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Tsiakiri A, Bakirtzis C, Plakias S, Vlotinou P, Vadikolias K, Terzoudi A, Christidi F. Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data. Biomedicines 2024; 12:1232. [PMID: 38927439 PMCID: PMC11201179 DOI: 10.3390/biomedicines12061232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors-ones that can be easily examined in clinical settings-into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.
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Affiliation(s)
- Anna Tsiakiri
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
| | - Christos Bakirtzis
- B’ Department of Neurology and the MS Center, School of Medicine, AHEPA University Hospital, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Spyridon Plakias
- Department of Physical Education and Sport Science, University of Thessaly, 41500 Trikala, Greece;
| | - Pinelopi Vlotinou
- Department of Occupational Therapy, University of West Attica, 12243 Athens, Greece;
| | - Konstantinos Vadikolias
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
| | - Aikaterini Terzoudi
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
| | - Foteini Christidi
- Neurology Department, School of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece; (A.T.); (K.V.); (A.T.)
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Choe J, Kudrna R, Fonseca LM, Chaytor NS. Usefulness of the Montreal Cognitive Assessment in Older Adults With Type 1 Diabetes. Diabetes Spectr 2023; 36:385-390. [PMID: 37982060 PMCID: PMC10654125 DOI: 10.2337/ds23-0012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Objective Older adults with type 1 diabetes are at high risk for cognitive impairment, yet the usefulness of common cognitive screening instruments has not been evaluated in this population. Methods A total of 201 adults ≥60 years of age with type 1 diabetes completed a battery of neuropsychological measures and the Montreal Cognitive Assessment (MoCA). Receiver operating characteristic (ROC) curves and Youden indices were used to evaluate overall screening test performance and to select an optimal MoCA cutoff score for detecting low cognitive performance, as defined as two or more neuropsychological test performances ≥1.5 SD below demographically corrected normative data. Results The ROC area under the curve (AUC) was 0.745 (P < 0.001). The publisher-recommended cutoff score of <26 resulted in sensitivity of 60.4% and specificity of 71.4%, whereas a cutoff score of <27 resulted in sensitivity of 75.0% and specificity of 61.0%. The Youden indices for these cutoff scores were 0.318 and 0.360, respectively. Minimally acceptable sensitivity (i.e., >0.80) was obtained when using a cutoff score of <28, whereas >0.80 specificity was obtained with a cutoff score of <25. Conclusions The MoCA has modest overall performance (AUC 0.745) as a cognitive screening instrument in older adults with type 1 diabetes. The standard cutoff score of <26/30 may not adequately detect individuals with neuropsychological testing-defined abnormal cognition. The optimal MoCA cutoff score (based on the Youden index) was <27/30. A score of <28 resulted in acceptable sensitivity but was accompanied by low specificity (42%). Future studies with a more diverse population are needed to confirm these findings.
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Affiliation(s)
- James Choe
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA
| | - Rachel Kudrna
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA
| | | | - Naomi S Chaytor
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA
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Sun R, Ge B, Wu S, Li H, Lin L. Optimal cut-off MoCA score for screening for mild cognitive impairment in elderly individuals in China: A systematic review and meta-analysis. Asian J Psychiatr 2023; 87:103691. [PMID: 37499366 DOI: 10.1016/j.ajp.2023.103691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/16/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
AIM To evaluate the optimal cut-off MoCA score for elderly individuals with MCI. DESIGN A systematic review and meta-analysis. METHOD Articles were retrieved from PubMed, Ovid, Embase, The Cochrane Library, PsycINFO, CBM, CNKI, WanFang and CQVIP and were assessed by using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Figures of the assessment were made by using Review Manager 5.3, and a meta-analysis of the data was conducted by using Bivariate Random-effects Meta-Analysis (BRMA) via Stata 14.0. RESULTS Seventeen articles were retrieved from the database, and when the cut-offs were 24/25 and 25/26, they represented the same diagnostic value; in addition, the AUC was 0.96, which demonstrated high predictive validity for mild cognitive impairment screening. However, the sensitivity was higher with 25/26 (se=0.95, sp=0.80), whereas the specificity was higher with 24/25 (se=0.92, sp=0.89).
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Affiliation(s)
- Rui Sun
- International Medical Services, Peking Union Medical College Hospital, Beijing, China
| | - Binqian Ge
- School of Nursing, Suzhou Vocational Health College, Suzhou, China
| | - Shiyu Wu
- International Medical Services, Peking Union Medical College Hospital, Beijing, China
| | - Huiling Li
- School of Nursing, Soochow University and The First Affiliated Hospital of Soochow University, Suzhou, China.
| | - Lu Lin
- The First Affiliated Hospital of Soochow University, Suzhou, China.
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Comparison of the Greek Version of the Quick Mild Cognitive Impairment Screen and Montreal Cognitive Assessment in Older Adults. Healthcare (Basel) 2022; 10:healthcare10050906. [PMID: 35628043 PMCID: PMC9141789 DOI: 10.3390/healthcare10050906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/01/2022] [Accepted: 05/06/2022] [Indexed: 02/04/2023] Open
Abstract
Objective: Cognitive screening instruments (CSIs) are essential for everyday practice. The Quick Mild Cognitive Impairment (Qmci) screen, a short instrument designed to identify mild cognitive impairment, was recently translated into Greek (Qmci-Gr). The present study compared its diagnostic value against the Montreal Cognitive Assessment (MoCA) screen and examined its optimal cutoffs. Method: We recruited consecutive patients aged ≥55 years that presented with cognitive complaints from two outpatient clinics in Greece. The Qmci-Gr and MoCA were completed by all patients. Furthermore, they were assessed independently with a comprehensive flexible neuropsychological battery to establish a diagnostic classification. Results: In the current study, we assessed a total of 145 patients, with a median age of 70 years; 44 were classified as having Subjective Memory Complaints (SMC) but normal cognition, 32 with MCI and 69 with dementia. The Qmci-Gr had a higher accuracy compared to the MoCA in discriminating MCI from dementia, area under the curve (AUC) of 0.81 versus 0.75, respectively; however, this finding was marginally significant (p = 0.08). Its accuracy was marginally higher for distinguishing SMC from dementia, AUC of 0.94 versus 0.89 (p = 0.03). However, Qmci-Gr presented a lower accuracy than MoCa in differentiating SMC from MCI, AUC of 0.76 versus 0.94 (p = 0.006). Conclusions: The Qmci-Gr has comparable diagnostic accuracy to the MoCA regarding MCI and dementia groups. Further research, with larger and more diverse samples, may be necessary to ensure generalizability.
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The Effects of Horticultural Therapy on Sense of Coherence among Residents of Long-Term Care Facilities: A Quasi Experimental Design. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19095412. [PMID: 35564806 PMCID: PMC9101382 DOI: 10.3390/ijerph19095412] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/23/2022] [Accepted: 04/27/2022] [Indexed: 01/25/2023]
Abstract
Promoting positive mental health is crucial for the elderly living in long-term care facilities (LTCFs). This study aims to examine the effectiveness of horticultural therapy on the level of sense of coherence (SOC) among older LTCF residents with relatively normal mental function. With convenient sampling, a total of 86 participants were recruited from 12 LTCFs in northeastern Taiwan. In the experimental group (n = 49), the mean (±standard deviation) score of SOC was 50.45 ± 6.07 at baseline and increased to 56.37 ± 7.20 (p < 0.001) after 12-week horticultural intervention. In contrast, the mean SOC score did not change significantly in the control group (n = 37) during the study period. Generalized estimating equation analysis showed that a significant interaction effect between group and time on the SOC score (p < 0.001). Our findings indicate that horticultural therapy is effective to strengthen the SOC level of older LTCF residents without dementia.
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Wang X, Li F, Gao Q, Jiang Z, Abudusaimaiti X, Yao J, Zhu H. Evaluation of the Accuracy of Cognitive Screening Tests in Detecting Dementia Associated with Alzheimer's Disease: A Hierarchical Bayesian Latent Class Meta-Analysis. J Alzheimers Dis 2022; 87:285-304. [PMID: 35275533 DOI: 10.3233/jad-215394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
BACKGROUND Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) are neuropsychological tests commonly used by physicians for screening cognitive dysfunction of Alzheimer's disease (AD). Due to different imperfect reference standards, the performance of MoCA and MMSE do not reach consensus. It is necessary to evaluate the consistence and differentiation of MoCA and MMSE in the absence of a gold standard for AD. OBJECTIVE We aimed to assess the accuracy of MoCA and MMSE in screening AD without a gold standard reference test. METHODS Studies were identified from PubMed, Web of Science, CNKI, Chinese Wanfang Database, China Science and Technology Journal Database, and Cochrane Library. Our search was limited to studies published in English and Chinese before August 2021. A hierarchical Bayesian latent class model was performed in meta-analysis when the gold standard was absent. RESULTS A total of 67 studies comprising 5,554 individuals evaluated for MoCA and 76,862 for MMSE were included in this meta-analysis. The pooled sensitivity was 0.934 (95% CI 0.906 to 0.954) for MoCA and 0.883 (95% CI 0.859 to 0.903) for MMSE, while the pooled specificity was 0.899 (95% CI 0.859 to 0.928) for MoCA and 0.903 (95% CI 0.879 to 0.923) for MMSE. MoCA was useful to rule out dementia associated with AD with lower negative likelihood ratio (LR-) (0.074, 95% CI 0.051 to 0.108). MoCA showed better performance with higher diagnostic odds ratio (DOR) (124.903, 95% CI 67.459 to 231.260). CONCLUSION MoCA had better performance than MMSE in screening dementia associated with AD from patients with mild cognitive impairment or healthy controls.
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Affiliation(s)
- Xiaonan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Fengjie Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Qi Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Zhen Jiang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Xiayidanmu Abudusaimaiti
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Jiangyue Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
| | - Huiping Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, P.R. China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, P. R. China
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