Email updates

Keep up to date with the latest news and content from Alzheimer's Research & Therapy and BioMed Central.

Open Access Research

A web-based normative calculator for the uniform data set (UDS) neuropsychological test battery

Steven D Shirk123, Meghan B Mitchell123, Lynn W Shaughnessy12345, Janet C Sherman12, Joseph J Locascio12, Sandra Weintraub6 and Alireza Atri123*

Author Affiliations

1 Department of Neurology, Massachusetts General Hospital Memory Disorders Unit, 15 Parkman Street, WACC 715, Boston, MA 02114, USA

2 Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA

3 Geriatric Research Education and Clinical Center, Edith Nourse Rogers Memorial Veterans Hospital, 200 Springs Road, Bedford, MA 01730, USA

4 Department of Psychiatry, Massachusetts Mental Health Center, 180 Morton Street, Jamaica Plain, MA 02130, USA

5 Department of Psychiatry, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA

6 Cognitive Neurology and Alzheimer's Disease Center, Northwestern University Feinberg School of Medicine, 320 E. Superior, Searle 11-467, Chicago, IL 60611, USA

For all author emails, please log on.

Alzheimer's Research & Therapy 2011, 3:32  doi:10.1186/alzrt94

Published: 11 November 2011

Abstract

Introduction

With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the neuropsychological tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets.

Methods

Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all neuropsychological tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model.

Results

For each neuropsychological test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output.

Conclusions

An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.

Keywords:
Alzheimer's disease; cognitive aging; MCI; memory; norms