Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall - Scorecard - MDSpire
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Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall
Clinical Scorecard: Optimizing Eligibility Evaluation for Alzheimer's Disease Treatment: Predicting MMSE Outcomes with the Digital Clock and Recall Method
At a Glance
Category
Detail
Condition
Alzheimer's Disease
Key Mechanisms
Digital cognitive assessment using machine learning to predict MMSE scores.
Target Population
Older adults with mild cognitive impairment or early dementia due to Alzheimer's disease.
Care Setting
Clinical practice integrating anti-amyloid disease-modifying therapies.
Key Highlights
Digital Clock and Recall (DCR™) offers a rapid, equitable cognitive assessment.
Machine learning model predicts MMSE scores with RMSE comparable to manual MMSE.
DCR demonstrates reduced bias against underrepresented racial and ethnic groups.
Guideline-Based Recommendations
Diagnosis
Utilize DCR for cognitive assessment to determine eligibility for DMTs.
Management
Incorporate DCR in clinical workflows to streamline patient triage for DMT eligibility.
Monitoring & Follow-up
Monitor cognitive function using DCR as a less biased alternative to MMSE.
Risks
Relying on MMSE may perpetuate health disparities in treatment eligibility.
Patient & Prescribing Data
Older adults with cognitive impairment or early dementia.
DMTs like lecanemab and donanemab target amyloid pathology in Alzheimer's disease.
Clinical Best Practices
Adopt digital cognitive assessments to improve diagnostic accuracy.
Ensure equitable access to cognitive assessments across diverse populations.
