Risk Assessment Models for Late AMD
MyVisionTest
signs of age-related macular degeneration (AMD), they often ask about
the risk of their AMD progressing. Long-term epidemiological studies
that have collected information on the prevalence, incidence, and
natural history of AMD provide data to answer this question. These
studies have shown that risk is influenced by many things, including
the severity of the signs of AMD that are already present (eg, drusen
size, area of involvement, and type and presence of pigmentary
abnormalities), demographic factors (eg, age, sex, and
race/ethnicity), family history of the disease, lifestyle factors (eg,
smoking, heavy drinking, and physical activity), medical conditions
(eg, inflammatory and pulmonary disease), environmental exposures (eg,
light), and ocular factors (eg, history of cataract surgery). Risk
assessment models account for multiple exposures that may influence
risk. These have been useful for heart disease and have recently been
developed for AMD. These models may also help to decide on the
interval of follow-up and whether to suggest options for preventive
care (eg, dietary supplements and specific foods).
The new risk assessment model proposed by Klein et al is based on
findings from the Age-Related Eye Disease Study (AREDS) for the
development of advanced AMD. It was designed for “the identification
of those individuals with early AMD who are at greatest risk to
progress to advanced, vision-threatening AMD (geographic atrophy or
neovascular AMD) ... and to predict when progression to advanced AMD
might occur.” The model uses the modified simple scale score of AMD
severity from the AREDS including indicator variables for the presence
of very large drusen and the presence of late AMD in 1 eye, age,
smoking history, history of AMD in a first-degree relative, and the
presence or absence of 2 genetic variants (CFH Y402H and ARMS2 A69S)
for the individual patient. It was restricted to white patients. The
area under the receiver operating characteristic curve showed good
discrimination between those who would progress to late AMD and those
who would not. Two previous models also showed similar overall
performance, although the end points differed between studies.
Of particular interest in the article by Klein and colleagues is that
the best model was the one that included 3 groups of risk factors:
demographic/environmental (age, smoking), phenotypic (severity of
early AMD based on the simple AREDS severity scale), and genetic
(ARMS2 and CFH Y402H). This model was just slightly better than the
model including only the first 2 of these groups; genetic factors
added only 0.007 to the C statistic, the estimate of the improvement
of the model. Figure 1 in the article by Klein and colleagues
graphically depicts the very small contribution of the presence of
“risk” alleles to decreasing prediction error in the model. The
difference between models including phenotypic and demographic
characteristics and including only demographic and genetic factors may
reflect the fact that phenotype includes effects of the several other
genes influencing lesions of AMD, gene X environment interactions, and
other possible environmental effects such as diet and supplement
deficiencies that may have influenced the phenotype before study
entry. 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. It is possible that the model would perform differently
in other populations.
A question arising from the fact that the risk assessment model was
derived from data from participants in a trial is whether the model is
applicable to persons in a general population who may be more like an
average patient in a clinic setting. Having replicated the validity of
the model in participants from another trial does not assure the
usefulness of this model in clinical practice. For this reason, we
examined the performance of the proposed risk formula (kindly provided
by Klein and colleagues) in a group of 1575 persons aged 55 to 80
years at baseline in the population-based Beaver Dam Eye Study (BDES).
These persons were white, participated in a 5-year follow-up
examination, and were at risk for developing late AMD in at least 1
eye. In all categories, the model predicted greater risk of
progression to late AMD than actually occurred in the BDES sample. For
example, in the BDES subgroup predicted to have a 20% to 30% 5-year
risk of progression to late AMD, the actual 5-year incidence of late
AMD was 4%. The model should be tested in other population-based
cohort studies.
Klein and colleagues did not include information on supplement intake,
a risk factor that the AREDS investigators are uniquely able to
examine. They inform the reader that one could expect a 10% to 15%
reduction or increase in calculated risk depending on the treatment
assignment. Why not consider this a person-specific risk factor in the
risk assessment analysis?
The importance of the genetic factors as a component of the model in
assessment tools in which the phenotype has been characterized invites
further consideration as to the usefulness of such factors for
predicting risk in clinical practice.
Ophthalmology. 2011 Feb;118(2):332-8
http://www.ncbi.nlm.nih.gov/pubmed/20801521
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