Analysis of Data Reports in Published Journal Articles

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: [email protected] 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). Doody et al. Alzheimer’s Research & Therapy 2010, 2:2 http://alzres.com/content/2/1/2 Page 2 of 9 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 Doody et al. Alzheimer’s Research & Therapy 2010, 2:2 http://alzres.com/content/2/1/2 Page 3 of 9 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 http://alzres.com/content/2/1/2 Page 4 of 9 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 Doody et al. Alzheimer’s Research & Therapy 2010, 2:2 http://alzres.com/content/2/1/2 Page 5 of 9 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 Doody et al. Alzheimer’s Research & Therapy 2010, 2:2 http://alzres.com/content/2/1/2 Page 6 of 9 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 References 1. Doody R, Massman P, Dunn J: A method for estimating progression rates in Alzheimer disease. Arch Neurol 2001, 58:449-454. 2. Doody R, Dunn J, Huang E, Azher S, Kataki M: A method for estimating duration of illness in Alzheimer’s disease. Dement Geriatr Cogn Disord 2004, 17:1-4. 3. Doody R: We should not distinguish between symptomatic and diseasemodifying treatments in Alzheimer’s disease drug development. Alzheimers Dement 2008, 4:S21-25. 4. Rountree S, Waring S, Chan W, Lupo P, Darby E, Doody R: Importance of subtle amnestic and nonamnestic defi cits in mild cognitive impairment: prognosis and conversion to dementia. Dement Geriatr Cogn Disord 2007, 24:476-482. 5. Doody R, Pavlik V, Massman P, Kenan M, Yeh S, Powell S, Cooke N, Dyer C, Demirovic J, Waring S, Chan W: Changing patient characteristics and survival experience in an Alzheimer’s center patient cohort. Dement Geriatr Cogn Disord 2005, 20:198-208. 6. Folstein M, Folstein S, McHugh P: “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975, 12:189-198. 7. Rosen W, Mohs R, Davis K: A new rating scale for Alzheimer’s disease. Am J Psychiatry 1984, 141:1356-1364. 8. Mahurin RK; Cooke N: Verbal Series Attention Test: Clinical utility in the assessment of dementia. The Clinical Neuropsychologist 1996, 10:43-53. 9. Hughes C, Berg L, Danziger W, Coben L, Martin R: A new clinical scale for the staging of dementia. Br J Psychiatry 1982, 140:566-572. 10. Morris J: The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993, 43:2412-2414. 11. Lawton M, Brody E: Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 1969, 9:179-186. 12. Nelson HE, O’Connell A: Dementia: the estimation of premorbid intelligence levels using the New Adult Reading Test. Cortex 1978, 14:234-244. 13. Grober E, Sliwinski M: Development and validation of a model for estimating premorbid verbal intelligence in the elderly. J Clin Exp Neuropsychol 1991, 13:933-949. 14. Stern Y, Albert M, Brandt J, Jacobs D, Tang M, Marder K, Bell K, Sano M, Devanand D, Bylsma F: Utility of extrapyramidal signs and psychosis as predictors of cognitive and functional decline, nursing home admission, and death in Alzheimer’s disease: prospective analyses from the Predictors Study. Neurology 1994, 44:2300-2307. 15. Chui H, Lyness S, Sobel E, Schneider L: Extrapyramidal signs and psychiatric symptoms predict faster cognitive decline in Alzheimer’s disease. Arch Neurol 1994, 51:676-681. 16. Pavlik V, Doody R, Massman P, Chan W: Infl uence of premorbid IQ and education on progression of Alzheimer’s disease. Dement Geriatr Cogn Doody et al. Alzheimer’s Research & Therapy 2010, 2:2 http://alzres.com/content/2/1/2 Page 8 of 9 Disord 2006, 22:367-377. 17. Clark C, Ewbank D, Lerner A, Doody R, Henderson V, Panisset M, Morris J, Fillenbaum G, Heyman A: The relationship between extrapyramidal signs and cognitive performance in patients with Alzheimer’s disease enrolled in the CERAD Study. Consortium to Establish a Registry for Alzheimer’s Disease. Neurology 1997, 49:70-75. 18. 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. 19. Laird N, Ware J: Random-eff ects models for longitudinal data. Biometrics 1982, 38:963-974. 20. Rountree SD, Chan W, Pavlik V, Darby E, Siddiqui S, Doody R: Persistent treatment with cholinesterase inhibitors and memantine slows clinical progression of Alzheimer’s disease (AD). Alzheimer’s Research and Therapy, 2009, 1:7 doi:101186/alzrt7. 21. Atri A, Shaughnessy L, Locascio J, Growdon J: Long-term course and eff ectiveness of combination therapy in Alzheimer disease. Alzheimer Dis Assoc Disord, 22:209-221. doi:10.1186/alzrt25 Cite this article as: Doody RS, et al.: Predicting progression of Alzheimer’s disease. Alzheimer’s Research & Therapy 2010, 2:2. Doody et al. Alzheimer’s Research & Therapy 2010, 2:2 http://alzres.com/content/2/1/2 Page 9 of 9 BioMed Central publishes under the Creative Commons Attribution License (CCAL). Under the CCAL, authors retain copyright to the article but users are allowed to download, reprint, distribute and /or copy articles in BioMed Central journals, as long as the original work is properly cited. 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: [email protected]. 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