It differs from the ADCS-CGIC used in AD trials mainly in its shorter length and its The CGIC rating is made on a 7-point Likert-type scale where change from. Characteristics and performance of a modified version of the ADCS-CGIC CIBIC+ in Alzheimer Disease Assessment Scale-cognitive, Activities of Daily Living. A mandate of the ADCS is to develop optimal assessment instruments for use in Living (ADL), and the Clinical Global Impression of Change Scale (CGIC).

Author: Moogujar Vudoktilar
Country: Laos
Language: English (Spanish)
Genre: Photos
Published (Last): 27 February 2016
Pages: 142
PDF File Size: 10.3 Mb
ePub File Size: 10.47 Mb
ISBN: 411-5-30766-711-2
Downloads: 30083
Price: Free* [*Free Regsitration Required]
Uploader: Vusida

We assessed feasibility for its use by determining whether or not: We used a generalized estimating equations approach for ordinal outcome data to test the effects of treatment, baseline characteristics and change in clinical measures on the MCI-CGIC over 12 months, and ordinal logistic regression to assess the association between MCI-CGIC and change in clinical measures at 6 months and 12 months.

Variables at screening or baseline that were associated with worse CGIC scores over 6 and 12 months included white race, greater years of education, worse depression, dementia severity rating, cognitive, and daily activities scores, and lower memory domain scores on a neuropsychological battery.

The effect size of the donepezil-placebo difference was similar to that of other outcomes at 12 months. Overall global change or severity scales ratings, or alternatively, activities of daily living scales, are adcs-ccgic co-primary outcomes in regulatory or registration trials for drugs for dementia 2. The co-primary, which compliments a significant difference between drug and placebo on a psychometric test, helps to determine the clinical significance of psychometric test differences for regulatory purposes.

It is the most frequently used example of a Clinician’s Interview-Based Impression of Change with caregiver’s input i. It is a severity scale that assesses the current state of a patient without reference to a prior state and does not rate change over time.

By comparison a CGIC requires a clinician’s specific judgment as to whether or not a patient has changed since the beginning of a trial. A rationale for the use of severity scales in longer-term trials is that clinicians may not be able to assess this change because they may not remember or be able to recreate from notes or records the patient’s baseline state scwle medical records at baseline may not be sufficient.

The ADCS MCI-CGIC was designed to provide a means to assess global change in an MCI clinical trial by providing a semi-structured format to allow clinicians to gather necessary clinical information from both adcs-vgic subject and informant in order to allow for an overall impression of clinical scqle. The goals of this investigation were to assess the feasibility of the use of this modified ADCS-CGIC in MCI clinical trials by assessing whether or not the CGIC distinguished a medication effect, whether or not baseline demographic or clinical characteristics predicted change, and whether or not there was an association between MCI-CGIC change and change in other clinical measures, to assess external or concurrent validity.

Briefly patients with MCI were randomized to donepezil, vitamin E, or placebo, and completed baseline assessments and were followed for up to three years 5. The primary outcome of the trial was time to the development of possible or probable AD. Cognitive-domain scores for memory the ADAS-cog immediate and delayed word-recall scores and the NYU immediate and delayed paragraph-recall scoresexecutive function the digits-backward test, Symbol Digit Modalities Test, and number-cancellation testlanguage the Boston Naming Afcs-cgic and category-fluency testand visuospatial skills the clock-drawing test were calculated in addition to an overall composite cognitive-function csale.

These domain and composite scores were calculated as the weighted sum of the individual standardized test scores standardized by dividing each score by the standard deviation of the baseline scores. Weights were calculated as the reciprocal of the sum of the correlation coefficients between the tests in each domain at baseline.

Maze tracing was not used in the domain score calculations. The measures were administered at 6-month intervals over three years except that the ADAS-cog and NTB were administered at 3 months as well; and the CADQoL was administered at 3 months and then at month intervals from baseline.


Written informed consent was obtained from all participants and study partners. Significant effects for vitamin E were observed on the NTB overall score at 6 months none of these corrected for adcs-cgix comparisons 5. The CGIC rating is adcs-cgkc on a 7-point Likert-type scale where change from baseline is rated as marked improvement 1moderate improvement 2minimal improvement 3 acale, no change 4minimal worsening 5moderate worsening 6marked worsening 7. At baseline, the clinician interviews the subject and informant about baseline status for later reference.

At baseline only, clinical information about the subject may be used, including medical history, physical and neurological examination, and other ratings done at screening. A third column provides space for notes. There are separate spaces for notes taken from the subject and informant interviews. At interval assessments, the subject is interviewed first, followed by the informant.


ADRC – CGIC page

To assess the rate of change in MCI-CGIC over 12 months and its association with each of the covariates of interest, the longitudinal analysis was done using a generalized estimating equations GEE approach, which accounts for within-subject correlation. Because very few change scores were at the extreme ratings of marked worsening, moderate worsening, marked improvement, or moderate improvement, we evaluated the rate of change of MCI-CGIC using two models.

The GEE method is suitable for the longitudinal analysis of both binary and ordinal outcomes. These models estimate odds ratios that indicate the relationship between the response variable and the covariates.

GEE models for binary data assuming a logistic function and ordinal data using a proportional odds model were used for the two models. The proportional odds model is very similar to the GEE model for binary data, with the difference being that a covariate effect leads to an increase in the likelihood of the patient being in any subsequent higher MCI-CGIC category.

To determine if the change in MCI-CGIC over 12 months differed between patients randomized to donepezil, vitamin E, or placebo, a GEE model for ordinal adcs-gic was fit to the data to account for the clustering due to the repeated observations within a patient. The dependent variable in the model was the MCI-CGIC scores over time 6 months, 12 months and the independent variables included a treatment factor placebo, vitamin E and donepezila time factor 6 and 12 monthsand the treatment by time interaction.

Hochberg’s method was used to adjust for multiple comparisons 13 if the overall F test for the interaction effect was significant. The dependent variable in the model was the MCI-CGIC scores over 12 months and the independent variables included change in secondary measures as a time varying covariatea time factor 6 and 12 monthsand the secondary measure by time interaction.

Missing data at 12 months was imputed using last observation carried forward LOCF. All models were adjusted for three pre-specified covariates, age, ApoE4 status and screening MMSE, similar to the model used in the primary MCI report 5 along with any additional observed confounders. No adjustments scalee multiple comparisons were made given the exploratory nature of the hypotheses.

Possible co-linearity between baseline MMSE and other baseline predictors were assessed using Spearman correlation coefficients. Results were similar whether the 3-category or 2-category model was used Table 2Figure 1. Because there might be differences in CGIC change based on baseline cognitive severity, the sample dcale split at the median ADAScog point into a higher scoring and lower scoring group.

The coefficients for the LOCF analysis had the same directions as those of adccs-cgic coefficients in the above analysis. The odds ratios were mostly similar and did not significantly change the conclusions of the above analysis.

The coefficients for the LOCF analysis had the same directions as those of the coefficients in the above analysis, with similar odds adcs-cguc.

Change in memory domain score i. Change in executive function domain score i. However, the coefficients for these analyses and the LOCF analysis had the same directions as those of the coefficients in the above adcs-cigc and with similar odds ratios. The coefficients for the LOCF analysis were similar to those of the coefficients in the above analysis. The difference in magnitude of the MCI-CGIC between donepezil or vitamin E treatment compared to placebo was similar to other secondary clinical outcomes – both significant and not – in a trial in which the medications did not show overall advantages compared to placebo.

The mean donepezil-placebo difference in CGIC of Nevertheless, the lack of significant outcomes compared to placebo limits the ability to compare these effect sizes.

Screening or baseline characteristics of patients, white ethnicity, higher education, and generally worse functioning on cognitive, ADLs, and depression measures, predicted worse CGIC at 6 and 12 months These predictors of worse CGIC have been identified previously as predictors of conversion to AD or more rapid cognitive decline 1415 In effect, the closer a participant was to AD the greater the likelihood for a change rating.

The MCI-CGIC also predicted changes in clinical ratings scales at 6 and 12 months, thus adfs-cgic evidence for its external or concurrent validity. In particular, increases in CDR-sb scores that reflect clinical progression of disease severity without reference to baseline functioning acds-cgic associated with increasing CGIC ratings, providing additional evidence for the validity of this CGIC rating approach in MCI.

The CGIC is measured in ordinal categories and can be considered ordered from improved to no change to worse. Although a common approach of analyzing CGIC in clinical trials has been to treat the variable as a continuous outcome, we chose to use the CGIC as an ordinal outcome and take advantage of the ordinal models that are available for such data such as the GEE rather than simply analyzing an ordinal outcome as a continuous outcome.

One advantage is that the logistic link function specification takes into account the ceiling and floor effects of the dependent variable, the CGIC, while the linear models do not This is particularly important when the dependent variable is skewed, or when different covariate groups are compared which have widely varying skewness of the dependent variables We used several different regression models to adcx-cgic the effect of the covariates on the CGIC at the 6 and 12 months assessments.


ADCS Instruments – ADCS

The GEE modeling framework was used to assess the impact of treatment hypothesis 1baseline covariates hypothesis 2 and change in clinical measures hypothesis 3 on the CGIC at 6 and 12 months based on the analyses of change patterns over these time intervals. Fixed-effects logistic regression models were used to test the significance adcs-gcic the change in clinical measures at a particular time point 6 or 12 months adcs-cgc the CGIC at that time. The results from the two modeling approaches had clinically similar effects, although the statistical significance differed.

There are two major reasons for this: We decided to use the GEE model because the scientific interest in this study was the estimation and inference scalee the regression parameters and not of the variance-covariance structure of the longitudinal data.

In addition, the time points in the study were fixed with wcale variability in terms of the timing of the measurements over time. The ease of interpretation of the odds ratio and the robustness of the model to variance-covariance misclassification makes the GEE an appropriate model for these particular analyses. Moreover, the scientific questions that the two models answer are different. The GEE approach models the longitudinal experience of the study patients and provides information scalee change over a period of time, while the fixed-effects model which models cross-sectional data does not.

Additionally, for hypothesis 3, not only does acs-cgic CGIC change over time, but the values of the predictors clinical variables arcs-cgic change over time. The GEE analyses describes the dynamic relationship between the two variables in time, while the fixed-effects LR adce-cgic describes the association between the two variables at one point in time i.

Although mean difference data was not provided, at six-months By comparison, in the present trial at 6 months, The authors of the six month trial suggest that the lack of treatment differences might be due to the impact of MCI primarily on a single domain memory within the CGIC. Further, they suggest that CGIC ratings are influenced by minimal impairments in other areas at baseline along with minimal adcs–cgic in the primary memory impairments associated with MCI over six months.

The results of the present analyses, using a larger sample size, are similar to the lack of adcs-cgix statistically significant differences on the CGIC-MCI reported 19 but suggest further adca-cgic there are various influences on a CGIC rating in MCI in addition to memory Thus, this study provides the first systematic evidence that CGIC ratings, at least in MCI patients, are based on more than assessments of memory.

Yet, these analyses also suggest that the CGIC, along with the other measures above, may be of more limited sensitivity and use for the more mildly cognitively-impaired Ads-cgic patients in clinical trials unless the memory domains are expanded. For the MCI amnestic subtype classification, an expanded memory domain would be appropriate.

Schneider, Raman, Reisberg, and Mr. Insel report that they have no relationships to disclose. Schmitt reports past clinical trial grant support from Pfizer. Morris reports that neither he nor his family own stock or have equity interest outside of mutual adcd-cgic or other externally directed accounts in any pharmaceutical or biotechnology company; that from July to the present he has participated or is currently participating in clinical trials of antidementia drugs sponsored by Elan, Eli Lilly and Company, and Wyeth; and from January to the present has served as a consultant or has received speaking honoraria from Bristol-Myers Squibb, Elan, GE Healthcare, Genworth, Janssen-Cilag, Merck, Myriad, Neurochem, Neuroptix, and Schering-Plough.

National Center for Biotechnology InformationU.

Alzheimer Dis Assoc Disord. Author manuscript; available in PMC Jul 1. ClarkMD, 5 John C. PetersenMD, 8 and Steven H. FerrisPhD 7. Morris 6 Washington University, St. Louis Find articles by John C. Author information Copyright and License information Disclaimer. Schneider, MD, Christopher M. Ferris, PhD, John C. The publisher’s final edited version of this article is available at Alzheimer Dis Assoc Disord.

See other articles in PMC that cite the published article. Methods We used a generalized estimating equations approach for ordinal outcome data arcs-cgic test the effects of treatment, baseline characteristics and acs-cgic in clinical measures on the MCI-CGIC over 12 months, and ordinal logistic regression to assess the association between MCI-CGIC and change in clinical measures at adcs-xgic months and 12 months.