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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Defining and Measuring Cognitive Decline and Clinical Relevance
Carolin Kurz1, Paul Newhouse2, Kate A Wyman-Chick3
1Department of Psychiatry and Psychotherapy, Technical University of Munich, TUM School of Medicine and Health, TUM University Hospital, Munich, Germany. carolin.kurz@mri.tum.de.
This study explores cognitive impairment definitions, balancing biomarkers with clinical function. Pragmatic diagnostic approaches are recommended for routine care and research to improve patient outcomes.
Area of Science:
- Neurology
- Gerontology
- Clinical Psychology
Background:
- Cognitive impairment frameworks increasingly use biomarkers but rely on clinical decline and functional impact for classification.
- Mild cognitive impairment (MCI) signifies an intermediate stage between normal aging and dementia, crucial for early detection and intervention.
Purpose of the Study:
- To examine the balance between biological and clinical definitions of cognitive impairment.
- To discuss implications for diagnosis, care, and research in cognitive disorders.
- To propose practical diagnostic strategies for routine care and research settings.
Main Methods:
- Literature review and conceptual analysis of current cognitive impairment frameworks.
- Examination of diagnostic approaches in routine care versus research settings.
- Discussion of challenges in definition standardization and outcome measure alignment.
Main Results:
- A tiered diagnostic approach combining screening, clinical assessment, and informant data is practical for routine care.
- Integrating cognitive performance with functional measures captures clinically meaningful change better than cognition-only endpoints in research.
- Heterogeneity in definitions and outcomes hinders cross-study comparability and integration of patient-relevant benefits.
Conclusions:
- Clinically useful definitions of cognitive impairment must integrate objective deficits with real-world impact.
- Pragmatic, staged diagnostic pathways are optimal for routine clinical practice.
- Future research should prioritize standardization, feasibility, and alignment with patient-centered outcomes.
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