New AMD risk calculator
MyVisionTest
of advanced age-related macular degeneration (AMD) incorporating
phenotypic, demographic, environmental, and genetic risk factors.
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.
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, phenotypic
risk factors derived from large population-based and interventional
studies, and established genetic risk variants.
Methods & Results
The researchers 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.
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.
Discussion & Conclusions
The researchers 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, diabetes,
and cancer. Risk models for ophthalmic diseases have been reported for
primary open-angle glaucoma and proliferative vitreoretinopathy. 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, and predictive models have
recently been presented.
The prediction model for advanced AMD described by Seddon et al 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 al 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.
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. 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.
The complete model comprises 3 risk factor components —
demographic/environmental, phenotypic, and genetic. Using statistical
methods to assess performance of various combinations of the model’s
3 components, the researchers 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 (FIGURE). 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.
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.
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.
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. 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).
In summary, researchers 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.
Arch Ophthalmol. 2011 Dec;129(12):1543-50
http://www.ncbi.nlm.nih.gov/pubmed/21825180
======================
ABOVE IS A PLAIN TEXT REPRODUCTION OF A NEWS STORY THAT APPEARS ON
MYVISIONTEST.COM. ANY HYPERLINKS, IMAGES, TABLES, OR OTHER
ENHANCEMENTS HAVE BEEN REMOVED. TO VIEW THE ORIGINAL VISIT THE BELOW
LINK.
http://www.myvisiontest.com/newsarchive.php?id=1364