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Validation of a novel Montreal Cognitive Assessment scoring algorithm in non-demented Parkinson's disease patients
Patricia Sulzer1,2, Sara Becker1,2, Walter Maetzler2,3
1German Center for Neurodegenerative Diseases (DZNE), University of Tübingen, Otfried-Müller-Str. 23, 72076, Tübingen, Germany.
Introduction:
The early diagnosis of mild cognitive impairment (PD-MCI) in Parkinson's disease (PD) is essential as it increases the future risk for PD dementia (PDD). Recently, a novel weighting algorithm for the Montreal Cognitive Assessment (MoCA) subtests has been reported, to best discriminate between those with and without cognitive impairment in PD. The aim of our study was to validate this scoring algorithm in a large sample of non-demented PD patients, hypothesizing that the weighted MoCA would have a higher diagnostic accuracy for PD-MCI than the original MoCA.
Methods:
In 202 non-demented PD patients, we evaluated cognitive status, clinical and demographic data, as well as the MoCA with a weighted and unweighted score. Receiver operating characteristic (ROC) curve analysis was used to evaluate discriminative ability of the MoCA. Group comparisons and ROC analysis were performed for PD-MCI classifications with a cut-off ≤ 1, 1.5, and 2 standard deviation (SD) below appropriate norms.
Results:
PD-MCI patients scored lower on the weighted than the original MoCA version (p < 0.001) compared to PD patients with normal cognitive function. Areas under the curve only differed significantly for the 2 SD cut-off, leading to an increased sensitivity of the weighted MoCA score (72.9% vs. 70.5%) and specificity compared to the original version (79.0% vs. 65.4%).
Conclusions:
Our results indicate better discriminant power for the weighted MoCA compared to the original for more advanced stages of PD-MCI (2 SD cut-off). Future studies are needed to evaluate the predictive value of the weighted MoCA for PDD.
Insights
A new weighted Montreal Cognitive Assessment (MoCA) score improves the diagnosis of mild cognitive impairment in Parkinson's disease (PD-MCI), particularly in more advanced stages.
Area of Science:
- Neurology
- Cognitive Science
- Biostatistics
Background:
- Early diagnosis of mild cognitive impairment in Parkinson's disease (PD-MCI) is crucial for predicting Parkinson's disease dementia (PDD).
- A novel weighting algorithm for the Montreal Cognitive Assessment (MoCA) has shown promise in distinguishing cognitive impairment in PD.
- Validating this weighted MoCA algorithm in a larger cohort is essential.
Purpose of the Study:
- To validate a novel weighted scoring algorithm for the MoCA in a large sample of non-demented Parkinson's disease patients.
- To compare the diagnostic accuracy of the weighted MoCA against the original MoCA for identifying PD-MCI.
- To hypothesize that the weighted MoCA would demonstrate superior diagnostic accuracy for PD-MCI.
Main Methods:
- Cognitive status, clinical, and demographic data were collected from 202 non-demented PD patients.
- The MoCA was administered using both weighted and unweighted scoring methods.
- Receiver operating characteristic (ROC) curve analysis was employed to assess the discriminative ability of both MoCA versions at different cut-off points (1, 1.5, and 2 SD below norms).
Main Results:
- Patients with PD-MCI exhibited lower scores on the weighted MoCA compared to the original MoCA (p < 0.001) versus cognitively normal PD patients.
- Significant differences in discriminative ability were observed at the 2 standard deviation (SD) cut-off.
- The weighted MoCA demonstrated increased sensitivity (72.9% vs. 70.5%) and specificity (79.0% vs. 65.4%) compared to the original MoCA at the 2 SD cut-off.
Conclusions:
- The weighted MoCA scoring algorithm shows improved discriminant power for identifying more advanced stages of PD-MCI (2 SD cut-off).
- These findings suggest the weighted MoCA is a more effective tool for detecting subtle cognitive changes in Parkinson's disease.
- Further research is warranted to explore the predictive capabilities of the weighted MoCA for the progression to PDD.