Early-onset Alzheimer’s disease (EOAD), a progressive neurological condition, is often difficult to diagnose before substantial neuronal damage. Neuropsychological assessments offer a more cost-effective and accessible method for detecting early cognitive decline, yet limited research exists on optimal combinations for EOAD identification. This study analyzed data from 24 clinical studies to determine the diagnostic accuracy of FDA-approved EOAD assessments used in combination. No single combination was found to be superior across all diagnostic metrics. MMSE + MoCA + ADAS-Cog showed the highest sensitivity, MMSE + MoCA + RBANS the highest specificity, MMSE + RBANS the highest AUC value, and MMSE + MoCA the highest classification accuracy. These findings highlight the importance of aligning assessment selection with diagnostic goals: high-sensitivity combinations for early detection; high-specificity combinations for diagnosis confirmation; and high AUC or classification accuracy combinations for balanced decision-making, overall enhancing accuracy, consistency, and timeliness of EOAD identification.
","abstract_html":"<p>Early-onset Alzheimer’s disease (EOAD), a progressive neurological condition, is often difficult to diagnose before substantial neuronal damage. Neuropsychological assessments offer a more cost-effective and accessible method for detecting early cognitive decline, yet limited research exists on optimal combinations for EOAD identification. This study analyzed data from 24 clinical studies to determine the diagnostic accuracy of FDA-approved EOAD assessments used in combination. No single combination was found to be superior across all diagnostic metrics. MMSE + MoCA + ADAS-Cog showed the highest sensitivity, MMSE + MoCA + RBANS the highest specificity, MMSE + RBANS the highest AUC value, and MMSE + MoCA the highest classification accuracy. These findings highlight the importance of aligning assessment selection with diagnostic goals: high-sensitivity combinations for early detection; high-specificity combinations for diagnosis confirmation; and high AUC or classification accuracy combinations for balanced decision-making, overall enhancing accuracy, consistency, and timeliness of EOAD identification.</p>","abstract_has_math":false,"creators":["Thomas, Elise"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Open Access Thesis","degree_discipline":"Health Sciences","degree_department":null,"school":null,"contributors":["Michael Switzer, Ph.D.","Shannon Murray Diacono, MHS."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-01-01T08:00:00Z","date_published":"2025-01-01T08:00:00Z","updated_at":"2026-07-24T02:17:53Z","subjects":["Under the Curve (AUC)","Diagnostic Accuracy","Early-Onset Alzheimer's Disease","Neuropsychological Assessment","Receiver Operating Characteristic (ROC) Analysis","Sensitivity and Specificity","Medicine and Health Sciences","Neurosciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.emich.edu/theses/1327","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Michael Switzer, Ph.D.","Shannon Murray Diacono, MHS."]},{"key":"dc:creator","label":"Author","values":["Thomas, Elise"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-20T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Health Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Under the Curve (AUC)","Diagnostic Accuracy","Early-Onset Alzheimer's Disease","Neuropsychological Assessment","Receiver Operating Characteristic (ROC) Analysis","Sensitivity and Specificity","Medicine and Health Sciences","Neurosciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.emich.edu/theses/1327"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Early-onset Alzheimer’s disease (EOAD), a progressive neurological condition, is often difficult to diagnose before substantial neuronal damage. Neuropsychological assessments offer a more cost-effective and accessible method for detecting early cognitive decline, yet limited research exists on optimal combinations for EOAD identification. This study analyzed data from 24 clinical studies to determine the diagnostic accuracy of FDA-approved EOAD assessments used in combination. No single combination was found to be superior across all diagnostic metrics. MMSE + MoCA + ADAS-Cog showed the highest sensitivity, MMSE + MoCA + RBANS the highest specificity, MMSE + RBANS the highest AUC value, and MMSE + MoCA the highest classification accuracy. These findings highlight the importance of aligning assessment selection with diagnostic goals: high-sensitivity combinations for early detection; high-specificity combinations for diagnosis confirmation; and high AUC or classification accuracy combinations for balanced decision-making, overall enhancing accuracy, consistency, and timeliness of EOAD identification.
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