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Online Risk-Assessment Model Predicts Advanced AMD

  • Authors: News Author: Ricki Lewis, PhD
    CME Author: Désirée Lie, MD, MSEd
  • CME Released: 12/28/2011
  • Valid for credit through: 12/28/2012
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Target Audience and Goal Statement

This article is intended for primary care clinicians, ophthalmologists, geriatricians, and other specialists who care for patients at risk for age-related macular degeneration.

The goal of this activity is to provide medical news to primary care clinicians and other healthcare professionals in order to enhance patient care.

Upon completion of this activity, participants will be able to:

  1. Describe the components of 3 risk-assessment models in the prediction of progression of age-related macular degeneration.
  2. Describe the validity and predictive value of the risk calculator for advanced age-related macular degeneration.


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  • Ricki Lewis, PhD

    Ricki Lewis, PhD, is a freelance writer for Medscape.


    Disclosure: Ricki Lewis, PhD, has disclosed no relevant financial relationships.


  • Brande Nicole Martin, MA

    CME Clinical Editor, Medscape, LLC


    Disclosure: Brande Nicole Martin, MA, has disclosed no relevant financial relationships.

CME Author(s)

  • Désirée Lie, MD, MSEd

    Clinical Professor; Director of Research and Faculty Development, Department of Family Medicine, University of California, Irvine at Orange


    Disclosure: Désirée Lie, MD, MSEd, has disclosed the following relevant financial relationship:
    Served as a nonproduct speaker for: "Topics in Health" for Merck Speaker Services

CME Reviewer(s)

  • Sarah Fleischman

    CME Program Manager, Medscape, LLC


    Disclosure: Sarah Fleischman has disclosed no relevant financial relationships.

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Online Risk-Assessment Model Predicts Advanced AMD

Authors: News Author: Ricki Lewis, PhD CME Author: Désirée Lie, MD, MSEdFaculty and Disclosures

CME Released: 12/28/2011

Valid for credit through: 12/28/2012


Clinical Context

According to the current study by Klein and colleagues, age-related macular degeneration (AMD) remains a leading cause of blindness despite advances in effective treatment, and identifying individuals at risk for progression to advanced AMD threatening vision would improve decision-making about therapy and preventive measures. Risk models have been developed for cardiovascular disease and diabetes and, in ophthalmology, have focused on open-angle glaucoma. There is potential value in use of demographic, environmental, and phenotypic factors to predict AMD progression.

This trial of the Age-Related Eye Disease Study (AREDS) cohort develops a validated risk-assessment model for practicing clinicians to predict AMD progression.

Study Synopsis and Perspective

A new risk-assessment model is available for online use to help practitioners identify patients with age-related macular degeneration (AMD; dry or wet) who are at highest risk for progressing to visual loss. This tool, described in an article published online August 8 and in the December print issue of the Archives of Ophthalmology, promises to be especially valuable in light of the several new treatments available for AMD.

Michael L. Klein, MD, from the Casey Eye Institute, Oregon Health & Science University, Portland, and colleagues evaluated longitudinal data from 2846 participants in AREDS and validated the findings using 297 participants in the Complications of Age-Related Macular Degeneration Prevention Trial (CAPT).

The study population presented with all levels of pathology at baseline, and follow-up averaged 9.3 years. Independent variables evaluated included age, smoking history, family history of AMD, phenotype (based on a modified AREDS simple scale score), and polymorphisms of both the CFH gene (Y402H) and the ARMS2 gene (A69S). Many past studies have identified risk factors such as severity of signs present (drusen size, abnormal pigment patterns, and area of involvement), age, sex, family history (first-degree relative), lifestyle factors, environmental exposures, and comorbidities.

The model performed well for discrimination (C statistic, 0.872) and calibration (Brier scores at 2, 5, and 10 years of 0.05, 0.08, and 0.095, respectively). The most important factors to emerge in evaluating risk for progression to visual loss from AMD were age, smoking history, severity of early AMD, and having either or both of the genetic variants, although genetics added only slightly to the overall risk, the researchers point out.

"Knowing the severity of the lesions of AMD that are already present coupled with knowledge of the important lifestyle factors (eg, smoking history) gives most of the important information about risk of progression," write Ronald Klein, MD, MPH, Barbara Klein, BD, MPH, and Chelsea Myers, MStat, from the Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, in an editorial.

A limitation of the model is that it was developed using a clinical population. Indeed, when the editorial writers applied the model to 1575 people aged 55 to 80 years at baseline in the population-based Beaver Dam Eye Study, they found that the model predicted a greater risk for progression than actually happened, possibly because the clinical population on which the model is based had more advanced disease. The editorial writers also suggest that the model should include nutritional supplement use.

The study was supported by the Casey Eye Institute Macular Degeneration Fund; Research to Prevent Blindness, New York City; Bea Arveson Macular Degeneration Fund; and Foundation Fighting Blindness, Owings Mills, Maryland. One article author is one of the inventors of a patented nutritional supplement to treat AMD that is owned by Bausch and Lomb, for which he receives government compensation. The work of 2 of the editorialists is supported by the National Eye Institute of the National Institutes of Health The editorialists have disclosed no relevant financial relationships.

Arch Ophthalmol. 2011;129:1543-1550. Full text

Study Highlights

  • The predictive model was developed from longitudinal data derived from the AREDS cohort, which was a natural study of AMD and cataract progression that also examined use of zinc and other antioxidants in slowing progression of AMD.
  • Samples of DNA from 2962 AREDS participants (2846 white) were obtained from the AREDS Genetic Repository.
  • Participants received comprehensive ocular and medical histories and ocular examinations at study entry; they also received anthropometric measurements.
  • The AREDS simplified severity scale was used to classify participants by retinal phenotype.
  • This scale was designed to define the risk for advanced AMD using either clinical examination or fundus photography.
  • The system uses 2 retinal abnormalities at baseline to classify risk: the presence of a large drusen, and pigment abnormality.
  • A 5-step severity rating scale is derived from 0 (no risk) to 4 (both risk factors in both eyes).
  • If advanced AMD is present in 1 eye at baseline, that eye is considered to have 2 risk factors.
  • The CAPT cohort was used as the validation cohort for the risk-assessment model.
  • A specific genotyping platform was used to perform genotyping of the AREDS and CAPT participants.
  • The endpoints of the study occurred when participants with no advanced AMD in either eye at baseline progressed to advanced AMD in either eye, or when those with advanced AMD in one eye at baseline progressed to AMD in the fellow eye.
  • Advanced AMD was classified as neovascular or geographic atrophy.
  • Baseline demographic factors affecting AMD progression included age, smoking, family history, body mass index, education, simple scale score, a very large drusen in the fellow eye, and gene variants.
  • 3 components of the prediction model were tested in the CAPT cohort: (1) complete (demographic/environmental, phenotypic, and genotypic); (2) demographic/environmental and phenotypic only; and (3) demographic/environmental and genotypic only.
  • The complete model performed well in the validation study for both discrimination and calibration.
  • For the demographic/environmental and phenotypic factors in model 2, performance was only slightly worse.
  • However, when only genotypic information and demographic/environmental factors were considered in model 3, performance declined.
  • The authors argued that in practice, genotyping is of limited value because of availability and cost and that phenotypic (retinal examination) and demographic/environmental factors were more available to clinicians.
  • Thus, an online version of the predictive model using a risk calculator is now available.
  • The calculator is designed for use with and without a genotype component.
  • Demographic/environmental questions ask about age, first-degree family history of AMD, and current smoking.
  • The retinal examination (phenotype) component uses the simple scale score (0 - 4), the presence of a very large drusen in either eye, and advanced AMD in 1 eye.
  • The genotype component (if known) established the presence or absence of one of 2 genotypes.
  • The risk for advanced AMD is then calculated.
  • For example, a 75-year-old man who smokes and has advanced AMD in one eye with no large drusen in the fellow eye will have a 22% risk for the development of advanced AMD in the fellow eye in 5 years.
  • If genetic information was available, his 5-year risk for advanced AMD would be 13%, 25%, or 34%, depending on his CFH and ARMS2 genotypes.
  • The authors concluded that as more advanced and effective preventive measures for AMD become available, the model would be more useful for risk assessment.
  • They noted that the predictive model would be updated to maintain currency for new data on AMD.

Clinical Implications

  • A full predictive model for advanced AMD takes into account all 3 components: demographic/environmental, phenotypic, and genotypic information. A partial model would remove either genotypic or phenotypic information.
  • The complete model (model 1) is the most discriminating in the prediction of advanced AMD. Next is the demographic/environmental and phenotypic model (model 2), and the demographic/environmental and genotypic model (model 3) is the least discriminating for advanced AMD prediction.

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