scholarly journals Cognitive Impairment and Dementia in Primary Care: Current Knowledge and Future Directions Based on Findings From a Large Cross-Sectional Study in Crete, Greece

2020 ◽  
Vol 7 ◽  
Author(s):  
Antonios Bertsias ◽  
Emmanouil Symvoulakis ◽  
Chariklia Tziraki ◽  
Symeon Panagiotakis ◽  
Lambros Mathioudakis ◽  
...  

Introduction: Dementia severely affects the quality of life of patients and their caregivers; however, it is often not adequately addressed in the context of a primary care consultation, especially in patients with multi-morbidity.Study Population and Methods: A cross-sectional study was conducted between March-2013 and December-2014 among 3,140 consecutive patients aged >60 years visiting 14 primary health care practices in Crete, Greece. The Mini-Mental-State-Examination [MMSE] was used to measure cognitive status using the conventional 24-point cut-off. Participants who scored low on MMSE were matched with a group of elders scoring >24 points, according to age and education; both groups underwent comprehensive neuropsychiatric and neuropsychological assessment. For the diagnosis of dementia and Mild-Cognitive-Impairment (MCI), the Diagnostic and Statistical Manual-of-Mental-Disorders (DSM-IV) criteria and the International-Working-Group (IWG) criteria were used. Chronic conditions were categorized according to ICD-10 categories. Logistic regression was used to provide associations between chronic illnesses and cognitive impairment according to MMSE scores. Generalized Linear Model Lasso Regularization was used for feature selection in MMSE items. A two-layer artificial neural network model was used to classify participants as impaired (dementia/MCI) vs. non-impaired.Results: In the total sample of 3,140 participants (42.1% men; mean age 73.7 SD = 7.8 years), low MMSE scores were identified in 645 (20.5%) participants. Among participants with low MMSE scores 344 (54.1%) underwent comprehensive neuropsychiatric evaluation and 185 (53.8%) were diagnosed with Mild-Cognitive-Impairment (MCI) and 118 (34.3%) with dementia. Mental and behavioral disorders (F00-F99) and diseases of the nervous system (G00-G99) increased the odds of low MMSE scores in both genders. Generalized linear model lasso regularization indicated that 7/30 MMSE questions contributed the most to the classification of patients as impaired (dementia/MCI) vs. non-impaired with a combined accuracy of 82.0%. These MMSE items were questions 5, 13, 19, 20, 22, 23, and 26 of the Greek version of MMSE assessing orientation in time, repetition, calculation, registration, and visuo-constructive ability.Conclusions: Our study identified certain chronic illness-complexes that were associated with low MMSE scores within the context of primary care consultation. Also, our analysis indicated that seven MMSE items provide strong evidence for the presence of dementia or MCI.

2010 ◽  
Vol 68 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Felipe Kenji Sudo ◽  
Gilberto Sousa Alves ◽  
Carlos Eduardo de Oliveira Alves ◽  
Maria Elisa Lanna ◽  
Letice Ericeira-Valente ◽  
...  

OBJECTIVE: Cerebrovascular disease (CVD) is associated with cognitive deficits. This cross-sectional study examines differences among healthy elderly controls and patients with vascular mild cognitive impairment (VaMCI) and vascular dementia (VaD) in performances on CAMCOG subscales. METHOD: Elderly individuals (n=61) were divided into 3 groups, according to cognitive and neuroimaging status: 16 controls, 20 VaMCI and 25 VaD. VaMCI and VaD individuals scored over 4 points on the Hachinski Ischemic Scale. RESULTS: Significant differences in total CAMCOG scores were observed across the three groups (p<0.001). VaD subjects performed worse than those with VaMCI in most CAMCOG subscales (p<0.001). All subscales showed differences between controls and VaD (p<0.001). Performance on abstract thinking showed difference between VaMCI and controls (p<0.001). CONCLUSION: CAMCOG discriminated controls from VaMCI and VaD. Assessment of abstract thinking may be useful as a screening item for diagnosis of VaMCI.


PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0145521 ◽  
Author(s):  
Pin Wang ◽  
Rong Huang ◽  
Sen Lu ◽  
Wenqing Xia ◽  
Rongrong Cai ◽  
...  

2020 ◽  
Vol 131 ◽  
pp. 110816 ◽  
Author(s):  
Felipe de Oliveira Silva ◽  
José Vinícius Ferreira ◽  
Jéssica Plácido ◽  
Daniel Chagas ◽  
Jomilto Praxedes ◽  
...  

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