Improved design of prodromal Alzheimer's disease trials through cohort enrichment and surrogate endpoints

Cohort identification of axial spondyloarthritis in a large healthcare dataset: current and future methods

BACKGROUND

Big data research is important for studying uncommon diseases in real-world settings. Most big data studies in axial spondyloarthritis (axSpA) have been limited to populations identified with billing codes for ankylosing spondylitis (AS). axSpA is a more inclusive concept, and reliance on AS codes does not produce a comprehensive axSpA study population. The first objective was to describe our process for establishing an appropriate sample of patients with and without axSpA for developing accurate axSpA identification methods. The second objective was to determine the classification performance of AS billing codes against the chart-reviewed axSpA reference standard.

METHODS

Veteran Health Affairs clinical and administrative data, between January 2005 and June 2015, were used to randomly select patients with clinical phenotypes that represented high, moderate, and low likelihoods of an axSpA diagnosis. With chart review, the sampled patients were classified as Yes axSpA, No axSpA or Uncertain axSpA, and these classification assignments were used as the reference standard for determining the positive predictive value (PPV) and sensitivity of AS ICD-9 codes for axSpA.

RESULTS

Six hundred patients were classified as Yes axSpA (26.8%), No axSpA (68.3%), or Uncertain axSpA (4.8%). The PPV and sensitivity of an AS ICD-9 code for axSpA were 83.3% and 57.3%, respectively.

CONCLUSIONS

Standard methods of identifying axSpA patients in a large dataset lacked sensitivity. An appropriate sample of patients with and without axSpA was established and characterized for developing novel axSpA identification methods that are anticipated to enable previously impractical big data research.

Neurofibrillary Tangle Stage and the Rate of Progression of Alzheimer Symptoms: Modeling Using an Autopsy Cohort and Application to Clinical Trial Design

Importance

The heterogeneity of rate of clinical progression among patients with Alzheimer disease leads to difficulty in providing clinical counseling and diminishes the power of clinical trials using disease-modifying agents.

Objective

To gain a better understanding of the factors that affect the natural history of progression in Alzheimer disease for the purpose of improving both clinical care and clinical trial design.

Design, Setting, and Participants

A longitudinal cohort study of aging from 2005 to 2014 in the National Alzheimer Coordinating Center. Clinical evaluation of the participants was conducted in 31 National Institute on Aging's Alzheimer Disease Centers. Nine hundred eighty-four participants in the National Alzheimer Coordinating Center cohort study who died and underwent autopsy and met inclusion and exclusion criteria.

Main Outcomes and Measures

We sought to model the possibility that knowledge of neurofibrillary tangle burden in the presence of moderate or frequent plaques would add to the ability to predict clinical rate of progression during the ensuing 2 to 3 years. We examined the National Alzheimer Coordinating Center autopsy data to evaluate the effect of different neurofibrillary tangle stages on the rates of progression on several standard clinical instruments: the Clinical Dementia Rating Scale sum of boxes, a verbal memory test (logical memory), and a controlled oral word association task (vegetable naming), implementing a reverse-time longitudinal modeling approach in conjunction with latent class estimation to adjust for unmeasured sources of heterogeneity.

Results

Several correlations between clinical variables and neurocognitive performance suggest a basis for heterogeneity: Higher education level was associated with lower Clinical Dementia Rating Scale sum of boxes (β = -0.19; P < .001), and frequent vs moderate neuritic plaques were associated with higher Clinical Dementia Rating Scale sum of boxes (β = 1.64; P < .001) and lower logical memory score (β = -1.07; P = .005). The rate of change of the clinical and cognitive scores varied depending on Braak stage, when adjusting for plaques, age of death, sex, education, and APOE genotype. For example, comparing high vs low Braak stage with other variables fixed, the logical memory score decreased a substantial 0.38 additional units per year (95% CI, -0.70 to -0.06; P = .02). Using these data, we estimate that a 300-participant clinical trial with end point of a 20% improvement in slope in rate of change of Clinical Dementia Rating Scale sum of boxes has 89% power when all participants in the trial are from the high Braak stage, compared with 29% power if Braak stage had not used for eligibility.

Conclusions and Relevance

We found that knowledge of neurofibrillary tangle stage, modeled as the sort of information that could be available from tau positron-emission tomography scans and its use to determine eligibility to a trial, could dramatically improve the power of clinical trials and equivalently reduce the required sample sizes of clinical trials.

Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration

OBJECTIVE

To investigate the effect of enriching mild cognitive impairment (MCI) clinical trials using combined markers of amyloid pathology and neurodegeneration.

METHODS

We evaluate an implementation of the recent National Institute for Aging-Alzheimer's Association (NIA-AA) diagnostic criteria for MCI due to Alzheimer disease (AD) as inclusion criteria in clinical trials and assess the effect of enrichment with amyloid (A+), neurodegeneration (N+), and their combination (A+N+) on the rate of clinical progression, required sample sizes, and estimates of trial time and cost.

RESULTS

Enrichment based on an individual marker (A+ or N+) substantially improves all assessed trial characteristics. Combined enrichment (A+N+) further improves these results with a reduction in required sample sizes by 45% to 60%, depending on the endpoint.

CONCLUSIONS

Operationalizing the NIA-AA diagnostic criteria for clinical trial screening has the potential to substantially improve the statistical power of trials in MCI due to AD by identifying a more rapidly progressing patient population.

Validation of the Chinese version of Addenbrooke's cognitive examination-revised for screening mild Alzheimer's disease and mild cognitive impairment

BACKGROUND/AIMS

As a suitable test to screen for Alzheimer's disease (AD) or mild cognitive impairment (MCI), studies to validate the Chinese version of Addenbrooke's Cognitive Examination-Revised (ACE-R) are rare.

METHODS

A total of 151 subjects were recruited and the neuropsychological assessments were employed. One-way analysis of variance and Bonferroni correction were used to compare scores of different psychometric scales. Intraclass correlation coefficient (ICC) and Cronbach's coefficient α were used to evaluate the reliability of psychometric scales. The validity of ACE-R to screen for mild AD and amnestic subtype of MCI (a-MCI) was assessed by receiver operating characteristic (ROC) curves.

RESULTS

The Chinese ACE-R had good reliability (inter-rater ICC = 0.994; test-retest ICC = 0.967) as well as reliable internal consistency (Cronbach's coefficient α = 0.859). With its cutoff of 67/68, the sensitivity (0.920) and specificity (0.857) were lower than for the Mini-Mental State Examination (MMSE) cutoff (sensitivity 1.000 and specificity 0.937) to screen for mild AD. However, the sensitivity of ACE-R to screen for a-MCI was superior to the MMSE with a cutoff of 85/86. The specificity of ACE-R was lower than that of the MMSE to screen for a-MCI. The area under the ROC curve of ACE-R was much larger than that of the MMSE (0.836 and 0.751) for detecting a-MCI rather than mild AD.

CONCLUSION

The Chinese ACE-R is a reliable assessment tool for cognitive impairment. It is more sensitive and accurate in screening for a-MCI rather than for AD compared to the MMSE.