Staging Disease Severity Using the Alzheimer’s Disease Composite Score (ADCOMS): A Retrospective Data Analysis

Monfared, Amir Abbas Tahami; Houghton, Katherine; Zhang, Quanwu; Mauskopf, Josephine

doi:10.1007/s40120-022-00326-y

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…ADCOMS scores for each subject at each assessment time point were calculated from the constituent item scores extracted from several ADNI files (listed in the supplement preamble). The method for calculating ADCOMS is described comprehensively elsewhere 1,14 ; however, Figure 1 shows ADCOMS’ maximum contributions from each of the item responses, weighted by the relevant coefficient. All items have a cognitive or functional focus.…”

Section: Methodsmentioning

confidence: 99%

“…ADCOMS scores for each subject at each assessment time point were calculated from the constituent item scores extracted from several ADNI files (listed in the supplement preamble). The method for calculating ADCOMS is described comprehensively elsewhere 1,14 ; however, Figure 1 The weighted sum of CN item scores will be less than for those with MCI, and only the most severe dementia cases will have the highest ADCOMS score. In its final form, the ADCOMS scale extends from 0 up to 1.97 as cognitive and functional severity increases.…”

Section: Adcoms Score Componentsmentioning

confidence: 99%

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ADCOMS sensitivity versus baseline diagnosis and progression phenotypes

Evenden,

Prosser,

Michopoulou

et al. 2024

Alz & Dem Diag Ass & Dis Mo

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BACKGROUNDThe Alzheimer's Disease COMposite Score (ADCOMS) is more sensitive in clinical trials than conventional measures when assessing pre‐dementia. This study compares ADCOMS trajectories using clustered progression characteristics to better understand different patterns of decline.METHODSPost‐baseline ADCOMS values were analyzed for sensitivity using mean‐to‐standard deviation ratio (MSDR), partitioned by baseline diagnosis, comparing with the original scales upon which ADCOMS is based. Because baseline diagnosis was not a particularly reliable predictor of progression, individuals were also grouped into similar ADCOMS progression trajectories using clustering methods and the MSDR compared for each progression group.RESULTSADCOMS demonstrated increased sensitivity for clinically important progression groups. ADCOMS did not show statistically significant sensitivity or clinical relevance for the less‐severe baseline diagnoses and marginal progression groups.CONCLUSIONSThis analysis complements and extends previous work validating the sensitivity of ADCOMS. The large data set permitted evaluation–in a novel approach–by the clustered progression group.

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Section: Methodsmentioning

confidence: 99%

“…ADCOMS scores for each subject at each assessment time point were calculated from the constituent item scores extracted from several ADNI files (listed in the supplement preamble). The method for calculating ADCOMS is described comprehensively elsewhere 1,14 ; however, Figure 1 The weighted sum of CN item scores will be less than for those with MCI, and only the most severe dementia cases will have the highest ADCOMS score. In its final form, the ADCOMS scale extends from 0 up to 1.97 as cognitive and functional severity increases.…”

Section: Adcoms Score Componentsmentioning

confidence: 99%

ADCOMS sensitivity versus baseline diagnosis and progression phenotypes

Evenden,

Prosser,

Michopoulou

et al. 2024

Alz & Dem Diag Ass & Dis Mo

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“…ADCOMS ADCOMS (range 0-1.97; higher scores indicate more severe impairment) is a statistically derived composite measure which consists of 12 clinically sensitive items from other clinical scales; the 6 CDR domains, 4 items from ADAS-Cog, and 2 items from the Mini-Mental State Examination (MMSE) [7,15]. This composite consists of cognitive and functional components of variable statistical weights [7]; scores typically range from 0.11 to < 0.31 in MCI and 0.31 to 0.77 in mild AD dementia [16]. Lecanemab slowed the worsening (i.e., increase) in baseline ADCOMS scores by 0.05 points (p < 0.001) at 18 months compared to placebo, reflecting 24% slowing of progression (Table 1).…”

Section: Adas-cog14mentioning

confidence: 99%

The search for clarity regarding “clinically meaningful outcomes” in Alzheimer disease clinical trials: CLARITY-AD and Beyond

Tarawneh,

Pankratz

2024

Alz Res Therapy

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CLARITY-AD is an 18-month, double-blinded, placebo-controlled, phase 3 trial which examined the safety and efficacy of the anti-amyloid agent, lecanemab, in mild cognitive impairment and mild dementia due to Alzheimer disease (AD). Lecanemab effectively reduced mean brain amyloid burden and was associated with statistically significant favorable effects, reflected by moderately less decline in the primary and secondary clinical outcomes, at 18 months compared to placebo. However, there is controversy within the AD community regarding the clinical significance of these results and whether they translate into clinically meaningful and tangible benefits on cognition or daily functions.We here review the primary and secondary clinical outcomes of CLARITY-AD and present our interpretation of the potential clinical meaningfulness of the group-level differences in study outcomes in the context of the 18-month study duration. We propose that the validation of stage-appropriate group-level thresholds for clinical meaningfulness of AD trial outcomes in biologically confirmed cohorts will allow objective interpretation of trial results and guide clinical decision-making. Further, in accordance with FDA guidance which emphasizes patient-focused drug development, the contextualization of AD clinical trial outcomes can be facilitated by supplementary individual-level data analyses which measure the risk of disease progression or summarize intraindividual change, using prespecified thresholds of clinically meaningful change, in each of the study groups over the trial period. The concepts of “time-saved” and “time-based” slowing in disease progression can be used to communicate clinical outcomes associated with emerging disease-modifying AD therapies to various stakeholders. We also describe several factors that need to be considered when evaluating outcomes of emerging AD therapies, including disease stage, the neuropathologic complexity of AD, time-based effects of disease-modifying therapies, and the possible influence of individual factors on treatment response and/or risk for adverse events. The consideration of these factors in the design and reporting of future trials of emerging AD therapies will guide clinicians regarding their appropriateness for use in various patient populations.Finally, we emphasize that data from clinical cohorts with longer durations of treatment and follow-up, including extension studies and patient registries, is needed to evaluate the long-term safety and efficacy of lecanemab in early symptomatic AD.

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“…Numerical representation of the differences between AD stages. Monfared et al [81] calculated the range of Alzheimer's disease composite scores to assess the severity of the cognitive decline in patients. Sheng et al [82] made multiple classifications and concluded that the gap between late mild cognitive impairment and early mild cognitive impairment was small, whereas a greater difference exists between early and late MCI patients.…”

Section: Challenges In Researchmentioning

confidence: 99%

A Survey of Deep Learning for Alzheimer’s Disease

Zhou

Wang

et al. 2023

MAKE

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Alzheimer’s and related diseases are significant health issues of this era. The interdisciplinary use of deep learning in this field has shown great promise and gathered considerable interest. This paper surveys deep learning literature related to Alzheimer’s disease, mild cognitive impairment, and related diseases from 2010 to early 2023. We identify the major types of unsupervised, supervised, and semi-supervised methods developed for various tasks in this field, including the most recent developments, such as the application of recurrent neural networks, graph-neural networks, and generative models. We also provide a summary of data sources, data processing, training protocols, and evaluation methods as a guide for future deep learning research into Alzheimer’s disease. Although deep learning has shown promising performance across various studies and tasks, it is limited by interpretation and generalization challenges. The survey also provides a brief insight into these challenges and the possible pathways for future studies.

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Staging Disease Severity Using the Alzheimer’s Disease Composite Score (ADCOMS): A Retrospective Data Analysis

Abstract: Introduction: The Alzheimer's disease (AD) composite score (ADCOMS) has been shown to be a more sensitive measure of cognitive change in early AD (i.e., mild cognitive impairment [MCI] and mild AD) than commonly used measures. This study derived ADCOMS value

Cited by 6 publications

References 33 publications

ADCOMS sensitivity versus baseline diagnosis and progression phenotypes

ADCOMS sensitivity versus baseline diagnosis and progression phenotypes

The search for clarity regarding “clinically meaningful outcomes” in Alzheimer disease clinical trials: CLARITY-AD and Beyond

A Survey of Deep Learning for Alzheimer’s Disease

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