scholarly journals CAIDE dementia risk score and cognitive correlates among Filipinos with mild cognitive impairment

2020 ◽  
Vol 16 (S10) ◽  
Author(s):  
Jemellee Cano ◽  
Ma Fe P Guzman ◽  
Thien Kieu Thi Phung ◽  
Jacqueline C Dominguez
BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e051185
Author(s):  
Meng Wang ◽  
Eric E Smith ◽  
Nils Daniel Forkert ◽  
Thierry Chekouo ◽  
Zahinoor Ismail ◽  
...  

IntroductionTo date, there is no broadly accepted dementia risk score for use in individuals with mild cognitive impairment (MCI), partly because there are few large datasets available for model development. When evidence is limited, the knowledge and experience of experts becomes more crucial for risk stratification and providing MCI patients with prognosis. Structured expert elicitation (SEE) includes formal methods to quantify experts’ beliefs and help experts to express their beliefs in a quantitative form, reducing biases in the process. This study proposes to (1) assess experts’ beliefs about important predictors for 3-year dementia risk in persons with MCI through SEE methodology and (2) to integrate expert knowledge and patient data to derive dementia risk scores in persons with MCI using a Bayesian approach.Methods and analysisThis study will use a combination of SEE methodology, prospectively collected clinical data, and statistical modelling to derive a dementia risk score in persons with MCI . Clinical expert knowledge will be quantified using SEE methodology that involves the selection and training of the experts, administration of questionnaire for eliciting expert knowledge, discussion meetings and results aggregation. Patient data from the Prospective Registry for Persons with Memory Symptoms of the Cognitive Neurosciences Clinic at the University of Calgary; the Alzheimer’s Disease Neuroimaging Initiative; and the National Alzheimer’s Coordinating Center’s Uniform Data Set will be used for model training and validation. Bayesian Cox models will be used to incorporate patient data and elicited data to predict 3-year dementia risk.DiscussionThis study will develop a robust dementia risk score that incorporates clinician expert knowledge with patient data for accurate risk stratification, prognosis and management of dementia.


2006 ◽  
Vol 14 (7S_Part_20) ◽  
pp. P1094-P1094
Author(s):  
Sultan Raja Chaudhury ◽  
Tulsi Patel ◽  
Abigail Fallows ◽  
Keeley J. Brookes ◽  
Tamar Guetta-Baranes ◽  
...  

2018 ◽  
Vol 24 (3) ◽  
pp. 421-430 ◽  
Author(s):  
Mark W. Logue ◽  
Matthew S. Panizzon ◽  
Jeremy A. Elman ◽  
Nathan A. Gillespie ◽  
Sean N. Hatton ◽  
...  

2020 ◽  
Vol 12 ◽  
Author(s):  
Patcharaporn Srisaikaew ◽  
Nahathai Wongpakaran ◽  
Nicole D. Anderson ◽  
J. Jean Chen ◽  
Suchart Kothan ◽  
...  

Damage to the fornix leads to significant memory impairment and executive dysfunction and is associated with dementia risk. We sought to identify if fornix integrity and fiber length are disrupted in mild cognitive impairment (MCI) and how they associate with cognition. Data from 14 healthy older adult controls (HCs) and 17 subjects with non-amnestic MCI (n-aMCI) were analyzed. Diffusion tensor imaging (DTI) at 1.5 Tesla MRI was performed to enable manual tracing of the fornix and calculation of DTI parameters. Higher fractional anisotropy of body and column of the fornix was associated with better executive functioning and memory, more strongly in the HC than in the n-aMCI group. Fornix fiber tract length (FTL) was associated with better executive function, more strongly in the n-aMCI than in the HC group, and with better memory, more strongly in the HC than in the n-aMCI group. These results highlight a decline in the contributions of the fornix to cognition in n-aMCI and suggest that maintenance of fornix FTL is essential for sustaining executive functioning in people with n-aMCI.


2020 ◽  
Vol 9 (9) ◽  
pp. 2726
Author(s):  
Angel Michael Ortiz Zuñiga ◽  
Rafael Simó ◽  
Octavio Rodriguez-Gómez ◽  
Cristina Hernández ◽  
Adrian Rodrigo ◽  
...  

Introduction: Although the Diabetes Specific Dementia Risk Score (DSDRS) was proposed for predicting risk of dementia at 10 years, its usefulness as a screening tool is unknown. For this purpose, the European consortium MOPEAD included the DSDRS within the specific strategy for screening of cognitive impairment in type 2 diabetes (T2D) patients attended in a third-level hospital. Material and Methods: T2D patients > 65 years, without known cognitive impairment, attended in a third-level hospital, were evaluated. As per MOPEAD protocol, patients with MMSE ≤ 27 or DSDRS ≥ 7 were referred to the memory clinic for complete neuropsychological assessment. Results: 112 T2D patients were recruited. A total of 82 fulfilled the criteria for referral to the memory unit (43 of them declined referral: 48.8% for associated comorbidities, 37.2% lack of interest, 13.95% lack of social support). At the Fundació ACE’s Memory Clinic, 34 cases (87.2%) of mild cognitive impairment (MCI) and 3 cases (7.7%) of dementia were diagnosed. The predictive value of DSDRS ≥ 7 as a screening tool of cognitive impairment was AUROC = 0.739, p 0.024, CI 95% (0.609–0.825). Conclusions: We found a high prevalence of unknown cognitive impairment in TD2 patients who attended a third-level hospital. The DSDRS was found to be a useful screening tool. The presence of associated comorbidities was the main factor of declining referral.


2018 ◽  
Vol 30 (9) ◽  
pp. 1415-1415
Author(s):  
Kyla-Louise Horne ◽  
Daniel J. Myall ◽  
Michael R. MacAskill ◽  
Tim J. Anderson ◽  
John C. Dalrymple-Alford

A recent paper, “Parkinson's disease mild cognitive impairment classifications and neurobehavioral symptoms” (McDermott et al., 2017), provides an interesting comparison of the influence of different criteria for Parkinson's disease with mild cognitive impairment (PD-MCI) on progression to dementia (PDD). Unfortunately, McDermott et al. (2017) incorrectly stated that “only 21% of PD-MCI participants (identified with a 1.5 SD cut-off) converted to PDD within four years” (p.6) in our study (Wood et al., 2016). However, the important point made by Wood et al. (2016) was that the proportion of conversions to PDD was 51% when the PD-MCI diagnosis required a minimum of two 1.5 SD impairments within any single cognitive domain, whereas additional PD-MCI patients classified with one impairment at 1.5 SD in each of the two domains (but never two impairments in the same domain) had a non-significant risk of dementia relative to non-MCI patients (11% vs. 6% converted, respectively). Our PDD conversion rate was 38% when combining both 1.5 SD criteria (21/56 PD-MCI patients vs. 4/65 non-MCI patients converted); McDermott et al. (2017) found a 42% conversion rate over three years for similarly described PD-MCI patients (10/24 PD-MCI patients vs. 0/27 non-MCI patients converted). Our study was also part of a multinational study (n = 467) showing that PD-MCI has predictive validity beyond known demographic and PD-specific factors of influence (Hoogland et al., 2017). All three studies found that multiple cognitive domain impairments are common in PD-MCI. Nonetheless, the research community needs to clarify the association between PD-MCI subtypes and, especially, the optimal cognitive markers for dementia risk in PD patients.


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