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A Method for Estimating Progression Rates in Alzheimer Disease
Rachelle Smith Doody, MD, PhD;
Paul Massman, PhD;
J. Kay Dunn, PhD
Arch Neurol. 2001;58:449-454.
ABSTRACT
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Background The ability to predict progression of disease in patients with Alzheimer
disease (AD) would aid clinicians, improve the validation of biomarkers, and
contribute to alternative study designs for AD therapies.
Objective To test a calculated rate of initial decline prior to the first physician
visit (preprogression rate) for its ability to predict progression during
subsequent follow-up.
Methods We calculated preprogression rates for 298 patients with probable or
possible AD (using the criteria of the National Institute of Neurological
and Communicative Disorders and Stroke and the Alzheimer's Disease and Related
Disorders Associations (NINCDS-ADRDA) with a formula using expected Mini-Mental
State Examination (MMSE) scores, scores at presentation, and a standardized
estimate of duration. The patients are being followed up longitudinally in
our Alzheimer Disease Research Center . The time to clinically meaningful
deterioration, defined as an MMSE score drop of 5 or more points, was compared
for patients stratified as slow, intermediate, and rapid progressors based
on the preprogression rate. Cox regression analysis was used to examine the
contribution of demographic variables (age, sex, ethnicity, and level of education),
initial MMSE scores, estimated symptom duration, and the calculated preprogression
rate to the time it took to reach the end point across the groups.
Results Both initial MMSE (hazard ratio, 0.95 (0.002); z
= 4.19; P<.001) and the calculated preprogression
rate (hazard ratio, 1.06 (0.019); z = 3.16; P = .002) were significant in determining time to clinically
meaningful decline during longitudinal follow-up (Cox regression analysis).
Slow, intermediate, and rapid progressors (based on preprogression rates)
experienced significantly different time intervals to clinically meaningful
deterioration, with the slow progressors taking the longest time, the intermediate
progressors in the middle, and the rapid progressors reaching the end point
first (log rank  21 = 9.81, P = .002).
Conclusion An easily calculable rate of early disease progression can classify
patients as rapid, intermediate, or slow progressors with good predictive
value, even at initial presentation.
INTRODUCTION
NEWLY DIAGNOSED patients with Alzheimer disease (AD) and their families
frequently ask: How severe is the disease? How fast will it progress? How
much longer do I have before it gets bad? The ability to predict progression
rates would aid clinicians, patients, and patients' families in decision making.
Models for predicting progression would also help to validate putative biomarkers,
which should correlate better with progression rates than with severity if
they reflect the active pathogenic process. Finally, better predictive models
would affect the design of clinical trials by making it possible in some cases
to use changes in individual progression rates to assess the efficacy of disease-modifying
therapies.
Natural history studies as well as data on placebo groups in AD clinical
trials have documented tremendous between-subject and between-group variability
of measured progression rates. This reported heterogeneity likely reflects
multiple phenomena, including (1) true differences in disease progression
rates between patients; (2) differing properties, ie, floor and ceiling effects,
of the measures selected; (3) differences in the end points selected to represent
progression (cognitive decline, functional decline, nursing home placement,
or death); (4) other methodological differences, such as the number of patients,
duration of follow-up, and interval between visits; (5) differences in medical
comorbidities; and (6) differences in patient care. Some authors have proposed
that certain clinical features, such as age at onset, sex, duration of illness,
and the presence or absence of extrapyramidal features are associated with
the time to progression as assessed by nursing home placement, drop in Mini-Mental
State Examination (MMSE) score, or death.1, 2, 3
Others have suggested that the initial stage of disease at the beginning of
the observation period ("how far") is an important predictor of subsequent
decline ("how fast").3, 4 Most
staging measures fail to document linear decline over the course of AD,5, 6 and as a result both "bilinear"7 and "trilinear"8, 9
models of decline have been proposed. However, no clear, predictive models
of progression have been developed and validated for AD.
Alzheimer disease likely begins histopathologically many years before
clinical symptoms are apparent.10 The insidious
onset of symptoms makes it difficult for patients and families to be certain
that an abnormal cognitive state has actually started, and often complicates
the initial diagnosis. Patients therefore come to medical diagnosis at variable
intervals after the first symptoms begin. Previous studies have reported average
estimates of disease duration from 3 to 4.5 years at the time of first clinical
presentation.4, 6, 11
At the time of presentation, a period of symptomatic disease has therefore
existed for long enough to allow an estimate of initial rates of decline,
reflecting disease activity prior to the in-clinic observation of progression
(preprogression). We hypothesized that this calculated preprogression rate
of decline would be predictive of subsequent disease progression (ie, rapid
initial progressors would continue to decline more rapidly than slow initial
progressors), but the relationship would not be linear. We therefore developed
a simple procedure for estimating initial progression at the patient's first
visit, and examined the ability of this measure to accurately predict the
time to significant clinical decline during subsequent longitudinal observation
in our Alzheimer's Disease Research Center (ADRC) at Baylor College of Medicine,
Houston, Tex. We also examined the value of several previously reported variables,
including age at onset, sex, level of education, duration of symptoms, and
initial severity, for predicting observed progression.
PATIENTS AND METHODS
PATIENTS
Three hundred and thirty-six patients were recuited to our ADRC and
diagnosed with probable (78%) or possible (22%) AD according to established
criteria.12 Patients were self-referred or
physician-referred and most often came for an initial diagnostic evaluation.
Approximately half were from the greater Houston area, and the rest were from
elsewhere in Texas or from other states. All eligible subjects who met the
following criteria were analyzed for this study: complete demographic data,
a standardized physician's estimate of duration prior to diagnosis, an initial-visit
MMSE score of 5 or higher, at least 1 year of follow-up, and at least 3 MMSE
scores. The requirements for a minimum follow-up and minimum number of MMSE
scores were designed to improve the reliability of the estimated slopes of
observed MMSE score decline.13 Thirty-eight
subjects did not meet the eligibility criteria: 1 lacked the physician's estimate
of duration; 10 had initial MMSE scores lower than 5; 18 had only 2 MMSE scores;
and 8 had less than 1 year of follow-up at the time of the analysis. Ninety
percent of our subjects were white; 65% were women; and the overall mean (SD)
age was 70 (8.3) years. The mean education level was 13 (3.5) years, and the
mean duration of symptoms prior to diagnosis in our center was 3 (1.8) years.
MEASURES
Duration of symptoms was estimated according to a standardized ADRC
protocol: we interviewed the patient and all available informants, reviewed
medical records to look for previous chronologies of symptoms, and asked the
patient's caregiver to estimate the duration of 34 symptoms commonly associated
with AD. The physician then estimated the duration of symptoms to the nearest
half year after resolving any discrepant information through further questioning
and by relating time frames to the patient's life events.
A preprogression rate was calculated for each patient according to the
following formula: (MMSE score [expected] - MMSE score [initial]) /
physician's estimate of duration [in years]). The expected MMSE score was
derived from a table of age- and education-corrected population-based norms.14 The importance of using MMSE normative data rather
than assuming that the premorbid MMSE should be a perfect score of 30 for
every patient is illustrated by the following example: a healthy 60-year-old
man with a college degree would be expected to score 29, whereas a normal
20-year-old man with 4 years of education would only be expected to score
a 20.14
Based on calculated preprogression rates, patients were stratified into
slow (0-1.9 MMSE points per year), intermediate (2-4.9 MMSE points per year),
and rapid progressors ( 5 MMSE points per year). The cutoff points were
based on literature showing that average group decline is usually in the range
of 2 to approximately 4 points per year.5, 6
We therefore set this range as intermediate, and defined slow and fast in
relation to it. Twenty patients in whom the preprogression rate was unexpectedly
less than 0 were considered a separate group for the analysis.
The observed progression rate during follow-up was obtained by the formula
(MMSE score [last] - MMSE score [initial]) / interval between follow-up
visits (years). For generating Kaplan-Meier curves and performing Cox regression
analysis, we chose as our clinical progression end point a drop of 5 or more
points on the MMSE score from the initial clinic visit. This value was chosen
to reflect clinically meaningful cognitive decline. Few patients' scores decline
this much in a typical year, and patients who improve or "back-cross" on the
MMSE score rarely achieve a 5-point improvement in score.15
STATISTICAL ANALYSIS
Basic statistics (ie, frequencies for categorical variables, means,
and SDs for continuous variables) for the following variables were initially
determined for the group as a whole: sex, ethnicity, age, education, expected
MMSE score (normative), physician's estimate of symptom duration, initial-visit
MMSE score, calculated preprogression rate, follow-up years, observed progression
rate, and percentage of patients reaching the end point of a 5-points decline
in MMSE score.
Pearson's r (with Bonferroni corrections for
multiple comparisons) was used to test for linear correlations between the
continuous variables. Analysis of variance was used to test whether the continuous
variables of interest differed for the groups stratified into slow, intermediate,
rapid, or "negative" progressors, based on the calculated preprogression rate,
with the 2 test used for the categorical variables. Cox regression
analysis was used to look for predictors of the time to a 5-point decline
in MMSE score. After testing the assumption of proportional hazards (Schoenfield
residuals), 1-variable, 2-variable, and finally multivariable Cox equations
were considered, using the following variables: age, sex, education level,
physician's estimate of duration, initial-visit MMSE score, and preprogression
rate. Age and education level were considered both as continuous and categorical
variables (age, <65 years vs age 65 years; education, high school or
less vs more than high school). These models were assessed with and without
the small group of 20 individuals with a negative preprogression rate. The
overall fit of the models was assessed by the Cox-Snell residuals, and the
model accuracy and outliers were assessed by the pattern of deviance residuals.16 Finally, we constructed Kaplan-Meier curves depicting
the time to 5-point or greater MMSE score drops for the estimated slow, intermediate,
and fast progressors, and analyzed the separation of these groups over time
with the log rank test.
RESULTS
The mean (SD) normalized MMSE score was 28 (1.4); initial MMSE score,
20 (6.3); and last MMSE score, 12 (8.7). The calculated preprogression rate
for the group of 298 subjects was 3 (3.4) points per year on the MMSE score,
and the mean observed progression during follow-up was 3 (3.2) points per
year. At the time of the analysis, 33% of the subjects had been observed for
1 year, 28% for 2 years, 18% for 3 years, and 22% for 4 or more years, with
continued follow-up ongoing. For 20 subjects, the initial-visit MMSE score
was higher than the population-derived expected MMSE score, indicating that
the normative score was not accurate for these subjects, and yielding a negative
preprogression rate. The MMSE values and progression rates as well as the
average duration, average follow-up, and percentage reaching the end point
are given for the 298 patients in Table
1.
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Table 1. Progression Characteristics of Patients With Alzheimer Disease*(N
= 298)
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Significant correlations between the demographic and progression variables
are given in Table 2. As expected,
the preprogression rate correlates negatively with its components: initial
MMSE score and duration of symptoms. Also as expected, the initial MMSE score
correlates with the final MMSE score, and correlates negatively with the symptom
duration. Observed progression rate did not correlate with the initial MMSE
score, perhaps reflecting the nonlinearity of progression as assessed by this
measure (confirmed by a graph of the 2 variables, not shown), and the preprogression
rate did not correlate with the observed progression rate or time to event,
probably for the same reason.
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Table 2. Pearson Correlations*
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Stratification by preprogression rates classified 123 patients as slow,
110 as intermediate, and 65 as rapid progressors, and 20 as negative progressors.
Since the clinical significance of this last group (comprising patients with
higher MMSE scores at presentation with dementia than would be predicted if
they were not demented) is unknown, the analysis was performed both with and
without this group. Data for slow, intermediate, and rapid progressors is
given in Table 3. Analysis of
variance testing for differences in age, length of follow-up, and 2 analysis of sex percentages did not reveal differences across the
stratified groups, but education level did differ across the preprogression
categories (N = 298; F3,294 = 5.18, P
= .002). Negative preprogressors had a mean (SD) 11 (3.6) years of education
vs 14 (3.2) years for slow, 13 (3.8) for intermediate, and 12 (3.1) years
for rapid progressors. Because analysis of variance assumptions were not met
for initial MMSE scores across preprogression categories, this variable was
examined by the Kruskal-Wallis test and showed significant differences (23 = 14.51, P = .002), with a gradient
of lower scores with more rapid preprogression rates, as would be expected.
The physician's estimate of duration also differed in the Kruskal-Wallis test,
with the shortest duration in the most rapidly progressing subjects (23 = 65.99, P<.001). The results
were the same with and without the 20 subjects with negative preprogression
rates, and were the same when we used a MMSE of 30 (instead of the normalized
score) in the preprogression formula.
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Table 3. Comparison of Patients With Slow, Intermediate, and Rapid
Progression of AD by Preprogression Rate in Lost MMSE Points per Year*
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Rapid progressors, intermediate progressors, slow progressors, and patients
with negative preprogression rates were equally likely to reach the end point
of a 5-point or greater MMSE score drop (23 = 4.30, P = .23). Seventy-three percent of the patients in this
study experienced this degree of decline in the course of follow-up, reflecting
the fact that we achieved sufficient follow-up for patients in all groups
to detect significant progression. Individual Cox regression analyses examining
age (continuous or categorical), sex, education level (continuous or categorical),
duration, calculated preprogression rate, and MMSE score at first visit, individually,
as predictors of time to event showed that only the preprogression rate (hazard
ratio [SE], 1.06 [0.019]; z = 3.16; P = .002; confidence interval [CI], 1.021-1.094) and initial MMSE score
(hazard ratio [SE], 0.96 [0.011]; z = -3.87; P<.001; CI, 0.936-0.979) were significant.
When age, education level, disease duration, and sex were added to preprogression
rate in the Cox model, preprogression rate remained significant, but none
of the other variables reached significance. The results were similar with
and without the negative preprogressors. When these variables were added one
at a time to the model using initial visit MMSE scores, both initial visit
MMSE score (hazard ratio [SE], 0.95 [0.011]; z = -4.25; P<.001; CI, 0.930-0.974) and duration (hazard ratio
[SE], 0.92 [0.038]; z = -2.05; P = .04; CI, 0.847-0.996) were simultaneously significant, but none
of the other variables yielded P values close to
significant. When the 20 negative preprogressors were omitted, only initial
visit MMSE score remained significant in the Cox model that included initial
visit MMSE scores. Models using multiple (>2) variables in addition to either
preprogression or initial-visit MMSE score did not yield any other significant
variables. The model of choice is therefore either preprogression alone or
initial MMSE score with duration if the negative preprogressors were excluded.
When the analysis was repeated on the probable AD group alone (n = 233), preprogression
and intial MMSE score, but not duration, were significant. The power was too
low to draw any conclusions for the possible AD group (n = 65). Findings of
the Cox-Snell residuals suggest that the model using both initial-visit MMSE
score and duration may fit slightly better than the preprogression rate, but
the pattern for deviance residuals did not distinguish between the 2 models
(data not shown).
Kaplan-Meier curves comparing the time to event for the slow, intermediate,
and rapid preprogressors are shown in Figure
1. The log rank test demonstrated that the time to event was different
when the 3 groups (22 = 20.17, P<.001) vs the 4 groups (creating a separate group for the 20 patients
with a negative preprogression rate) were considered (data not shown). The
mean (SD) time to a 5-point MMSE score drop (end point) was 2.3 (1.42) years
for the slow progressors, 1.8 (1.13) years for the intermediate progressors,
and 1.6 (0.94) years for the rapid progressors. The greater variability in
the intermediate group may indicate multiple factors influencing progression
in this group. Additional Kaplan-Meier curves comparing the time to event
for those achieving equal to or above the median initial MMSE score (21 points)
with those below it were also generated (not shown), and the log rank test
demonstrated significant differences (21 = 9.81, P = .002). A post hoc analysis comparing the relationship
between MMSE score severity group (5-10, 11-19, and 20-30 MMSE points) and
subsequent observed progression rate (< 0, 0-1.9, 2-4.9, 5 MMSE points/y)
was negative (23 = 10.26, P
= .11).
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Kaplan-Meier curves showing the likelihood of reaching the end point
of clinically significant decline, defined as a 5-point or greater drop on
the Mini-Mental State Examination (MMSE) score, by group (based on estimated
rate of progression prior to visit 1). Group 1 had calculated preprogression
rates of less than 2 points per year; group 2, 2 to 4.9 points per year; and
group 3, 5 or more points per year.
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COMMENT
Progression rates of AD are variable between subjects, and a graph of
cognitive decline vs time does not yield a linear function over the course
of the disease. Nonetheless, we have shown that progression rates in AD show
some consistency in that patients who begin with progression rates that are
more rapid than average (5 MMSE points per year) continue to experience
clinically significant decline sooner than patients who begin at slow ( 2
points per year) or average rates (2-4.9 points per year). We used a calculated
rate of progression, reflecting disease activity prior to the initial visit
(the preprogression rate) as well as data obtained by longitudinal follow-up
of up to 10 years of 298 patients in our ADRC. Several of our results support
this conclusion of consistency in progression: (1) Kaplan-Meier curves graphing
the time to significant clinical decline that show continued separation of
the slow, intermediate, and rapid preprogressing patients; (2) standardized
estimates of disease duration were shorter for those classified as rapid preprogressors,
and longest for the slow preprogressors, with the intermediate preprogressors
showing times in the middle; and (3) the fact that initial visit MMSE scores
were lowest for patients who were rapid preprogressors, with the expected
gradient for the intermediate and slow groups.
Our data support previous reports that AD-associated drop in MMSE scores
over time is nonlinear5, 6, 7, 8, 9:
there was no simple correlation in our study between preprogression rates
and observed progression rates, despite the fact that the average (SD) preprogression
rate (3 [3.4] MMSE points per year) was similar to the observed progression
rate for the group (3 [3.2] points per year). We did observe a relationship
between preprogression and the observed time to significant clinical deterioration,
defined as the time to a 5-point drop on the MMSE score. Selection of the
time to clinically significant worsening as our primary outcome measure and
use of a survival analysis allowed us to discover the importance of preprogression
rates to subsequent deterioration on the MMSE score, even though decline on
this measure is not linear.
Our model differs from that of Stern et al3
in several ways. First, their model seeks to predict mean progression rates
(and variances), whereas ours is an attempt to predict the interval before
clinical decline. Their predictors were derived from one population and applied
to another, while ours were derived from a single population. Theirs is a
multicenter study while ours is a single-center study, although the number
of patients included in the 2 studies is comparable. The 2 models are compatible
but lead to different information: after applying their approach, a patient
can be given a mean time and range before reaching nursing home placement
or death. After applying our method, the patient might be told whether he/she
is declining at a rapid or a slow rate, and how long it might be before clinically
meaningful deterioration occurs, of a nature to be reflected on the MMSE score.
Our results also show that, for modeling purposes, knowing the duration
of symptoms in addition to the initial visit MMSE score (2 of the 3 components
of preprogression rate) is as good as knowing the preprogression rate. However,
combining these variables into the simple formula that we used to calculate
preprogression rates makes the data points more useful in a clinical setting,
and the predicted time to clinical deterioration was similar with both methods.
The use of the preprogression rate was problematic for 20 subjects who had
higher MMSE scores when they presented with dementia than the population-based
norms would have predicted for their best normal scores. Most of these individuals
had relatively low (less than high school) educational levels, suggesting
that the normative MMSE values we used for this study14
might not be as accurate for subjects with lower educational levels. However,
when these 20 negative preprogressors were excluded from the analysis, or
when we substituted an MMSE score of 30 in place of the normalized score,
the predictive value of the preprogression rate remained significant.
In conclusion, our data suggest that it is reasonable to calculate a
patient's rate of progression prior to presentation and to use this information
to predict whether subsequent clinical decline will occur at a shorter-than-average,
average, or longer-than-average interval. These findings are limited because
we used only the MMSE score as a measure of decline. Future studies will investigate
whether the MMSE-based preprogression rate is predictive of decline on other
measures. Also, we have not yet observed all of the subjects throughout the
duration of their disease, so our findings apply only to individuals observed
for at least a year and up to 10 years. We do not know whether the predictive
utility of the preprogression rate is equally strong in the first, second,
or third years, etc. Better understanding of the preprogression rate will
likely lead to models of progression in AD that can be used for clinical prognostication,
validation of putative biological markers, and for designing clinical trials
of disease-modifying therapies where patients' preprogression rates may serve
as controls for their postintervention rates of decline.
AUTHOR INFORMATION
Accepted for publication January 2, 2000.
Drs Doody, Massman, and Dunn were supported by Alzheimer's Disease Research
Center grant AGO8664 from the National Institute on Aging, Bethesda, Md. This
work was also supported by the B. and L. Martin fund and the Fyfe Foundation,
both in Houston, Tex.
We would like to thank Elaine Teoh for her assistance in preparing the
manuscript, and Peggy Lynn for her assistance in an earlier version of the
analysis.
From the Departments of Neurology, Alzheimer's Disease Research Center
(Drs Doody and Massman), and Internal Medicine, Division of Design and Analysis
(Dr Dunn), Baylor College of Medicine, Houston, Tex.
Reprints: Rachelle Smith Doody, MD, PhD, Baylor College of Medicine,
Department of Neurology, 6550 Fannin St, Suite 1801, Houston, TX 77030.
REFERENCES
| |
1. Jacobs D, Sano M, Marder K, et al. Age at onset of Alzheimer's disease: relation to pattern of cognitive
dysfunction and rate of decline. Neurology. 1994;44:1215-1220.
FREE FULL TEXT
2. Chui H, Lyness S, Sobel E, Schneider L. Extrapyramidal signs and psychiatric symptoms predict faster cognitive
decline in Alzheimer disease. Arch Neurol. 1994;51:676-681.
ABSTRACT
3. Stern Y, Tang M, Albert M, et al. Predicting time to nursing home care and death in individuals with
Alzheimer disease. JAMA. 1997;277:806-812.
ABSTRACT
4. Kraemer H, Tinklenberg J, Yesavage J. "How far" vs "how fast" in Alzheimer disease. Arch Neurol. 1994;51:275-279.
ABSTRACT
5. Salmon D, Thal L, Butters N, Heindel W. Longitudinal evaluation of dementia of the Alzheimer type: a comparison
of three standardized mental status examinations. Neurology. 1990;40:1225-1230.
FREE FULL TEXT
6. Morris J, Edland S, Clark C, et al. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD),
IV: rates of cognitive change in the longitudinal assessment of probable Alzheimer's
disease. Neurology. 1993;43:2457-2465.
FREE FULL TEXT
7. Haxby J, Raffaele K, Gillette J, et al. Individual trajectories of cognitive decline in patients with dementia
of the Alzheimer type. J Clin Exp Neuropsychol. 1992;14:575-592.
ISI
| PUBMED
8. Stern R, Mohs R, Davidson M, et al. A longitudinal study of Alzheimer's disease: measurement, rate, and
predictors of cognitive deterioration. Am J Psychiatry. 1994;151:390-396.
ABSTRACT
9. Brooks J, Yesavage J. Identification of fast and slow decliners in Alzheimer disease: a different
approach. Alzheimer Dis Assoc Disord. 1995;9(suppl):S19-S25.
10. Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol. 1991;82:239-259.
FULL TEXT
| PUBMED
11. Bracco L, Gallato R, Grigoletto F, et al. Factors affecting course and survival in Alzheimer's disease: a 9-year
longitudinal study. Arch Neurol. 1994;51:1213-1219.
ABSTRACT
12. McKhann G, Drachman D, Folstein M, Katzmann R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA
Work Group under the auspices of the Department of Health and Human Services
Task Force on Alzheimer's Disease. Neurology. 1984;34:939-944.
FREE FULL TEXT
13. Van Belle G, Uhlmann R, Hughes J, Larson E. Reliability of estimates of changes in mental status test performance
in senile dementia of the Alzheimer type. J Clin Epidemiol. 1990;43:589-595.
FULL TEXT
|
ISI
| PUBMED
14. Crum R, Anthony J, Bassett S, Folstein M. Population-based norms for the Mini-Mental State Examination by age
and educational level. JAMA. 1993;269:2386-2391.
ABSTRACT
15. Clark C, Sheppard L, Fillenbaum G, et al. Variability in annual Mini-Mental State Examination score in patients
with probable Alzheimer disease. Arch Neurol. 1999;56:857-862.
FREE FULL TEXT
16. Nardi A, Schemper M. New residuals for Cox regression and their application to outlier screening. Biometrics. 1999;55:523-529.
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