Assessing Cognitive Impairment in the Health and Retirement Study Harmonized Cognitive Assessment Protocol Project: Comparing a Diagnostic Algorithm With a Diagnostic Consensus Panel

BackgroundAccurate classification of cognitive impairment in population studies is challenging.ObjectiveTo compare the performance of a diagnostic algorithm with a clinical consensus panel.SampleIn 2016, the Health and Retirement Study (HRS) implemented the Harmonized Cognitive Assessment Protocol Project (HRS-HCAP) to streamline cognitive assessments for select HRS participants.MethodsThe Manly-Jones HCAP diagnostic classification was used to classify cognitive status as normal, mild cognitive impairment (MCI), or dementia. For this analysis, a consensus panel of five clinicians reviewed 50 cases with high diagnostic uncertainty, each reviewing 30 cases, blinded to the algorithm's classifications.AnalysisDiagnostic concordance was assessed using unweighted and weighted Cohen's kappa (κ).ResultsUnweighted concordance was 70% (35/50), with discordance mostly among MCI cases. Weighted concordance was 84%. Unweighted κ was 0.56 (95% CI 0.30-0.81) and weighted κ was 0.75 (95% CI 0.49-0.91), indicating moderate to substantial agreement between the two methods.

Authors

Institutions

Publication Details

Journal
Journal of Aging and Health
Published
2025-04-15
DOI
https://doi.org/10.1177/08982643251335370
Citations
1
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
1.47

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing Cognitive Impairment in the Health and Retirement Study Harmonized Cognitive Assessment Protocol Project: Comparing a Diagnostic Algorithm With a Diagnostic Consensus Panel

Brenda L. Plassman, Bruno Giordani, Lindsay H. Ryan, Richard N. Jones et al.
1 citations
Journal of Aging and Health
Dementia and Cognitive Impairment Research
1.47
article

Assessing Cognitive Impairment in the Health and Retirement Study Harmonized Cognitive Assessment Protocol Project: Comparing a Diagnostic Algorithm With a Diagnostic Consensus Panel

Brenda L. Plassman, Bruno Giordani, Lindsay H. Ryan, Richard N. Jones, Kenneth M. Langa, Jennifer J. Manly, Madeline Farron, Deborah A. Levine
article en
1 citations

Abstract

BackgroundAccurate classification of cognitive impairment in population studies is challenging.ObjectiveTo compare the performance of a diagnostic algorithm with a clinical consensus panel.SampleIn 2016, the Health and Retirement Study (HRS) implemented the Harmonized Cognitive Assessment Protocol Project (HRS-HCAP) to streamline cognitive assessments for select HRS participants.MethodsThe Manly-Jones HCAP diagnostic classification was used to classify cognitive status as normal, mild cognitive impairment (MCI), or dementia. For this analysis, a consensus panel of five clinicians reviewed 50 cases with high diagnostic uncertainty, each reviewing 30 cases, blinded to the algorithm's classifications.AnalysisDiagnostic concordance was assessed using unweighted and weighted Cohen's kappa (κ).ResultsUnweighted concordance was 70% (35/50), with discordance mostly among MCI cases. Weighted concordance was 84%. Unweighted κ was 0.56 (95% CI 0.30-0.81) and weighted κ was 0.75 (95% CI 0.49-0.91), indicating moderate to substantial agreement between the two methods.

Journal of Aging and Health
Duke University (US), Brown University (US), University of Michigan (US), Columbia University (US)
National Institute on Aging
Openalex Percentile: Top 20%
Dementia and Cognitive Impairment Research
1.47
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.