Analysis of Data Reports in Published Journal Articles

EACH ARTICLE HAS TO ADDRESS:

1.Are the procedures used parametric or nonparametric? Why?

2. Were hypothesis testing errors present? What are the consequences for the studies if a Type I or a Type II error was made?

On the 1st two slides you will deal with atrial fibrillation.You need to put in the findings again for this topic. I just need a plain slide with no colors on it and text should be on bullet form.
Underneath each slide should be a 100 words speaker notes. And I need to have a peer-review citation like a reference.

Other paper is about alzheimers. You need a 2 slide for this topic.You need to put in the findings again for this topic and I need it to be in bullet form. I just need a plain slide with no colors on it.
Underneath each slide should be a 100 words speaker notes. And I need to have a peer-review citation like a reference.

Each article(2) analysis is required to have 2 power point slides each total 4, with Each slide will need a 100 word speaker note with citations.
Introduction
Th ere 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].
Th e 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
refl ects 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-modifi cation 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].
Th e 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
Abstract
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 eff ects regression analysis, we examined the predictive
value of a 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 to 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.
© 2010 BioMed Central Ltd
Predicting progression of Alzheimer’s disease
Rachelle S Doody*1, Valory Pavlik1,2, Paul Massman1,3, Susan Rountree1, Eveleen Darby1, Wenyaw Chan4
RESEARCH Open Access
*Correspondence: rdoody@bcm.edu
1Alzheimer’s Disease and Memory Disorders Center, Baylor College of Medicine,
6501 Fannin Street, NB302, Houston, TX 77030, USA
Full list of author information is available at the end of the article
Doody et al. Alzheimer’s Research & Therapy 2010, 2:2
http://alzres.com/content/2/1/2
© 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.
assessment predict subsequent performance in specifi c
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
Th e Baylor Alzheimer’s Disease and Memory Disorders
Center sees self-referred, agency-referred, and physicianreferred
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 refl ecting more cognitive impairment.
Attention and concentration are assessed with the
Verbal Series Attention Test (VSAT) [8]. Th is test consists
of forward and reverse generation of arithmetic series,
verbal series (for example, months of the year), numberletter
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]. Th is score is derived from a patient interview and
mental status examination in conjunction with an
interview of a collateral source. Th e 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 aff airs,
home and hobbies, and personal care. Higher scores
refl ect 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]. Th e PSMS quantifi es
diffi culties 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. Th e 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 infl uence progression
in AD and routinely collected at the baseline visit are premorbid
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 modifi cation of the
motor scale of the Unifi ed 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
Th e 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 specifi c
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 preprogression
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 classifi ed as slow progressors, between a
two- to four-point decline as intermediate progressors,
and more than or equal to fi ve 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 diff erent from two
points per year because of test-retest variability).
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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.
Th e fi rst patient was enrolled in 1989, and accrual has
been ongoing since then. Th e AMNART was incorporated
in 1994. Th e 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 nonmissing
values on the adjustment covariates. We report
in the Results section the number of persons included in
each regression equation.
Statistical analysis
Th e study data are longitudinal, with fi xed 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 eff ects linear regression
models to estimate the relationship between the preprogression
categories and the rate of change in the
ADAS-Cog, VSAT Time, VSAT Errors, CDR Sum of
Boxes, PSMS and IADL scores [19]. Coeffi cients yielded
by this type of model refl ect 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. Th e random eff ect is time in years, and we used
a time by pre-progression rate interaction term to
indicate whether or not there is a diff erence in average
rate of decline (slope) associated with a patient’s initially
calculated pre-progression group. A signifi cant time by
pre-progression rate interaction term could represent
divergence among the groups in rates of change. We
examined each model for signifi cance of a quadratic term
and used non-linear interactions when the quadratic was
signifi cant (but report both the linear and non-linear
interactions in Table 1). Potential confounders or eff ect
modifi ers 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-tosevere
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 classifi ed as severe at baseline.
Table 1. Relationship between pre-progression category and subsequent rate of decline on cognitive and functional
measures
Progression measures
ADAS-Cog VSAT Time VSAT Errors CDR-SB IADL PSMS
(n = 552) (n = 589) (n = 589) (n = 596) (n = 573) (n = 575)
Independent Variables ¶ Beta P Beta P Beta P Beta P Beta P Beta P
Duration of Symptoms 1.352 <.001 7.405 <.001 -0.778 <.001 0.446 <.001 0.523 <.001 0.243 .015
Baseline Severity (mild vs. moderate/ severe) -10.052 <001 -61.158 <001 -7.886 <.001 -3.088 <.001 -3.204 <.001 -2.129 <.001
Years of Follow-up 3.323 <.001 20.335 <.001 3.033 <.001 2.084 <.001 3.309 <.001 2.430 <.001
Years of Follow-up Squared 0.514 .036 – NS – NS – NS -0.207 .003 – NS
Pre-progression Rate
Intermediate vs. Fast -4.032 .006 -20.351 .033 -3.046 .007 -1.399 .003 -1.915 .012 -0.442 .424
Slow vs. Fast -9.458 <.001 -49.417 <.001 -6.533 <.001 -2.593 <.001 -3.051 .001 -0.454 .520
Linear Interaction 1* – NS – NS – NS 0.247 .039 – NS – NS
Linear Interaction 2* – NS – NS – NS – NS – NS -1.133 <.001
Non-linear Interaction1* -0.807 .004 – NS – NS – NS – NS – NS
Non-linear Interaction 2* -0.554 .039 – NS – NS – NS – NS – NS
Model Intercept 56.601 617.164 62.203 10.364 14.96 4.243
* Interaction 1 = time by intermediate pre-progression group (fast = reference group); Interaction 2 = time by slow pre-progression group (fast = reference); Nonlinear
Interaction 1 = time squared by intermediate pre-progression group (fast = reference group); Non-linear interaction 2 = Time squared by slow pre-progression
group (fast = reference group).
¶ Models adjusted for age at diagnosis, sex, years of education, duration of symptoms at diagnosis, baseline severity (categorical), pre-morbid IQ, and presence of
hallucinations and/or delusions. If the quadratic term for follow-up time and the pre-progression group by quadratic time variable were not signifi cant, coeffi cients for
models with linear terms only are shown. Non-signifi cant (NS) betas for interaction terms omitted from table.
ADAS-cog = Alzheimer’s disease Assessment Scale-cognitive subscale; VSAT = Verbal Series Attention Test; CDR-SB = Clinical Dementia Rating Scale Sum of Boxes;
IADL = Instrumental Activities of Daily Living scale; PSMS = Physical Self-Maintenance Scale
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Covariates signifi cant at the P <0.10 level were included in
a fi nal model for each cognitive or functional outcome.
Our analysis included data for up to seven years of followup,
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
eff ect 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-specifi ed
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 inter mediate
or fast progressors, consistent with slow progression.
IQ and education were also higher in slow progressors.
Th e distribution of APO E epsilon 4 alleles did not diff er.
Signifi cant diff erences between the groups were taken
into account in the analysis.
Table 1 contains the mixed eff ects linear regression
coeffi cients associated with pre-progression categories
and the interaction of pre-progression categories with
time, after adjustment for the prospectively defi ned
covariates. Figures 1 to 6 display the fi tted regression
lines predicted by the regression model for each outcome.
Patients in both the slow and intermediated preprogression
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 signifi cant baseline diff erence 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
Table 2. Selected patient characteristics at baseline by preprogression category (n = 597)
Mean ± SD or n (Percent)
Variable Fast (N = 124) Intermediate (n = 274) Slow (n = 199) P*
Age at Diagnosis (years) 74.0 ± 8.7 73.6 ± 8.8 72.9± 8.2 .516
Sex (% female) 72.6 68.3 58.3 .016
Race/Ethnic Group (% white) 90.3 91.2 90.9 .957
Years of Education 13.0± 3.1 13.7± 3.1 14.4± 3.4 <.001
Estimated duration of disease before diagnosis (yrs) 1.7± 0.9 3.4± 1.6 4.9± 2.6 <.001
Baseline MMSE 18.1± 5.0 20.3± 4.4 24.7± 3.8 <.001
First AMNART (estimated IQ) 105.5± 9.8 106.3± 10.2 110.7± 9.6 <.001
Baseline MMSE 18.1±5.0 20.3±4.4 24.7±3.8 <.001
Hallucinations (% yes at or before Baseline) 21.0 21.9 12.6 .027
Delusions (% yes at or before Baseline) 40.32 46.0 34.2 .035
Parkinsonian Symptoms at Baseline 6.5 4.4 3.0 .147
Number of APOE e4 Alleles (% in each group) .573
0 22.2 47.3 30.6
1 19.4 46.2 34.4
2 20.0 40.0 40.0
ADAS Cog 27.4±12.0 24.9±11.0 17.6±8.4 <.001
CDR Sum of Boxes 6.7±3.9 6.0±3.6 4.0±2.8 <.001
PSMS 7.7±2.5 7.7±2.7 7.2±2.2 .177
IADL 16.0±6.8 15.2±6.3 13.3±5.5 .002
VSAT (time) 250.2±91.6 229.15±87.6 184.6±73.7 <.001
VSAT (errors) 18.3±11.8 15.0±9.9 9.5±8.1 <.001
*P -values based on one-way analysis of variance for continuous variables or Chi square test for categorical variables
MMSE = Mini Mental Status Exam; AMNART = American version of the New Adult Reading Test; ADAS-cog = Alzheimer’s disease Assessment Scale-cognitive subscale;
CDR = Clinical Dementia Rating Scale; PSMS = Physical Self-Maintenance Scale; IADL = Instrumental Activities of Daily Living scale; VSAT = Verbal Series Attention Test
Doody et al. Alzheimer’s Research & Therapy 2010, 2:2
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Figure 1. Fitted regression lines for ADAScog by pre-progression
category calculated from model coeffi cients shown in Table 1.
0 60 80
ADAS-Cog
20 40
A
0 2 Follow-u4p Years 6 8
Fast Intermediate
Slow
Figure 2. Fitted regression lines for VSAT time by
pre-progression category calculated from model coeffi cients
shown in Table 1.
300 350 400
VSAT Time
200 250
V
0 2 4 6 8
Follow-up Years
Fast Intermediate
Slow
Figure 3. Fitted regression lines for VSAT errors by
pre-progression category calculated from model coeffi cients
shown in Table 1.
30 40
s
20
VSAT Errors
10
0 2 4 6 8
Follow-up Years
Fast Intermediate
Slow
Figure 4. Fitted regression lines for CDR-SB by pre-progression
category calculated from model coeffi cients shown in Table 1.
20 25
oxes
15
R Sum of Bo
5 10
CD
0 2 Follow-u4p Years 6 8
Fast Intermediate
Slow
Figure 5. Fitted regression lines for IADL by pre-progression
category calculated from model coeffi cients shown in Table 1.
20 25 30
IADL
15
0 2 4 6 8
Follow Up Years
Fast Intermediate
Slow
Figure 6. Fitted regression lines for PSMS by pre-progression
category calculated from model coeffi cients shown in Table 1.
15 20 25
MS Score
10
PS
5
0 2 Follow-u4p Years 6 8
Fast Intermediate
Slow
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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). Th is
relative diff erence between the slow and fast progressors
was maintained (no signifi cant 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). Th is tendency of the intermediate
group to speed up on the CDR-SB was probably not
accounted for by functional defi cits, since this did not
occur on the IADL measure (Table 1 and Figure 5). Basic
activities of daily living assessed by PSMS were not
diff erent at baseline and did not begin to diverge until the
fi rst 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
relation ship of the pre-specifi ed covariates to each outcome.
Not unexpectedly, age was related to cognitive
scores, and sex to performance of complex ADLs. Premorbid
IQ (AMNART score) was related to the cognitive
measures. Education did not remain a signifi cant predictor
of progression on any measure in the presence of the
AMNART, consistent with our previous fi ndings [16].
Th e 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 fi nd 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 fi rst visit to death was 5.5 ± 2.7
years (median = 5.0 years). Th e 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). Th e results of Cox proportional hazards
modeling indicated that slow progressors had signifi cantly
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 diff erence between
the intermediate and fast progressors was not statistically
signifi cant (HR = 0.81 95% CI = 0.59 to 1.15, P = 0.24). Our
study may have been underpowered to detect the small
diff erence in survival between these two groups.
Discussion
We have demonstrated in a large cohort of probable
Alzheimer’s disease patients that a simple, calculated,
Table 3. Eff ect of covariates: betas (P-values) for signifi cant covariates*
Covariates
Sex
Progression (1 = male, Extra-pyramidal APOE
Measures Age 0 = female) Education AMNART Delusions Hallucinations Signs Genotype
ADAS-Cog -0.962 (.067) NS 0.291 (.055) -0.229 (<.001) 2.914 (.001) NS NS NS
VSAT Time -1.493 (<.001) NS NS -2.339 (.001) NS NS NS NS
VSAT Errors -0.179 (<.001) NS NS -0.272 (<.001) NS NS NS NS
SCDR NS NS NS NS 1.386 (<.001) 1.245 (.003) NS NS
IADL NS -2.109 (<.001) NS NS 2.762 (<.001) 1.619 (.008) NS NS
PSMS 0.037 (.055) NS NS NS 1.509 (<.001) 1.945 (.009) NS NS
*Betas calculated in models adjusted for baseline severity, duration, pre-progression rate x time, pre-progression x time squared (if applicable), and other covariates
that achieved the selection criterion of P <0.10. NS means the covariate did not achieve the criterion of P <.10, or did not retain this signifi cance level when included in
the full model.
ADAS- Cog = Alzheimer’s disease Assessment Scale-Cognitive Subscale; VSAT = Verbal Series Attention Test; CDR-SB = Clinical Dementia Rating Scale Sum of Boxes;
IADL = Instrumental Activities of Daily Living scale; PSMS = Physical Self-Maintenance Scale; AMNART = American version of the New Adult Reading Test
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).
1.00
Survivor functions
adjusted for age, sex, severity
0.00 0.25 0.50 0.75
0 2 4 6 8
analysis time
Slow Intermediate
Fast
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progression rate at the initial clinic visit is predictive of
longitudinal performance on multiple cognitive and
func tional measures over time. Th ese 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. Th e clearest and best maintained
diff erences were observed between the slow progressors
and those classifi ed 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
eff ects regression modeling showed that, in eff ect, 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 diff erences on the VSAT,
CDR-SB and IADL. Even though they did not diff er 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 diff erentiated at baseline and sped up over time
on a global measure, the CDR sum of the boxes score,
and they were not diff erentiated at any time on the basic
ADL (PSMS). Th e survival diff erences between inter mediate
and fast progressors were not signifi cantly diff erent,
but our study may have been underpowered to detect a
small diff erence. Our results suggest that prognos tications
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
suffi ciently to aff ect 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]. Th e 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
modifi ed, 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 diff erent
approach [21]. Th is eff ect of treatment persistence is
signifi cant in our mixed eff ects models which also include
the pre-progression rate, indicating that treatment may
provide benefi t 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 eff ect
diff ers by pre-progression category.
Many investigators seek to validate biomarkers of disease
progression, such as changes in hippocampal volume and
serum and cerebrospinal fl uid (CSF) biomarkers. Th e
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 fi ndings 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. Preprogression
rates are not assessed – yet imbalances
across the treatment groups in this important variable
could obscure true treatment diff erences, or could create
apparent diff erences when there is no drug eff ect,
especially in long duration clinical trials. Further, if our
hypothesis that the persistency of anti-dementia drug
treatment alters progression is correct, baseline diff erences
in cumulative duration of drug use could create
similar imbalances. Future clinical trials may benefi t 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 antidementia
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 multisite
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,
Doody et al. Alzheimer’s Research & Therapy 2010, 2:2
http://alzres.com/content/2/1/2
Page 7 of 9
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 infl uence rates of
progression and survival in AD, including age, sex,
education, premorbid IQ, hallucinations, delusions and
extrapyramidal features. Th e 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. Th e importance of our fi ndings is
strengthened by the fact that the current data are
internally consistent across multiple measures; progression
groups maintained their diff erences on measures
that included cognition, global performance, and
activities of daily living. Th e fact that survival data were
available for every subject and that survival time also
diff erentiated the slow and fast progressors provides
additional evidence for the clinical utility of the preprogression
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 fi nd real treatment diff erences, and could
reduce the duration of trials designed to assess diseasemodifying
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 fl uid; 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 fi nal 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.
Author details
1Alzheimer’s Disease and Memory Disorders Center, Baylor College of
Medicine, 6501 Fannin Street, NB302, Houston, TX 77030, USA. 2Division of
Family Medicine, Baylor College of Medicine, 3701 Kirby Drive, Houston,
TX 77098, USA. 3Department of Psychology, University of Houston, 126 Heyne
Building, Houston, TX 77204-5022, USA. 4Department of Epidemiology and
Biostatistics, University of Texas Health Sciences Center, 7703 Floyd Curl Drive,
San Antonio, TX 78229-3900, USA
Received: 27 Apr 2009 Revisions requested: 21 Oct 2009
Revisions received: 03 Dec 2009 Accepted: 23 Sep 2010
Published: 23 Sep 2010
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Higher Frequency of Atrial Fibrillation Linked
to Colder Seasons and Air Temperature on the Day
of Ischemic Stroke Onset
Osvaldo Fustinoni, MD,* Gustavo Saposnik, MD, MSc, FAHA,†‡xjj{
Maria Martha Esnaola y Rojas, MD,# Susan G. Lakkis, PhD,** and
Luciano A. Sposato, MD, MBA, FAHA,††‡‡ on behalf of ReNACer Investigators
Background: Whether a seasonal variation of atrial fibrillation among acute ischemic
stroke (AIS) patients occurs is unknown. We studied the distribution of atrial fibrillation
across seasons and air temperatures in a cohort of AIS patients. Methods: We
selected 899 AIS patients from the Argentinean Stroke Registry (ReNACer), who
were admitted to 43 centers in the Province of Buenos Aires. We recorded the minimum
and maximum temperatures at local weather centers on the day and the city
where each stroke occurred. We used the goodness-of-fit c2 test to assess the distribution
of atrial fibrillation across seasons and air temperatures and the Pearson correlation
coefficient to assess the relationship between these variables.We developed
a regression model for testing the association between seasons and atrial fibrillation.
Results: We found a seasonal variation in the occurrence of atrial fibrillation, with
a peak in winter and a valley in summer (23.1% versus 14.0%, P,.001). The semester
comprised by autumn and winter was associated with atrial fibrillation (Pearson
P ,.001). Atrial fibrillation showed a nonhomogeneous distribution across ranges
of temperature (P , .001, goodness-of-fit test), with a peak between 5C and 9C,
and was associated with minimum (Pearson P 5 .042) and maximum (Pearson
P 5.002) air temperature. After adjusting for significant covariates, there was a 2-
fold risk of atrial fibrillation during autumn and winter. Conclusions: In this cohort
of AIS patients, atrial fibrillation showed a seasonal variation and a nonhomogeneous
distribution across air temperatures, with peaks in cold seasons and low temperatures
on the day of stroke onset. Key Words: Ischemic stroke—atrial
fibrillation—risk—season—temperature—weather.
2013 by National Stroke Association
From the *Cerebrovascular Diseases, Instituto de Neurociencias
Buenos Aires, Buenos Aires, Argentina; †Stroke Outcomes Research
Center, St Michael’s Hospital, Ontario, Canada; ‡Li Ka Shing Knowledge
Institute, St Michael’s Hospital, Ontario, Canada; xDepartment
of Medicine, University of Toronto, Ontario, Canada; jjDepartment
of Health Policy Management and Evaluation, University of Toronto,
Ontario, Canada; {Institute for Clinical Evaluative Sciences, Ontario,
Canada; #Department of Neurology, Hospital Dr Cesar Milstein, Buenos
Aires, Argentina; **Equipo Interdisciplinario para el Estudio del
Cambio Global, Pontificia Universidad Catolica Argentina, Puerto
Madero, Argentina; ††Stroke Center at the Institute of Neurosciences,
University Hospital, Favaloro Foundation, Buenos Aires, Argentina;
and ‡‡Vascular Research Unit, INECO Foundation, Buenos Aires,
Argentina.
Received February 7, 2013; revision received March 6, 2013;
accepted March 7, 2013.
Address correspondence to Luciano A. Sposato, MD, MBA, FAHA,
Stroke Center at the Institute of Neurosciences, University Hospital,
Favaloro Foundation and Vascular Research Unit, INECO Foundation,
Rosales 2616, Olivos, Buenos Aires, Argentina. E-mail:
lucianosposato@gmail.com.
1052-3057/$ – see front matter
2013 by National Stroke Association
http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2013.03.009
476 Journal of Stroke and Cerebrovascular