMD. A risk score for development of GA was recently presented, comprising age, ocular phenotype, smoking status, hypertension, and night vision score in CAPT participants.63

Our model extends the utility of previous models in estimating risk of developing advanced AMD. We used an expanded baseline ocular phenotype classification system easily usable in clinical practice (see Methods). This resulted in strengthening of baseline phenotype stratification and potentially greater accuracy in predicting progression to advanced AMD. In addition, we used a multivariate Cox proportional hazards approach based on longitudinal data derived from participants in the AREDS, providing risk estimates for the development of advanced AMD at variable intervals during the follow-up period from years 1 through 10. This information can be of potential value in clinical practice by helping determine the frequency of follow-up examinations, the use of home monitoring of central vision, and the advisability of initiating preventive measures including beneficial lifestyle changes such as dietary alterations and nutritional supplement use. The short-term end points (eg, 2 years) may be helpful in planning clinical trials. The model also includes an estimate of progression to either of the 2 advanced forms of AMD, GA and NV. This feature might be of some current value in clinical management and design of clinical trials, and of potential future value should interventions more applicable to 1 of the 2 forms of AMD become available.

Prior to the application of a predictive risk assessment model in clinical practice, it has been recommended that the model meet acceptable performance standards using measures of discrimination (separation of those who do and do not have or develop a disease or event) and calibration (the degree to which the predicted probability of events agrees with the actual occurrences).64,65 Our complete model, comprising 3 risk factor components—demographic/environmental, phenotypic, and genetic—performed well with regard to both discrimination (C statistic = 0.872) and calibration (Brier scores at 2, 5, and 10 years of 0.05, 0.08, and 0.095, respectively). Using these same methods to assess performance of various combinations of the model's 3 components, we found similar performance results for the complete model and a model including only phenotypic and demographic/environmental factors (excluding genotype). A model comprising only genetic and demographic/environmental factors (excluding phenotype) did not perform as well. An alternative method incorporating the genetic component using a weighted score for all variants did not improve the genetic contribution to the model (data not shown). These results indicate that phenotypic variables in our model are of greatest predictive value and, when combined with demographic and environmental factors, will provide a reasonably adequate risk assessment with or without inclusion of specific genetic factors beyond first-degree family history.

We also performed external validation of our complete model in an independent population drawn from the CAPT.38 This cohort differed from our study population in some respects, including absence of family history data, presence of several large drusen in both eyes, and performance of laser grid treatment in 1 eye of all participants. Nevertheless, using the Hosmer-Lemeshow calibration statistic, the model performed satisfactorily, adding further to its utility as a prediction tool.

Our findings support the view that genetic testing alone or in combination with demographic/environmental factors is currently of limited value as a screening tool for AMD. We believe that the first priority for individuals at potentially increased risk for developing AMD based on age, family history, and other factors should be to obtain an eye examination, including an assessment of the macula for manifestations of AMD. Although commercial genetic testing for AMD is becoming available, we feel that genetic analysis prior to having an eye examination would not be the most practical approach since nearly 80% of individuals aged 55 years and older do not have large drusen in the macula and would thus be at minimal risk for developing advanced AMD in the next 5 to 10 years regardless of genetic testing results.27,28,30 Furthermore, a routine eye examination can assess a patient's risk for all of the leading causes of blindness, especially in older individuals, and is consistent with recommendations by the American Academy of Ophthalmology.66 As part of this examination, the demographic, environmental, and phenotypic risk factor information can be readily obtained, providing most of the predictive value, which can be immediately discussed with the patient. In certain circumstances, use of a risk assessment tool to calculate risk of advanced AMD might be of value. For this purpose, a risk calculator based on our prognostic model has been constructed and is available online at http://www.ohsucasey.com/amdcalculator (Figure 2).

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Figure 2. Representation of risk assessment tool for development of advanced age-related macular degeneration (AMD). NV indicates neovascular AMD; GA, geographic atrophy.

The risk calculator based on our prognostic model is designed to be used with and without a genetic component. By indicating in the model that genetic information is not being entered for a given individual, the model will assume that the individual is heterozygous for both genes (CFH CT and ARMS2 TG), and the resulting risk for advanced AMD will generally range within 0% to 6% of the risk obtained from our prediction model without a genetic component.

While use of our prediction model without entering genetic information seems adequate for risk prediction in most current clinical settings, the inclusion of genetic risk factors can somewhat further refine the risk estimate for advanced AMD in individual cases. For example, without genetic data, a 75-year-old man who smokes cigarettes, has advanced AMD in 1 eye, and has no large drusen or pigment changes in the fellow eye (simple scale score = 2) will have a 22% chance of developing advanced AMD in the fellow eye in 5 years. If genetic information were available, his 5-year risk for advanced AMD would be 13%, 25%, or 34% depending on his CFH and ARMS2 genotypes. This additional information might be of more benefit in the future as new and more effective preventive measures and treatments at earlier stages of AMD become available. The degree to which genetic information might refine risk assessment in our model is related in part to an individual's genotype and the extent of retinal changes. In general, eyes with more advanced phenotypic changes (eg, simple scale scores of 2, 3, and 4) will have greater variation in risk depending on their genotype as compared with individuals who have less advanced phenotypic changes (eg, simple scale scores of 0 and 1). Further examples are given in the eTable.

There are limitations of this risk assessment model. The decision to limit participants to white individuals was motivated by known ethnic variations in AMD-associated gene variants and phenotype manifestations. For instance, the CFH risk allele frequency is very strong in white individuals but not in certain Asian populations.67,68 The model, however, could be readily adapted to accommodate different hazard ratios in other populations. Another limitation is that patients used in constructing our model were limited to those aged between 55 and 80 years. However, this includes the majority of individuals at risk for advanced AMD in whom prognostic testing would be most appropriate. Finally, our patient population was not derived from a population-based sample. However, this well-characterized and carefully documented AREDS population is similar to the general population in that the full range of AMD, from no disease through intermediate stages to advanced AMD, is well represented.

We believe our current model is of substantial value in assessing AMD risk, and we expect that future advances will further improve its accuracy. Unexplained heritability of AMD will be uncovered,69 studies on diet, other environmental factors, and serum biomarkers may identify new predictive factors,70,71 and better phenotyping methods are under development.30,7276 As these new findings become available, we plan to update the model and maintain a current version for online use.

Correspondence: Michael L. Klein, MD, Casey Eye Institute, Oregon Health & Science University, 3375 SW Terwilliger Blvd, Portland, OR 97239 (kleinm@ohsu.edu).

Submitted for Publication: January 14, 2011; final revision received May 20, 2011; accepted May 27, 2011.

Published Online: August 8, 2011. doi:10.1001/archophthalmol.2011.216. This article was corrected for errors on September 20, 2011.

Author Contributions: Dr Klein had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Financial Disclosure: A US patent entitled “Nutritional Supplement to Treat Macular Degeneration (patent No. 6,660,297) was issued on December 9, 2003; Dr Ferris is one of the inventors. The patent is owned by Bausch and Lomb. Dr Ferris has assigned his interest in the patent to the US government and receives government compensation.

Funding/Support: This work was supported by the Casey Eye Institute Macular Degeneration Fund (Drs Klein and Francis), Research to Prevent Blindness, New York, New York (Drs Klein and Francis), the Bea Arveson Macular Degeneration Fund (Dr Klein), and the Foundation Fighting Blindness, Owings Mills, Maryland (Dr Francis).

Additional Contributions: We are grateful to all of the participants and investigators of the AREDS and to the AREDS Genetic Repository. We thank the investigators and participants of the CAPT. Maureen Maguire, PhD, provided and facilitated transfer of data for the CAPT validation. Jennifer Maykoski, BS, helped in compiling the data and Genevieve Long, PhD, assisted in preparing and editing the manuscript.

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Davis MD, Gangnon RE, Lee LY,  et al; Age-Related Eye Disease Study Group.  The Age-Related Eye Disease Study severity scale for age-related macular degeneration: AREDS report No. 17.  Arch Ophthalmol. 2005;123(11):1484-1498
PubMed   |  Link to Article
Bird AC, Bressler NM, Bressler SB,  et al; International ARM Epidemiological Study Group.  An international classification and grading system for age-related maculopathy and age-related macular degeneration.  Surv Ophthalmol. 1995;39(5):367-374
PubMed   |  Link to Article
Age-Related Eye Disease Study Research Group.  The Age-Related Eye Disease Study (AREDS): design implications: AREDS report No. 1.  Control Clin Trials. 1999;20(6):573-600
PubMed   |  Link to Article
Dewan A, Liu M, Hartman S,  et al.  HTRA1 promoter polymorphism in wet age-related macular degeneration.  Science. 2006;314(5801):989-992
PubMed   |  Link to Article
Fritsche LG, Loenhardt T, Janssen A,  et al.  Age-related macular degeneration is associated with an unstable ARMS2 (LOC387715) mRNA.  Nat Genet. 2008;40(7):892-896
PubMed   |  Link to Article
Francis PJ, Zhang H, Dewan A, Hoh J, Klein ML. Joint effects of polymorphisms in the HTRA1, LOC387715/ARMS2, and CFH genes on AMD in a Caucasian population.  Mol Vis. 2008;14:1395-1400
PubMed
Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests.  JAMA. 1982;247(18):2543-2546
PubMed   |  Link to Article
Gerds TA, Schumacher M. Efron-type measures of prediction error for survival analysis.  Biometrics. 2007;63(4):1283-1287
PubMed   |  Link to Article
Complications of Age-Related Macular Degeneration Prevention Trial Research Group.  Laser treatment in patients with bilateral large drusen: the Complications of Age-Related Macular Degeneration Prevention Trial.  Ophthalmology. 2006;113(11):1974-1986
PubMed   |  Link to Article
Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models.  Am J Epidemiol. 1982;115(1):92-106
PubMed
Age-Related Eye Disease Study Research Group.  A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C and E, beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report No. 8.  Arch Ophthalmol. 2001;119(10):1417-1436
PubMed
Kannel WB. Role of blood pressure in cardiovascular disease: the Framingham Study.  Angiology. 1975;26(1, pt 1):1-14
PubMed   |  Link to Article
Gordon T, Kannel WB. Multiple risk functions for predicting coronary heart disease: the concept, accuracy, and application.  Am Heart J. 1982;103(6):1031-1039
PubMed   |  Link to Article
Hippisley-Cox J, Coupland C, Vinogradova Y,  et al.  Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.  BMJ. 2008;336(7659):1475-1482
PubMed   |  Link to Article
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories.  Circulation. 1998;97(18):1837-1847
PubMed
Lin X, Song K, Lim N,  et al.  Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score: the CoLaus Study.  Diabetologia. 2009;52(4):600-608
PubMed   |  Link to Article
Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.  Arch Intern Med. 2007;167(10):1068-1074
PubMed   |  Link to Article
Meigs JB, Shrader P, Sullivan LM,  et al.  Genotype score in addition to common risk factors for prediction of type 2 diabetes.  N Engl J Med. 2008;359(21):2208-2219
PubMed   |  Link to Article
Lyssenko V, Jonsson A, Almgren P,  et al.  Clinical risk factors, DNA variants, and the development of type 2 diabetes.  N Engl J Med. 2008;359(21):2220-2232
PubMed   |  Link to Article
Lango H, Palmer CN, Morris AD,  et al; UK Type 2 Diabetes Genetics Consortium.  Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk.  Diabetes. 2008;57(11):3129-3135
PubMed   |  Link to Article
Antoniou AC, Easton DF. Risk prediction models for familial breast cancer.  Future Oncol. 2006;2(2):257-274
PubMed   |  Link to Article
Dong LM, Potter JD, White E, Ulrich CM, Cardon LR, Peters U. Genetic susceptibility to cancer: the role of polymorphisms in candidate genes.  JAMA. 2008;299(20):2423-2436
PubMed   |  Link to Article
Graf W, Bergström R, Påhlman L, Glimelius B. Appraisal of a model for prediction of prognosis in advanced colorectal cancer.  Eur J Cancer. 1994;30A(4):453-457
PubMed   |  Link to Article
Freedman AN, Slattery ML, Ballard-Barbash R,  et al.  Colorectal cancer risk prediction tool for white men and women without known susceptibility.  J Clin Oncol. 2009;27(5):686-693
PubMed   |  Link to Article
Gordon MO, Beiser JA, Brandt JD,  et al.  The Ocular Hypertension Treatment Study: baseline factors that predict the onset of primary open-angle glaucoma.  Arch Ophthalmol. 2002;120(6):714-720
PubMed
Coleman AL, Miglior S. Risk factors for glaucoma onset and progression.  Surv Ophthalmol. 2008;53:(suppl 1)  S3-S10
PubMed   |  Link to Article
Mansberger SL, Medeiros FA, Gordon M. Diagnostic tools for calculation of glaucoma risk.  Surv Ophthalmol. 2008;53:(suppl 1)  S11-S16
PubMed   |  Link to Article
Rojas J, Fernandez I, Pastor JC,  et al.  Development of predictive models of proliferative vitreoretinopathy based on genetic variables: the Retina 4 project.  Invest Ophthalmol Vis Sci. 2009;50(5):2384-2390
PubMed   |  Link to Article
Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE. Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers.  PLoS Genet. 2009;5(2):e1000337
PubMed   |  Link to Article
Edwards AO. Genetic testing for age-related macular degeneration.  Ophthalmology. 2006;113(4):509-510
PubMed   |  Link to Article
Despriet DD, Klaver CC, van Duijn CC, Janssens AC. Predictive value of multiple genetic testing for age-related macular degeneration.  Arch Ophthalmol. 2007;125(9):1270-1271
PubMed   |  Link to Article
Baird PN, Hageman GS, Guymer RH. New era for personalized medicine: the diagnosis and management of age-related macular degeneration.  Clin Experiment Ophthalmol. 2009;37(8):814-821
PubMed   |  Link to Article
Gorin MB. A clinician's view of the molecular genetics of age-related maculopathy.  Arch Ophthalmol. 2007;125(1):21-29
PubMed   |  Link to Article
Ying GS, Maguire MG.Complications of Age-Related Macular Degeneration Prevention Trial Research Group.  Development of a risk score for geographic atrophy in Complications of the Age-Related Macular Degeneration Prevention Trial.  Ophthalmology. 2011;118(2):332-338
PubMed   |  Link to Article
Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction.  Circulation. 2007;115(7):928-935
PubMed   |  Link to Article
Steyerberg EW, Vickers AJ, Cook NR,  et al.  Assessing the performance of prediction models: a framework for traditional and novel measures.  Epidemiology. 2010;21(1):128-138
PubMed   |  Link to Article
American Academy of Ophthalmology.  Frequency of ocular examinations. http://one.aao.org/CE/PracticeGuidelines/ClinicalStatements.aspx. Updated November 2009. Accessed January 4, 2011
Kim NR, Kang JH, Kwon OW, Lee SJ, Oh JH, Chin HS. Association between complement factor H gene polymorphisms and neovascular age-related macular degeneration in Koreans.  Invest Ophthalmol Vis Sci. 2008;49(5):2071-2076
PubMed   |  Link to Article
Gotoh N, Yamada R, Nakanishi H,  et al.  Correlation between CFH Y402H and HTRA1 rs11200638 genotype to typical exudative age-related macular degeneration and polypoidal choroidal vasculopathy phenotype in the Japanese population.  Clin Experiment Ophthalmol. 2008;36(5):437-442
PubMed
Manolio TA, Collins FS, Cox NJ,  et al.  Finding the missing heritability of complex diseases.  Nature. 2009;461(7265):747-753
PubMed   |  Link to Article
Coleman H, Chew E. Nutritional supplementation in age-related macular degeneration.  Curr Opin Ophthalmol. 2007;18(3):220-223
PubMed   |  Link to Article
Moeller SM, Voland R, Sarto GE, Gobel VL, Streicher SL, Mares JA. Women's Health Initiative diet intervention did not increase macular pigment optical density in an ancillary study of a subsample of the Women's Health Initiative.  J Nutr. 2009;139(9):1692-1699
PubMed   |  Link to Article
Friberg TR, Huang L, Palaiou M, Bremer R. Computerized detection and measurement of drusen in age-related macular degeneration.  Ophthalmic Surg Lasers Imaging. 2007;38(2):126-134
PubMed
Leng T, Rosenfeld PJ, Gregori G, Puliafito CA, Punjabi OS. Spectral domain optical coherence tomography characteristics of cuticular drusen.  Retina. 2009;29(7):988-993
PubMed   |  Link to Article
Witkin AJ, Ko TH, Fujimoto JG,  et al.  Ultra-high resolution optical coherence tomography assessment of photoreceptors in retinitis pigmentosa and related diseases.  Am J Ophthalmol. 2006;142(6):945-952
PubMed   |  Link to Article
Holz FG, Bellman C, Staudt S, Schütt F, Völcker HE. Fundus autofluorescence and development of geographic atrophy in age-related macular degeneration.  Invest Ophthalmol Vis Sci. 2001;42(5):1051-1056
PubMed
Smith RT, Chan JK, Busuoic M, Sivagnanavel V, Bird AC, Chong NV. Autofluorescence characteristics of early, atrophic, and high-risk fellow eyes in age-related macular degeneration.  Invest Ophthalmol Vis Sci. 2006;47(12):5495-5504
PubMed   |  Link to Article

Figures

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Figure 1. Prediction error curves (Brier score) for prediction models of developing advanced age-related macular degeneration.

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Figure 2. Representation of risk assessment tool for development of advanced age-related macular degeneration (AMD). NV indicates neovascular AMD; GA, geographic atrophy.

Tables

Table Graphic Jump LocationTable 1. Univariate Association of Baseline Demographic, Environmental, Phenotypic, and Genetic Variables and Progression to Advanced Age-Related Macular Degeneration in 2846 Participants
Table Graphic Jump LocationTable 2. Multivariate Association of Baseline Independent Variables Included in Final Model With Hazard Ratios and 95% Confidence Intervals for Progression to Advanced Age-Related Macular Degeneration at 2, 5, and 10 Years in 2602 Participants
Table Graphic Jump LocationTable 3. The C Statistic and Brier Score for 3 Predictive Models for Development of Advanced Age-Related Macular Degeneration at 5 Yearsa

References

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van Leeuwen R, Ikram MK, Vingerling JR, Witteman JC, Hofman A, de Jong PT. Blood pressure, atherosclerosis, and the incidence of age-related maculopathy: the Rotterdam Study.  Invest Ophthalmol Vis Sci. 2003;44(9):3771-3777
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Ferris FL, Davis MD, Clemons TE,  et al; Age-Related Eye Disease Study (AREDS) Research Group.  A simplified severity scale for age-related macular degeneration: AREDS report No. 18.  Arch Ophthalmol. 2005;123(11):1570-1574
PubMed   |  Link to Article
Klein R, Klein BE, Linton KL. Prevalence of age-related maculopathy: the Beaver Dam Eye Study.  Ophthalmology. 1992;99(6):933-943
PubMed
Klein R, Klein BE, Knudtson MD, Meuer SM, Swift M, Gangnon RE. Fifteen-year cumulative incidence of age-related macular degeneration: the Beaver Dam Eye Study.  Ophthalmology. 2007;114(2):253-262
PubMed   |  Link to Article
Davis MD, Gangnon RE, Lee LY,  et al; Age-Related Eye Disease Study Group.  The Age-Related Eye Disease Study severity scale for age-related macular degeneration: AREDS report No. 17.  Arch Ophthalmol. 2005;123(11):1484-1498
PubMed   |  Link to Article
Bird AC, Bressler NM, Bressler SB,  et al; International ARM Epidemiological Study Group.  An international classification and grading system for age-related maculopathy and age-related macular degeneration.  Surv Ophthalmol. 1995;39(5):367-374
PubMed   |  Link to Article
Age-Related Eye Disease Study Research Group.  The Age-Related Eye Disease Study (AREDS): design implications: AREDS report No. 1.  Control Clin Trials. 1999;20(6):573-600
PubMed   |  Link to Article
Dewan A, Liu M, Hartman S,  et al.  HTRA1 promoter polymorphism in wet age-related macular degeneration.  Science. 2006;314(5801):989-992
PubMed   |  Link to Article
Fritsche LG, Loenhardt T, Janssen A,  et al.  Age-related macular degeneration is associated with an unstable ARMS2 (LOC387715) mRNA.  Nat Genet. 2008;40(7):892-896
PubMed   |  Link to Article
Francis PJ, Zhang H, Dewan A, Hoh J, Klein ML. Joint effects of polymorphisms in the HTRA1, LOC387715/ARMS2, and CFH genes on AMD in a Caucasian population.  Mol Vis. 2008;14:1395-1400
PubMed
Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests.  JAMA. 1982;247(18):2543-2546
PubMed   |  Link to Article
Gerds TA, Schumacher M. Efron-type measures of prediction error for survival analysis.  Biometrics. 2007;63(4):1283-1287
PubMed   |  Link to Article
Complications of Age-Related Macular Degeneration Prevention Trial Research Group.  Laser treatment in patients with bilateral large drusen: the Complications of Age-Related Macular Degeneration Prevention Trial.  Ophthalmology. 2006;113(11):1974-1986
PubMed   |  Link to Article
Lemeshow S, Hosmer DW Jr. A review of goodness of fit statistics for use in the development of logistic regression models.  Am J Epidemiol. 1982;115(1):92-106
PubMed
Age-Related Eye Disease Study Research Group.  A randomized, placebo-controlled, clinical trial of high-dose supplementation with vitamins C and E, beta carotene, and zinc for age-related macular degeneration and vision loss: AREDS report No. 8.  Arch Ophthalmol. 2001;119(10):1417-1436
PubMed
Kannel WB. Role of blood pressure in cardiovascular disease: the Framingham Study.  Angiology. 1975;26(1, pt 1):1-14
PubMed   |  Link to Article
Gordon T, Kannel WB. Multiple risk functions for predicting coronary heart disease: the concept, accuracy, and application.  Am Heart J. 1982;103(6):1031-1039
PubMed   |  Link to Article
Hippisley-Cox J, Coupland C, Vinogradova Y,  et al.  Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.  BMJ. 2008;336(7659):1475-1482
PubMed   |  Link to Article
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories.  Circulation. 1998;97(18):1837-1847
PubMed
Lin X, Song K, Lim N,  et al.  Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score: the CoLaus Study.  Diabetologia. 2009;52(4):600-608
PubMed   |  Link to Article
Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB Sr. Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.  Arch Intern Med. 2007;167(10):1068-1074
PubMed   |  Link to Article
Meigs JB, Shrader P, Sullivan LM,  et al.  Genotype score in addition to common risk factors for prediction of type 2 diabetes.  N Engl J Med. 2008;359(21):2208-2219
PubMed   |  Link to Article
Lyssenko V, Jonsson A, Almgren P,  et al.  Clinical risk factors, DNA variants, and the development of type 2 diabetes.  N Engl J Med. 2008;359(21):2220-2232
PubMed   |  Link to Article
Lango H, Palmer CN, Morris AD,  et al; UK Type 2 Diabetes Genetics Consortium.  Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk.  Diabetes. 2008;57(11):3129-3135
PubMed   |  Link to Article
Antoniou AC, Easton DF. Risk prediction models for familial breast cancer.  Future Oncol. 2006;2(2):257-274
PubMed   |  Link to Article
Dong LM, Potter JD, White E, Ulrich CM, Cardon LR, Peters U. Genetic susceptibility to cancer: the role of polymorphisms in candidate genes.  JAMA. 2008;299(20):2423-2436
PubMed   |  Link to Article
Graf W, Bergström R, Påhlman L, Glimelius B. Appraisal of a model for prediction of prognosis in advanced colorectal cancer.  Eur J Cancer. 1994;30A(4):453-457
PubMed   |  Link to Article
Freedman AN, Slattery ML, Ballard-Barbash R,  et al.  Colorectal cancer risk prediction tool for white men and women without known susceptibility.  J Clin Oncol. 2009;27(5):686-693
PubMed   |  Link to Article
Gordon MO, Beiser JA, Brandt JD,  et al.  The Ocular Hypertension Treatment Study: baseline factors that predict the onset of primary open-angle glaucoma.  Arch Ophthalmol. 2002;120(6):714-720
PubMed
Coleman AL, Miglior S. Risk factors for glaucoma onset and progression.  Surv Ophthalmol. 2008;53:(suppl 1)  S3-S10
PubMed   |  Link to Article
Mansberger SL, Medeiros FA, Gordon M. Diagnostic tools for calculation of glaucoma risk.  Surv Ophthalmol. 2008;53:(suppl 1)  S11-S16
PubMed   |  Link to Article
Rojas J, Fernandez I, Pastor JC,  et al.  Development of predictive models of proliferative vitreoretinopathy based on genetic variables: the Retina 4 project.  Invest Ophthalmol Vis Sci. 2009;50(5):2384-2390
PubMed   |  Link to Article
Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE. Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers.  PLoS Genet. 2009;5(2):e1000337
PubMed   |  Link to Article
Edwards AO. Genetic testing for age-related macular degeneration.  Ophthalmology. 2006;113(4):509-510
PubMed   |  Link to Article
Despriet DD, Klaver CC, van Duijn CC, Janssens AC. Predictive value of multiple genetic testing for age-related macular degeneration.  Arch Ophthalmol. 2007;125(9):1270-1271
PubMed   |  Link to Article
Baird PN, Hageman GS, Guymer RH. New era for personalized medicine: the diagnosis and management of age-related macular degeneration.  Clin Experiment Ophthalmol. 2009;37(8):814-821
PubMed   |  Link to Article
Gorin MB. A clinician's view of the molecular genetics of age-related maculopathy.  Arch Ophthalmol. 2007;125(1):21-29
PubMed   |  Link to Article
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