Spanish version of the Mattis Dementia Rating Scale-2 for early detection of Alzheimer's disease and mild cognitive impairment

2017 ◽  
Vol 33 (6) ◽  
pp. 832-840 ◽  
Author(s):  
Elina Boycheva ◽  
Israel Contador ◽  
Bernardino Fernández-Calvo ◽  
Francisco Ramos-Campos ◽  
Verónica Puertas-Martín ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Rating Scale ◽  
Spanish Version ◽  
Mattis Dementia Rating Scale ◽  
Dementia Rating Scale

Validity of the Clinical Dementia Rating Scale Sum of Boxes in Staging and Detection of Cognitive Impairment in Mexican Americans

2020 ◽  
pp. 089198872097375
Author(s):  
Parunyou Julayanont ◽  
John C. DeToledo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Mexican Americans ◽  
Rating Scale ◽  
Amnestic Mild Cognitive Impairment ◽  
Clinical Dementia Rating ◽  
Education Levels ◽  
Dementia Rating Scale

Objective: We evaluated the utility of the Clinical Dementia Rating Sum of Boxes score (CDR-SB) in staging and detecting amnestic-mild cognitive impairment (a-MCI) and Alzheimer’s disease (AD) among Mexican Americans. Methods: Receiver operator curves were generated to evaluate the validity of the CDR-SB in staging and detecting a-MCI and AD in 1,073 Mexican Americans (758 controls, 163 a-MCI, and 152 AD). Results: Optimal ranges of the CDR-SB were 0, 0.5-4, 4.5-8.0, 8.5-13 and 13.5-18 for staging the global CDR score of 0, 0.5, 1, 2, and 3, respectively. The CDR-SB ≥ 0.5 differentiated the a-MCI patients from the controls (sensitivity 100% and specificity 99.5%) and ≥ 2.0 distinguished the AD from a-MCI patients (sensitivity 83.6% and specificity 87.1%). These cutoffs were also appropriate for patients with ≤6 years of education. Conclusion: The CDR-SB is useful to detect and stage a-MCI and AD in Mexican Americans with diverse education levels.

scholarly journals Performance of Mattis dementia rating scale-Chinese Version in patients with mild cognitive impairment and Alzheimer's disease

2020 ◽  
Author(s):  
Shuxia Qian ◽  
Keliang Chen ◽  
Qiaobing Guan ◽  
Qihao Guo
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sensitivity And Specificity ◽  
Roc Curve ◽  
Rating Scale ◽  
Chinese Version ◽  
Roc Curve Analysis ◽  
Mattis Dementia Rating Scale ◽  
Sensitive Function ◽  
Dementia Rating Scale

Abstract Background: To identify the applicability of the Chinese Version of Mattis Dementia Rating Scale (DRS-CV).Methods: The DRS-CV was administered to 483 participants, including 136 normal controls, 167 patients with mild cognition impairment (MCI), and 180 patients with Alzheimer’s disease (AD). Receiver Operating Characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the scale.Results: The scores of DRS-CV were ranked in the order of NC >MCI > mild AD > moderate AD group. Memory was the sensitive function affected at a relatively earlier stage of AD. ROC curve analysis indicated the DRS-CV total score and memory subscale showed excellent sensitivity and specificity in the discrimination between MCI from mild AD and mild AD from moderate AD, but poor sensitivity and specificity in the discrimination between MCI and NC.Conclusion: The DRS-CV is useful to the early diagnosis and severity of AD, not to the early identification of MCI.

scholarly journals Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Support Vector ◽  
Alzheimer’S Disease Dementia

Abstract Background An increase in lifespan in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power enable new methods for the early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease dementia. Methods We conducted a systematic review following PRISMA guidelines of studies where machine learning was applied to neuroimaging data in order to predict whether patients with mild cognitive impairment might develop Alzheimer’s disease dementia or remain stable. After removing duplicates, we screened 452 studies and selected 116 for qualitative analysis. Results Most studies used magnetic resonance image (MRI) and positron emission tomography (PET) data but also magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease neuroimaging initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common was support vector machine with a mean accuracy of 75.4%, but convolutional neural networks achieved a higher mean accuracy of 78.5%. Studies combining MRI and PET achieved overall better classification accuracy than studies that only used one neuroimaging technique. In general, the more complex models such as those based on deep learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, genetic, and behavioral) achieved the best performance. Conclusions Although the performance of the different methods still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

scholarly journals Machine Learning Methods for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease. A Systematic Review.

2021 ◽  
Author(s):  
Sergio Grueso ◽  
Raquel Viejo-Sobera
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Neurocognitive Disorders ◽  
Multidimensional Data ◽  
Support Vector

Abstract Background: Increase in life-span in our society is a double-edged sword that entails a growing number of patients with neurocognitive disorders, Alzheimer’s disease being the most prevalent. Advances in medical imaging and computational power, enable new methods for early detection of neurocognitive disorders with the goal of preventing or reducing cognitive decline. Computer-aided image analysis and early detection of changes in cognition is a promising approach for patients with mild cognitive impairment, sometimes a prodromal stage of Alzheimer’s disease.Methods: We conducted a systematic review following PRISMA guidelines of studies where Machine Learning was applied to neuroimaging data in order to predict the progression from Mild Cognitive Impairment to Alzheimer’s disease. After removing duplicates, we screened 159 studies and selected 47 for a qualitative analysis. Results: Most studies used Magnetic Resonance Image and Positron Emission Tomography data but also Magnetoencephalography. The datasets were mainly extracted from the Alzheimer’s disease Neuroimage Initiative (ADNI) database with some exceptions. Regarding the algorithms used, the most common were support vector machines, but more complex models such as Deep Learning, combined with multimodal and multidimensional data (neuroimaging, clinical, cognitive, biological, and behavioral) achieved the best performance. Conclusions: Although performance of the different models still has room for improvement, the results are promising and this methodology has a great potential as a support tool for clinicians and healthcare professionals.

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