Science

HIGH PRECISION OF THE MCI SCREEN

High Accuracy in All Stages of Memory Impairment

The MCI Screen has high overall accuracy, sensitivity and specificity in distinguishing normal aging from all stages of memory impairment from Normal to MCI to Dementia.

Overall Accuracy Sensitivity Specificity MCI vs. Normal 97% 95% 88% MCI/Mild Dementia vs. Normal 98% 97% 88% Mild Dementia vs. Normal 99% 96% 99% Proceedings of National Academy of Science. 2005; 102(13):4919-24.

More Accurate than Commonly Used Assessments

The MCI Screen has much higher accuracy compared to commonly used assessments in physicians’ offices. In fact, no other instrument in the published literature can reliably distinguish MCI from Normal, whereas the MCI Screen does so with 97% accuracy.

Overall Accuracy Sensitivity Specificity MCI Screen 96% 94% 97% Mini Mental State Exam 62% 71% 36% Clock Drawing 54% 59% 39% (Journal of Alzheimer's Disease. 2007; 11(3):323-335. WHY IS PRECISION SO IMPORTANT?)

Better Traffic Control Needed in Primary Care Settings

The aging public is concerned about cognitive health. To ensure timely intervention against all causes of impairment, without over-utilization of diagnostic tests, physicians need a simple but accurate tool for assessing cognition in primary care. With such a tool, physicians can prescribe further diagnostic evaluation for those with an underlying medical condition, without overutilizing healthcare resources on the worried-well.

Currently AD Is Detected Too Late

Over 2/3 of patients with Alzheimer’s disease are detected in the mild to moderate dementia stage when treatment efficacy is marginal. This is partly due to the lack of assessment tools that can identify more subtle cognitive impairment at the MCI stage.

Predicting clinical decline over 36 months with a memory-based digital biomarker

Davide Bruno, Ainara Jauregi Zinkunegi, Jason R Bock

First published: 25 December 2023

Abstract

Background

Word-list recall tests are routinely employed in clinical practice for dementia screening. Process scoring and latent modeling of these tests, where underlying neurocognitive mechanisms are exploited, have shown potential to enhance test accuracy without requiring test redesign. We examined Embic’s digital biomarker M, a measure of recall derived from multinomial processing trees and hierarchical Bayesian computational methods, as a predictor of conversion to mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and of increase in Clinical Dementia Rating (CDR) score.

Methods

Secondary data analyses were carried out from ADNI data. M was computed from Rey’s AVLT data, and compared to standard AVLT clinical metrics (total and delayed recall). We conducted logistic regression analyses with diagnosis at 36 months (normal vs. MCI + AD) as outcome; all 169 participants were classified as normal at baseline, and 13 of these progressed to a clinical diagnosis (see Table 1). We controlled for baseline age, gender and years of education; and ran separate analyses with other metrics to avoid multicollinearity. We then repeated the same analytical framework with CDR scores (stable at 0 vs. increased to 0.5 or 1) at 36 months as outcome: stable participants were 141, and 24 increased (see Table 2). Covariates and predictors were the same as in the previous analysis.

Results

M predicted conversion to MCI or AD after three years (FDR-corrected p value = 0.048), while neither AVLT total nor delayed recall did. Performance metrics showed that M generated 92% accuracy, or probability of yielding a correct classification, and 100% specificity, or no false positives (Figure 1). Analogously, M, but not standard AVLT metrics, also predicted a CDR increase after three years (FDR-corrected p value = 0.003), with high accuracy (84%), specificity (96%), and precision (80%), or positive predictive value (Figure 2). Sensitivity analyses with ordinal regression broadly confirmed these findings.

Conclusions

These results indicate Embic’s digital biomarker M outperforms some of AVLT’s traditional metrics in identifying individuals who will progress to MCI and AD after three years, from a healthy baseline.

Detecting Cognitive Impairment in Primary Care: Performance Assessment of Three Screening Instruments

Douglas L. Trenkle, D.O.1 , William R. Shankle, M.D.2,3, Stanley P. Azen, Ph.D.4

Maine Coast Memorial Hospital, Ellsworth, Maine; 2 Medical Care Corporation; 3 Department of Cognitive Sciences, University of California, Irvine; 4 Department of Preventive Medicine, Keck School of Medicine, University of Southern California

Early detection of Alzheimerʼs disease and related disorders (ADRD) is important, especially in primary care settings. We compared performances of two common screening tests, the MiniMental State Exam (MMSE) and Clock Drawing Test (CDT), with that of the MCI Screen (MCIS) in 254 patients over 65. None had previous diagnosis of ADRD, and 81% were asymptomatic by Functional Assessment Staging Test (FAST) (FAST=1). 215 patients completed all screening tests - 141 had ≥1 abnormal result, 121/141 completed standardized diagnostic assessment, and the remaining 74/215 (34%) screened entirely normally and weren’t further evaluated. Potential bias due to unevaluated cases was statistically adjusted. Among diagnosed cases: AD=43%, cerebrovascular disease=36%, other causes=21%. Bias-adjusted MCI prevalence for FAST stages 1 and 1-3 were 13.9-20.3% and 23.0-28.3%. Bias-adjusted results for the CDT, MMSE and MCIS were: clinical diagnosis validity (kappa statistic) = {-0.02 (p=.61), 0.06 (p=.23), 0.92 (p<.0001)}; sensitivity = {59%, 71%, 94%}; specificity = {39%, 36%, 97%}; overall accuracy = {54%, 62%, 96%}; positive predictive value = {16%, 17%, 86%}; and negative predictive value = {83%, 87%, 96%}. The MMSE and CDT were not valid for early detection, while the MCIS had high validity and accuracy in the primary care cohort.

Reference:

Trenkle D, Shankle WR, Azen SP. Detecting Cognitive Impairment in Primary Care: Performance Assessment of Three Screening Instruments. Journal of Alzheimer’s Disease. 2007;11(3):323-335.

Detecting Cognitive Impairment in Primary Care: Performance Assessment of Three Screening Instruments

Douglas L. Trenkle, D.O.1 , William R. Shankle, M.D.2,3, Stanley P. Azen, Ph.D.4

Reference:

Trenkle D, Shankle WR, Azen SP. Detecting Cognitive Impairment in Primary Care: Performance Assessment of Three Screening Instruments. Journal of Alzheimer’s Disease. 2007;11(3):323-335.

The Japanese MCI Screen for Early Detection of Alzheimerʼs Disease and Related Disorders

Ai Cho; Mika Sugimura; Seigo Nakano, MD, PhD; Tatsuo Yamada, MD, PhD Department of Neurology, Fukuoka University

Early detection of Alzheimerʼs disease and related disorders in Japan is increasingly important. The MCI Screen (MCIS) - derived from the National Institute of Aging CERAD neuropsychologic battery—differentiates normal aging from mild cognitive impairment (MCI) and mild dementia with 97.3% and 99% accuracy, respectively. The Japanese MCIS (JMCIS), MMSE, quantitative SPECT (qSP) and MRI (qMR) were used to classify 63 outpatients at Fukuoka University Hospital who were either normal or had MCI based on Clinical Dementia Rating scores of 0 and 0.5 respectively. Performance statistics for the JMCIS, MMSE, qSP, and qMR were respectively: 1) Accuracy = .964, .768, .722, .733; 2) Sensitivity = .958, .792, .688, .700; 3) Specificity = 1.000, .625, 1.000, 1.000; and 4) Kappa validity = 0.813, 0.420, 0.296, 0.308. This initial study shows negligible differences between the English and Japanese MCIS, supporting its potential use for early detection in Japan.

Reference:

Cho A, Sugimura M, Nakano S, Yamada T. The Japanese MCI Screen for Early Detection of Alzheimer’s Disease and Related Disorders. The American Journal of Alzheimer’s Disease and Other Dementias. 2008;23(2):162-166.

Early Detection and Diagnosis of Demented Disorders Using the MCI Screen and Neuroimaging

Ai Cho, Mika Sugimura, Seigo Nakano, Tatsuo Yamada

The Dementia Care Program (DCP), developed by Medical Care Corporation, is used in the USA for early detection and management of dementing disorders due to Alzheimerʼs disease and related disorders (ADRD). The MCI Screen (MCIS)—the DCPʼs objective memory tool—is 97% accurate in discriminating mild cognitive impairment (MCI) from normal aging. The present study evaluated 63 patients at the Fukuoka University Memory clinic using the Clinical Dementia Rating (CDR) Scale to identify a sample of 52 MCI (CDR=0.5) and 11 normal aging (CDR=0) patients. These patients were administered the MCIS, the Depression Screen, and scanned with brain SPECT and/or quantitative MRI. After excluding 7 patients diagnosed with depression, the neuroimaging and MCIS results of the remaining 56 patients were compared. Among the 48 MCI patients, 46 were correctly classified by the MCIS (96% sensitivity), and 37 (80%) had a specific ADRD etiology diagnosed by neuroimaging. All 8 normal aging patients were correctly identified by both the MCIS neuroimaging studies. These findings support the value of the Japanese version of the MCIS followed by SPECT or quantitative MRI in early detection and diagnosis of MCI.

Reference:

Cho A, Sugimura M, Nakano S, Yamada T. Early Detection and Diagnosis of Demented Disorders Using the MCI Screen and Neuroimaging. The Japanese Journal of Clinical and Experimental Medicine. 2007;84(8):1152-1160..

Methods to Improve the Detection of Mild Cognitive Impairment

William R. Shankle, A. Kimball Romney, Junko Hara, Dennis Fortier, Malcolm B. Dick, James M. Chen, Timothy Chan, and Xijiang Sun

Departments of Cognitive Science and Anthropology and Brain Aging Research Unit, University of California, Irvine, CA 92612; and Medical Care Corporation, Irvine, CA 92612

Contributed by A. Kimball Romney, February 12, 2005

We examined whether the performance of the National Institute of Agingʼs Consortium to Establish a Registry for Alzheimerʼs Diseaseʼs 10-word list (CWL), part of the consortiumʼs neuropsychological battery, can be improved for detecting Alzheimerʼs disease and related disorders early. We focused on mild cognitive impairment (MCI) and mild dementia because these stages often go undetected, and their detection is important for treatment. Using standardized diagnostic criteria combined with history, physical examination, and cognitive, laboratory, and neuroimaging studies, we staged 471 community-dwelling subjects for dementia severity by using the Clinical Dementia Rating Scale. We then used correspondence analysis (CA) to derive a weighted score for each subject from their item responses over the three immediate- and one delayed-recall trials of the CWL. These CA-weighted scores were used with logistic regression to predict each subjectʼs probability of impairment, and receiver operating characteristic analysis was used to measure accuracy. For MCI vs. normal, accuracy was 97% [confidence interval (C.I.) 97–98%], sensitivity was 94% (C.I. 93–95%), and specificity was 89% (C.I. 88–91%). For MCI mild dementia vs. normal, accuracy was 98% (C.I. 98–99%), sensitivity was 96% (C.I. 95–97%), and specificity was 91% (C.I. 89–93%). MCI sensitivity was 12% higher (without lowering specificity) than that obtained with the delayed-recall total score (the standard method for CWL interpretation). Optimal positive and negative predictive values were 100% and at least 96.6%. These results show that CA-weighted scores can significantly improve early detection of Alzheimerʼs disease and related disorders.

Reference:

Shankle WR, Romney AK, Hara J, Fortier D, Dick M, Chen J, Chan T, Sun S. Method to improve the detection of mild cognitive impairment. Proceeding of National Academy of Science. 2005;102(13):4919-4924.

Development and Validation of the Memory Performance Index: Reducing Measurement Error in Recall Tests

William R. Shankle, Tushar Mangrola, Timothy Chan, Junko Hara

Medical Care Corporation

Background: The Memory Performance Index (MPI) quantifies the pattern of recalled and nonrecalled words of the Consortium to Establish a Registry for Alzheimer’s Disease Wordlist (CWL) onto a 0 to 100 scale and distinguishes normal from mild cognitive impairment with 96% to 97% accuracy.

Methods: In group A, 121,481 independently living individuals, 18 to 106 years old, were assessed with the CWL and classified as cognitively impaired (N 5 5,971) or normal (N 5 115,510). The MPI and CWL immediate free recall (IFR), delayed free recall (DFR), and total free recall (TFR) scores (the outcome measures) were each regressed against predictors of age, gender, race, education, test administration method (in-person or telephone), and wordlist used. Predictor effect sizes (Cohen’s f2) were computed for each outcome. In addition, CWL plus Functional Assessment Staging Tests (FAST) were administered to 441 normal to moderately severely demented (FAST stages 1 to 6) patients (group B). Median MPI scores were tested for significant differences across FAST stage.

Results: For group A, the variance explained by all predictors combined was MPI 5 55.0%, IFR 5 24.9%, DFR523.4%, and TFR526.9%. The age effect size on MPI score was large, but it was small on IFR, DFR, and TFR. The other predictors all had negligible (<0.02) or small effect sizes (0.02 to 0.15). For group B, median MPI scores progressively declined across all FAST stages (P<.0002).

Conclusions: MPI score progressively declines with increasing dementia severity. Also, MPI score explains 2 to 3 times more variance than total scores, which improves ability to detect treatment effects.

Reference:

Shankle WR, Mangrola T, Chan T, Hara J. Development and Validation of the Memory Performance Index: Reducing Measurement Error in Recall Tests. Alzheimer’s & Dementia. 2009;5(4):295-306.

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