Predicting progression of Alzheimer's disease

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Introduction

Clinicians need to predict prognosis of Alzheimer's disease (AD), and researchers need models of progression to develop biomarkers and clinical trials designs. We tested a calculated initial progression rate to see whether it predicted performance on cognition, function and behavior over time, and to see whether it predicted survival.

Methods

We used standardized approaches to assess baseline characteristics and to estimate disease duration, and calculated the initial (pre-progression) rate in 597 AD patients followed for up to 15 years. We designated slow, intermediate and rapidly progressing groups. Using mixed effects regression analysis, we examined the predictive value of pre-progression group for longitudinal performance on standardized measures. We used Cox survival analysis to compare survival time by progression group.

Results

Patients in the slow and intermediate groups maintained better performance on the cognitive (ADAScog and VSAT), global (CDR-SB) and complex activities of daily living measures (IADL) (P values < 0.001 slow versus fast; P values <0.003-0.03 intermediate versus fast). Interaction terms indicated that slopes of ADAScog and PSMS change for the slow group were smaller than for the fast group, and that rates of change on the ADAScog were also slower for the intermediate group, but that CDR-SB rates increased in this group relative to the fast group. Slow progressors survived longer than fast progressors (P=0.024).

Conclusions

A simple, calculated progression rate at the initial visit gives reliable information regarding performance over time on cognition, global performance and activities of daily living. The slowest progression group also survives longer. This baseline measure should be considered in the design of long duration Alzheimer's disease clinical trials.

Doody RS, Pavlik V, Massman P, Rountree SD, Darby E, Chan W. Predicting progression of Alzheimer's disease. Alz Research Therapy 2010; 2:2.

(provisional version)


INTRODUCTION


There is considerable variability in progression rates among Alzheimer’s disease (AD) patients.
Patients and families frequently ask clinicians to prognosticate regarding expected rates of
cognitive and functional decline, and clinicians have little basis for making such predictions. We
have shown that it is possible to reliably estimate early AD symptom onset, and together with
baseline MMSE score, to calculate a rate of progression at the initial assessment (the pre-
progression rate) [1, 2]. The use of a rate to estimate early progression gives information on
severity, but also on how long it took for the patient to reach the current severity level, which
reflects that individual’s disease characteristics better than a severity score alone. However, it is
not clear whether patients maintain a similar rate of decline throughout the course of their
disease or change trajectories over time, due to endogenous or exogenous factors (such as
treatment). Demonstrating the predictive value of the calculated pre-progression rate would be
valuable for patient and family counseling, as well as for providing a research marker of
phenotypic variability to validate biological markers of progression. Further, the ability to model
group progression of AD patients is essential for designing disease-modification studies of new
AD treatments, and pre-progression might be an important baseline variable to take into account
in the analysis of clinical trial data [3].

The Baylor Alzheimer’s Disease and Memory Disorders Center has followed a cohort of AD
patients for up to 15 years, with detailed clinical and neuropsychological data obtained at
baseline and at annual follow up visits which are maintained in an ongoing electronic data base.
We used these data to answer the following questions: 1) does a pre-progression rate
calculated at the initial assessment predict subsequent performance in specific cognitive and
functional domains during follow up, and 2) is the pre-progression rate associated with overall
survival, after adjustment for relevant covariates?


MATERIALS AND METHODS


The Baylor Alzheimer’s Disease and Memory Disorders Center sees self-referred, agency-
referred, and physician-referred individuals for evaluation and management of cognitive
complaints. We evaluate patients for systemic and brain disorders with laboratory testing,
including neuroimaging, and psychometric tests. We assign a diagnosis of various subtypes of
mild cognitive impairment (MCI) or dementia according to standardized criteria through a
consensus conference [4, 5]. Details of the Baylor ADMDC patient recruitment, assessment,
follow up procedures, and long-term clinical outcomes in the patient cohort have been reported
[5]. Patients who meet standardized diagnostic criteria for probable or possible Dementia with
Lewy Bodies are excluded from the Probable AD diagnostic category. Patients included in this
analysis are enrolled in the Baylor Alzheimer’s Disease Center and the database has been
approved by the Baylor Institutional Review Board. Patients and/or their legally designated
representative sign consent for storage and use of their data.

Measures

Cognitive outcome measures routinely obtained at baseline and at annual follow up include the
Mini Mental Status Exam (MMSE), [6] a widely used dementia severity test with scores ranging
from 0 to 30 points, and the Alzheimer’s disease Assessment Scale-Cognitive Subscale
(ADAS), [7] a measure of cognitive domains often impaired in AD including memory, orientation,
visuospatial ability, language, and praxis. Scores range from 0 to 70 with higher scores
reflecting more cognitive impairment. Attention and concentration are assessed with the Verbal
Series Attention Test (VSAT) [8]. This test consists of forward and reverse generation of
arithmetic series, verbal series (for example, months of the year), number-letter sequencing and
auditory vigilance for a spoken target letter and is scored for time taken to complete each task
(up to 480 seconds) and the number of errors made (up to 45). To assess global performance
we use the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) [9, 10]. This score is
derived from a patient interview and mental status examination in conjunction with an interview
of a collateral source. The CDR-SB score (range 0 to 18) is obtained by summing ratings in
each of six cognitive domains or boxes including memory, orientation, judgment/problem
solving, community affairs, home and hobbies, and personal care. Higher scores reflect more
global impairment. Functional outcomes are assessed with the Physical Self-Maintenance Scale
(PSMS) and Instrumental Activities of Daily Living scale (IADL), which together constitute the
Lawton and Brody Activities of Daily Living Scale [11]. The PSMS quantifies difficulties with
basic activities of daily living such as eating and dressing, and each item is scored from 1 to 5
with a maximal score of 30, representing maximal impairment. The IADL evaluates eight
complex daily living tasks such as the use of the telephone, ability to shop, and to make use of
transportation. Scores range from zero to 31, with higher scores indicating more functional
impairment.

Covariates previously reported to influence progression in AD and routinely collected at the
baseline visit are pre-morbid IQ estimated by the American version of the New Adult Reading
Test (AMNART) [12, 13], age, sex, years of education, history or presence of hallucinations,
delusions, and extra-pyramidal signs [14, 15]. In our previous work, premorbid IQ was a better
predictor of progression rates than education [16], and this was taken into account in the
modeling described below. We used a modification of the motor scale of the Unified Parkinson’s
disease Rating Scale to capture extra-pyramidal signs [17].

Vital status is obtained from the National Death Index every six months, with a censoring date
on December 31, 2004.

Calculation of pre-progression rate

The pre-progression rate is calculated using a clinician’s standardized assessment of symptom
duration in years and the baseline MMSE. We obtain the clinician estimate of duration using a
standard procedure which includes a series of questions about the duration of specific
symptoms that might be a sign of AD, combined with medical records review, an informant
interview, and hypothesis-testing. Inter-rater reliability for the estimate is 0.95 [2]. Since a
cognitively intact individual should obtain the maximum MMSE score of 30, the pre-progression
rate is given by the formula: (30 – baseline MMSE)/estimated duration of symptoms in years.
Patients with an MMSE decline of less than two points per year are classified as slow
progressors, between a two- to four-point decline as intermediate progressors, and more than or
equal to five points per year as rapid progressors [1]. In a previous study, we found that use of a
normed MMSE score, based upon age, education, and gender [18] underestimated the baseline
MMSE score for 7% of the subjects [1], which is why we have adopted the maximal score of 30
in our formula. Since MMSE decline is non-linear, we used groupings of MMSE change rates
(slow, intermediate, rapid) which are more clinically relevant than absolute rates of change (for
example, one point per year is really not clinically different from two points per year because of
test-retest variability).

Patient inclusion criteria

Only probable AD patients (NINCDS-ADRDA, DSM IV) were included. Patients had to have a
pre-progression index calculated at baseline, an AMNART score, and at least one
comprehensive follow-up visit approximately one year later.

The first patient was enrolled in 1989, and accrual has been ongoing since then. The AMNART
was incorporated in 1994. The ADAS-Cog, PSMS, and IADL scales were not used routinely until
1995, whereas other outcome measures were collected in earlier years. Rather than requiring
all patients to have all of the outcome measures, we allowed individuals to enter each analysis if
they had a measure of the outcome in question and non-missing values on the adjustment
covariates. We report in the Results section the number of persons included in each regression
equation.

Statistical analysis

The study data are longitudinal, with fixed values associated with demographic characteristics
and baseline clinical presentation, and time varying values on cognitive and functional
outcomes. For the analysis of progression of AD, we used random effects linear regression
models to estimate the relationship between the pre-progression categories and the rate of
change in the ADAS-Cog, VSAT Time, VSAT Errors, CDR Sum of Boxes, PSMS and IADL
scores [19]. Coefficients yielded by this type of model reflect the change, or slope, in the
outcome for each unit change in a predictor variable, holding values of the other variables in the
model constant. The random effect is time in years, and we used a time by pre-progression rate
interaction term to indicate whether or not there is a difference in average rate of decline (slope)
associated with a patient’s initially calculated pre-progression group. A significant time by pre-
progression rate interaction term could represent divergence among the groups in rates of
change. We examined each model for significance of a quadratic term and used non-linear
interactions when the quadratic was significant (but report both the linear and non-linear
interactions in Table 1). Potential confounders or effect modifiers of the association between
cognitive or functional outcomes and the pre-progression rate included age, sex, race/ethnicity
(non-Hispanic whites vs. Hispanic whites, blacks and other ethnicities), years of education,
AMNART score (as a measure of pre-morbid IQ), and baseline clinical features of history or
presence of hallucinations, delusions, and Parkinsonian signs. Each covariate was evaluated in
a base model that included baseline severity (dichotomized as mild or moderate-to-severe
based on MMSE score), duration of symptoms, and pre-progression rate categories (slow,
intermediate, fast). For the baseline covariate, the moderate and severe groups were combined
(MMSE <20) since there were relatively few patients classified as severe at baseline. Covariates
significant at the P <0.10 level were included in a final model for each cognitive or functional
outcome. Our analysis included data for up to seven years of follow-up, since this interval
represented the 90th percentile.

Cox survival analysis with robust variance estimators for correlated observations was used to
examine the contribution of baseline demographic variables, clinician’s standardized estimate of
duration, baseline AMNART score, and baseline MMSE score to annual risk of death. In the
survival analysis, we considered the effect of each study variable alone and then in a full
multivariable model. Using a conservative estimate, our study had 80% power to detect a
reduction in hazard ratio of 32% (based upon N = 124 per group, medians of 8 and 10 years,
type 1 error = 5% and Bonferroni correction).

All analyses were performed using STATA version 9.0.


RESULTS


Of 798 probable AD patients who met inclusion criteria, 597 had the AMNART as part of their
initial baseline assessment. Since the AMNART was a pre-specified covariate, these 597
individuals formed the inclusion sample. Table 2 reports demographic characteristics and
baseline test scores by preprogression group. From 34 to 46% of patients had a history of or
current delusions at their initial visit, and 13 to 22% had a history of or current hallucinations, but
only 3 to 7% had Parkinsonian signs on examination. It is notable that slow progressors had a
longer estimated duration of symptoms than intermediate or fast progressors, consistent with
slow progression. IQ and education were also higher in slow progressors. The distribution of
APO E epsilon 4 alleles did not differ. Significant differences between the groups were taken
into account in the analysis.

Table 1 contains the mixed effects linear regression coefficients associated with pre-progression
categories and the interaction of pre-progression categories with time, after adjustment for the
prospectively defined covariates. Figures 1 to 6 display the fitted regression lines predicted by
the regression model for each outcome. Patients in both the slow and intermediated pre-
progression groups maintained better performance on the ADAS-Cog, the CDR-SB, VSAT Time
and Errors and the IADL, compared to fast pre-progressors, but showed no significant baseline
difference on the PSMS. For example, slow progressors were about 9.5 points better and
intermediate progressors four points better than fast progressors on the ADAS-Cog at baseline
(Table 1). Over time, slow progressors gained 0.6 fewer points per year, and intermediate
progressors gained 0.8 fewer points per year. Figure 1 shows that both of these groups
diverged from the fast group over time. Similarly, slow progressors were 2.6 points lower and
intermediate progressors 1.4 points lower on the CDR-SB to start with (Table 1). This relative
difference between the slow and fast progressors was maintained (no significant interaction
term), while the intermediate progressors gained 0.2 points per year more than the fast
progressors, so that they caught up over time (Figure 4). This tendency of the intermediate
group to speed up on the CDR-SB was probably not accounted for by functional deficits, since
this did not occur on the IADL measure (Table 1 and Figure 5). Basic activities of daily living
assessed by PSMS were not different at baseline and did not begin to diverge until the first
couple of years of follow up (Table 1 and Figure 6), but the slower rate of worsening of the slow
group (1.1 points less per year) led to more divergence from the fast group over time. Table 3
presents information on the relationship of the pre-specified covariates to each outcome. Not
unexpectedly, age was related to cognitive scores, and sex to performance of complex ADLs.
Pre-morbid IQ (AMNART score) was related to the cognitive measures. Education did not
remain a significant predictor of progression on any measure in the presence of the AMNART,
consistent with our previous findings [16]. The presence of delusions at or before baseline was
associated with worse performance on all measures except the VSAT, and hallucinations at or
before baseline were related to lower scores on measures that included activities of daily living.
We did not find a relationship between any of our outcomes over time and the presence of
baseline extrapyramidal signs in this population of probable AD subjects, from whom Dementia
with Lewy Bodies was carefully excluded, and APO E genotype was not associated with the
outcomes.

Average survival from first visit to death was 5.5 ± 2.7 years (median = 5.0 years). The median
survival times for each of the pre-progression categories were: 4.7 years for slow, 4.1 years for
intermediate, and 2.5 years for rapid progressors adjusted for age, sex, education and baseline
severity (Figure 7). The results of Cox proportional hazards modeling indicated that slow
progressors had significantly reduced mortality compared to fast progressors (HR = 0 .62, 95%
CI = 0.43 to 0.91, P = 0.024). Although intermediate progressors are distinguishable on the
survival curves and the curves do not cross, the difference between the intermediate and fast
progressors was not statistically significant (HR = 0.81 95% CI = 0.59 to 1.15, P = 0.24). Our
study may have been underpowered to detect the small difference in survival between these two
groups.


DISCUSSION


We have demonstrated in a large cohort of probable Alzheimer’s disease patients that a simple,
calculated, progression rate at the initial clinic visit is predictive of longitudinal performance on
multiple cognitive and functional measures over time. These measures of cognition (ADAScog),
attention and concentration (VSAT), global performance (CDR-SB), and activities of daily living
(PSMS and IADL) are highly relevant to caregiving needs and to patient and caregiver quality of
life, as well as representing measures commonly employed in clinical trials of AD treatments.
The clearest and best maintained differences were observed between the slow progressors and
those classified as fast progressors, who together constituted 54% of the population. On the
ADAScog, for example, slow progressors maintained nearly a 10-point advantage over fast
progressors (intermediate progressors maintained nearly a four-point advantage). Mixed effects
regression modeling showed that, in effect, slow progressors are unlikely to catch up with fast
progressors on standard outcome measures, even after up to seven years of observation. In
fact, slow progressors diverge further from fast progressors over time on the ADAScog, while
maintaining baseline differences on the VSAT, CDR-SB and IADL. Even though they did not
differ in performance of basic ADL (PSMS) at baseline, slow progressors added disability in this
area at a slower rate than fast progressors so that their performance diverged over time. Slow
progressors also survived longer than fast progressors.

Intermediate progressors (46 % of the patients) also maintained better cognition (ADAScog and
VSAT) and function (IADL) compared to fast progressors, but they were less differentiated at
baseline and sped up over time on a global measure, the CDR sum of the boxes score, and
they were not differentiated at any time on the basic ADL (PSMS). The survival differences
between intermediate and fast progressors were not significantly different, but our study may
have been underpowered to detect a small difference. Our results suggest that prognostications
based upon initial progression rate are most reliable for slow and fast progressors, but that long
duration reliability of an intermediate progression rate may depend upon the patient’s age and
life expectancy at diagnosis. It would be safe to say that an intermediate progressor may remain
so for several years, but that, if the patient lives for a long time after diagnosis, the rate may
increase sufficiently to affect both abilities and survival.

Our methodology for classifying patients as slow, intermediate or rapid progressors could be
easily employed by clinicians to calculate pre-progression rate at an initial clinic visit, using the
MMSE score and a standardized approach to estimating duration [1, 2]. The clinician could
predict that a patient would generally progress slowly, moderately, or rapidly over several years.
However, an important question remains as to whether these apparently intrinsic rates of
disease progression can be modified, and this question must be resolved before the pre-
progression approach is widely adopted for clinical purposes. In a separate paper, we
demonstrated that persistent anti-dementia drug treatment impacts observed progression over
time [20], an observation which is consistent with a recent analysis using a very different
approach [21]. This effect of treatment persistence is significant in our mixed effects models
which also include the pre-progression rate, indicating that treatment may provide benefit to
patients regardless of their intrinsic progression rates. Treatment appears to alter slopes on
measures which include the ones used in the current study, but we have not yet assessed
whether the effect differs by pre-progression category.

Many investigators seek to validate biomarkers of disease progression, such as changes in
hippocampal volume and serum and cerebrospinal fluid (CSF) biomarkers. The progression
rates that are based upon clinical measures in such studies may need to be adjusted for early
progression, or progression group, as well as for persistence of treatment, which could enhance
observed correlations between valid biomarkers and clinical measures.

Our findings have important implications for the design and interpretation of AD clinical trials.
Currently, parallel group studies count on randomization to yield comparable placebo and
treatment groups. Pre-progression rates are not assessed -- yet imbalances across the
treatment groups in this important variable could obscure true treatment differences, or could
create apparent differences when there is no drug effect, especially in long duration clinical
trials. Further, if our hypothesis that the persistency of anti-dementia drug treatment alters
progression is correct, baseline differences in cumulative duration of drug use could create
similar imbalances. Future clinical trials may benefit from gathering systematic data regarding
individual symptom onset in order to perform a formal estimate of duration [2] and to calculate
pre-progression rates [1], which could be used to stratify patients by progression group or as a
covariate in the analysis. For those clinical trials that allow background treatment with marketed
anti-dementia drugs while testing a new therapy against placebo, information about the quartile
of persistence of anti-dementia treatment may also be needed to control for the impact of these
variables in the analysis [20].

Our study has both strengths and limitations. It is a large study, including nearly 600 carefully
diagnosed probable AD subjects followed for up to 15 years. Yet all of the subjects were
followed at a single site, and we do not know how consistent our results would be in a multi-site
study. Although we are located at a tertiary care center, we are one of the few clinics providing
dementia care in the state, and we have few barriers to access, which together have led to an
unusually diverse population [5]. Still we utilized a sample of convenience which may not be
representative of the general AD population, and we do not know whether our results would be
the same in a community based sample.

Further, because we did not randomize patients according to pre-progression rates at baseline,
our inclusion of consecutive cases yielded groups of unequal size. We made appropriate
adjustments to our analysis for clinical variables shown or hypothesized to influence rates of
progression and survival in AD, including age, sex, education, premorbid IQ, hallucinations,
delusions and extrapyramidal features. The progression group was an important predictor of
longitudinal course even when these factors were taken into account.
Another strength of the study is our choice of standardized outcomes that are in clinical use and
widely used in clinical trials. The importance of our findings is strengthened by the fact that the
current data are internally consistent across multiple measures; progression groups maintained
their differences on measures that included cognition, global performance, and activities of daily
living. The fact that survival data were available for every subject and that survival time also
differentiated the slow and fast progressors provides additional evidence for the clinical utility of
the pre-progression rate.


CONCLUSIONS


In conclusion there is a lack of data in the medical literature to guide clinicians and researchers
in understanding the progression of Alzheimer’s disease. Our data provide powerful evidence
that prediction is possible, which addresses an important clinical need. Additionally, inclusion of
the pre-progression rate in clinical trials for proposed AD therapies should enhance the power of
such studies to find real treatment differences, and could reduce the duration of trials designed
to assess disease-modifying therapies, which would also aid patients and those who care for
them.


ABBREVIATIONS


AD = Alzheimer’s disease; ADAScog = Alzheimer’s disease Assessment Scale cognitive
subscale; ADMDC = Alzheimer’s Disease and Memory Disorders Center; AMNART = American
New Adult Reading Test; APO E = apolipoprotein E; CDR-SB = Clinical Dementia Rating Scale
cognitive subscale; CSF = cerebrospinal fluid; DSM IV = Diagnostic and Statistical Manual of
Mental Disorders; IADL = Instrumental Activities of Daily Living; MMSE = Mini-mental Status
Examination; IQ = intelligence quotient; NIH = National Institutes of Health; NINCDS-ADRDA =
National Institute of Nervous and Communicative Disorders and Stroke-Alzheimer’s Disease
and Related Disorders Association; PSMS = Progressive Self-Maintenance Scale; VSAT =
Verbal Series Attention Task.


COMPETING INTERESTS


The authors declare that they have no competing interests


AUTHORS' CONTRIBUTIONS


RSD designed the study, drafted the manuscript, and obtained funding. RSD and SDR were
involved in data acquisition and critical revision of the manuscript. RSD, VP, PM, and WC were
involved in data analysis and critical revision of the manuscript. ED managed the database and
was involved in data analysis and critical revision of the manuscript. All authors read and
approved the final manuscript.


ACKNOWLEDGEMENTS


This patient cohort was supported, in part, by NIH grant AGO-8664 until 2000, and by a Zenith
award from the Alzheimer’s Association in 2002-2004. Dr. Doody receives support from the Cain
Foundation and Dr. Doody and Dr. Rountree receive support from the Cynthia and George
Mitchell Foundation.


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TABLES

Table 1: Relationship between pre-progression category and
subsequent rate of decline on cognitive and functional measures

Table 2: Selected patient characteristics at baseline by
preprogression category (n = 597)

Table 3: Effect of covariates: betas (P-values) for significant
covariates


FIGURE LEGENDS

Figure 1

Fitted regression lines for ADAScog by pre-progression category calculated from model
coefficients shown in Table 1.

Figure 2

Fitted regression lines for VSAT time by pre-progression category calculated from model
coefficients shown in Table 1.

Figure 3

Fitted regression lines for VSAT errors by pre-progression category calculated from model
coefficients shown in Table 1.

Figure 4

Fitted regression lines for CDR-SB by pre-progression category calculated from model
coefficients shown in Table 1.

Figure 5

Fitted regression lines for IADL by pre-progression category calculated from model coefficients
shown in Table 1.

Figure 6

Fitted regression lines for PSMS by pre-progression category calculated from model coefficients
shown in Table 1.

Figure 7

Kaplan-Meier Survival curves by pre-progression group adjusted for age and sex. HR for slow
vs. fast = 0.62 (P = 0.024).




_____
Rachelle S Doody (corresponding author)
Alzheimer’s Disease and Memory Disorders Center, Baylor College of Medicine, 6501 Fannin
Street, NB302, Houston, TX 77030
E-mail: rdoody@bcm.edu

© 2010 Doody et al.; licensee BioMed Central Ltd. This is an open access article distributed
under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.