Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study

Monsell, Sarah E.; Dodge, Hiroko H.; Zhou, Xiao‐Hua; Bu, Yunqi; Besser, Lilah M.; Mock, Charles; Hawes, Stephen E.; Kukull, Walter A.; Weıntraub, Sandra

doi:10.1097/wad.0000000000000111

Cited by 125 publications

(126 citation statements)

References 28 publications

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“…The neuropsychological (NP) test battery in UDS used in the current study (Version 2.0) was replaced by new tests in March of 2015 (Version 3) 29 as part of an effort for NACC to use non-proprietary cognitive tests (see detail: https://www.alz.washington.edu/WEB/researcher_home.html). Once data with Version 3 NP test battery are accumulated, we plan to construct an equivalent calculator based on the new battery.…”

Section: Discussionmentioning

confidence: 99%

Big Data Analytical Approaches to the NACC Dataset

Lin¹,

Gong²,

Yang³

et al. 2018

Alzheimer Disease &Amp; Associated Disorders

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Background Clinical trials increasingly aim to retard disease progression during pre-symptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive. Methods We used only non-invasive clinical variables collected in the National Alzheimer’s Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years. Results 31,872 unique subjects, 748 clinical variables and additional 128 derived variables in NACC data sets were used. 15 non-invasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval. Conclusions We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in pre-clinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.

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Section: Discussionmentioning

confidence: 99%

Big Data Analytical Approaches to the NACC Dataset

Lin¹,

Gong²,

Yang³

et al. 2018

Alzheimer Disease &Amp; Associated Disorders

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“…After a brief period of pilot data collection with the new instruments to refine the instructions and address any questions about administration and scoring we made additional revisions and conducted a larger pilot study (N=935) that compared the UDSNB 2.0 and 3.0 versions in individuals divided into 4 groups based on their Mini Mental State Examination (MMSE) 14 scores (26 to 30, 21 to 25, 16 to 20, 10 to 15) in a “crosswalk” study. 15 The pairs of scores for the original and corresponding replacement tests were compared using equipercentile equating, and the analyses provided a crosswalk of equivalent test scores between the original and replacement tests [eg, a score of 15 on the Montreal Cognitive Assessment (MoCA) is equivalent to a score of 21 on the MMSE]. The results of the crosswalk study provided good evidence for relatively reliable equivalence across both measures and that the chosen tests were reasonable replacements for the older tests.…”

Section: Methodsmentioning

confidence: 99%

Version 3 of the Alzheimer Disease Centers’ Neuropsychological Test Battery in the Uniform Data Set (UDS)

Weıntraub

Besser

Dodge

et al. 2018

Alzheimer Disease &Amp; Associated Disorders

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Introduction:The neuropsychological battery of the Uniform Data Set (UDSNB) was implemented in 2005 by the National Institute on Aging (NIA) Alzheimer Disease Centers program to measure cognitive performance in dementia and mild cognitive impairment due to Alzheimer Disease. This paper describes a revision, the UDSNB 3.0.Methods:The Neuropsychology Work Group of the NIA Clinical Task Force recommended revisions through a process of due diligence to address shortcomings of the original battery. The UDSNB 3.0 covers episodic memory, processing speed, executive function, language, and constructional ability. Data from 3602 cognitively normal participants in the National Alzheimer Coordinating Center database were analyzed.Results:Descriptive statistics are presented. Multivariable linear regression analyses demonstrated score differences by age, sex, and education and were also used to create a normative calculator available online.Discussion:The UDSNB 3.0 neuropsychological battery provides a valuable non proprietary resource for conducting research on cognitive aging and dementia.

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“…This approach has a number of benefits relative to mean or linear equating methods; for example, it results in all imputed scores falling within the actual range of the scale and does not rely on the assumption of a normal distribution of test scores. Equipercentile equating methods have been used to develop crosswalks for a number of neurocognitive and psychiatric rating scales (e.g., Choi, Schalet, Cook, & Cella, ; Monsell et al., ).…”

Section: Methodsmentioning

confidence: 99%

An Empirical Crosswalk for the PTSD Checklist: Translating DSM‐IV to DSM‐5 Using a Veteran Sample

Moshier

Lee

Bovin

et al. 2019

Journal of Traumatic Stress

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The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) introduced numerous revisions to the fourth edition's (DSM-IV) criteria for posttraumatic stress disorder (PTSD), posing a challenge to clinicians and researchers who wish to assess PTSD symptoms continuously over time. The aim of this study was to develop a crosswalk between the DSM-IV and DSM-5 versions of the PTSD Checklist (PCL), a widely used self-rated measure of PTSD symptom severity. Participants were 1,003 U.S. veterans (58.7% with PTSD) who completed the PCL for DSM-IV (the PCL-C) and DSM-5 (the PCL-5) during their participation in an ongoing longitudinal registry study. In a randomly selected training sample (n = 800), we used equipercentile equating with loglinear smoothing to compute a "crosswalk" between PCL-C and PCL-5 scores. We evaluated the correspondence between the crosswalk-determined predicted scores and observed PCL-5 scores in the remaining validation sample (n = 203). The results showed strong correspondence between crosswalkpredicted PCL-5 scores and observed PCL-5 scores in the validation sample, ICC = .96. Predicted PCL-5 scores performed comparably to observed PCL-5 scores when examining their agreement with PTSD diagnosis ascertained by clinical interview: predicted PCL-5, κ = 0.57; observed PCL-5, κ = 0.59. Subsample comparisons indicated that the crosswalk's accuracy did not differ across characteristics including gender, age, racial minority status, and PTSD status. The results support the validity of this newly developed PCL-C to PCL-5 crosswalk in a veteran sample, providing a tool with which to interpret and translate scores across the two measures.

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Results From the NACC Uniform Data Set Neuropsychological Battery Crosswalk Study

Cited by 125 publications

References 28 publications

Big Data Analytical Approaches to the NACC Dataset

Big Data Analytical Approaches to the NACC Dataset

Version 3 of the Alzheimer Disease Centers’ Neuropsychological Test Battery in the Uniform Data Set (UDS)

An Empirical Crosswalk for the PTSD Checklist: Translating DSM‐IV to DSM‐5 Using a Veteran Sample

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