How Can We Best Screen for Cognitive Impairment in Malaysia? A Pilot of the IDEA Cognitive Screen and Picture-Based Memory Impairment Scale and Comparison of Criterion Validity with the Mini Mental State Examination

Cognitive status has been reported to affect the peri-operative and post-operative outcomes of certain surgical procedures. This prospective study investigated the effect of preoperative cognitive impairment on the postoperative course of elderly patients (n = 122, >65 years), following spine surgery for degenerative spinal disease. Data on demographic characteristics, medical history, and blood analysis results were collected. Preoperative cognition was assessed using the mini-mental state examination, and patients were divided into three groups: normal cognition, mild cognitive impairment, and moderate-to-severe cognitive impairment. Discharge destinations (p = 0.014) and postoperative cardiopulmonary complications (p = 0.037) significantly differed based on the cognitive status. Operation time (p = 0.049), white blood cell count (p = 0.022), platelet count (p = 0.013), the mini-mental state examination score (p = 0.033), and the Beck Depression Inventory score (p = 0.041) were significantly associated with the length of hospital stay. Our investigation demonstrated that improved understanding of preoperative cognitive status may be helpful in surgical decision-making and postoperative care of elderly patients with degenerative spinal disease.

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Can the MMSE help clinicians predict progression from mild cognitive impairment to dementia?

BJPsych Advances ◽

10.1192/apt.21.6.363 ◽

2015 ◽

Vol 21 (6) ◽

pp. 363-366 ◽

Cited By ~ 3

Author(s):

Alex J. Mitchell

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Risk Prediction ◽

Mental State ◽

Mini Mental State Examination ◽

Case Finding ◽

Cognitive Test ◽

Prediction Tool ◽

State Examination

SummaryThe Mini-Mental State Examination (MMSE) is the most widely used bedside cognitive test. It has previously been shown to be poor as a case-finding tool for both dementia and mild cognitive impairment (MCI). This month's Cochrane Corner review examines whether the MMSE might be used as a risk prediction tool for later dementia in those with established MCI. From 11 studies of modest quality, it appears that the MMSE alone should not be relied on to predict later deterioration in people with MCI. As this is the case, it is likely that only a combination of predictors would be able to accurately predict progression from MCI to dementia.

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Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores

Journal of Personalized Medicine ◽

10.3390/jpm12010037 ◽

2022 ◽

Vol 12 (1) ◽

pp. 37

Author(s):

Jie Wang ◽

Zhuo Wang ◽

Ning Liu ◽

Caiyan Liu ◽

Chenhui Mao ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Impairment ◽

Random Forest ◽

Mental State ◽

Mini Mental State Examination ◽

Machine Learning Algorithms ◽

Assessment Model ◽

Test Time ◽

Cognitive Screening ◽

State Examination

Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis.

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A meta-analysis of the accuracy of the mini-mental state examination in the detection of dementia and mild cognitive impairment

Journal of Psychiatric Research ◽

10.1016/j.jpsychires.2008.04.014 ◽

2009 ◽

Vol 43 (4) ◽

pp. 411-431 ◽

Cited By ~ 457

Author(s):

Alex J. Mitchell

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Mental State ◽

Mini Mental State Examination ◽

Meta Analysis ◽

State Examination

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Detection of mild cognitive impairment in middle-aged and older adults with obstructive sleep apnoea

European Respiratory Journal ◽

10.1183/13993003.01137-2018 ◽

2018 ◽

Vol 52 (5) ◽

pp. 1801137 ◽

Cited By ~ 7

Author(s):

Katia Gagnon ◽

Andrée-Ann Baril ◽

Jacques Montplaisir ◽

Julie Carrier ◽

Sirin Chami ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Obstructive Sleep Apnoea ◽

Mental State ◽

Cognitive Assessment ◽

Mini Mental State Examination ◽

Sleep Apnoea ◽

Screening Tests ◽

Obstructive Sleep ◽

State Examination

Obstructive sleep apnoea increases the risk for mild cognitive impairment and dementia. The present study aimed to characterise the ability of two cognitive screening tests, the Mini-Mental State Examination and the Montreal Cognitive Assessment, to detect mild cognitive impairment in adults aged 55–85 years with and without obstructive sleep apnoea.We included 42 subjects with mild and 67 subjects with moderate-to-severe obstructive sleep apnoea. We compared them to 22 control subjects. Mild cognitive impairment was diagnosed by a comprehensive neuropsychological assessment. We used receiver operating characteristic curves to assess the ability of the two screening tests to detect mild cognitive impairment.The two screening tests showed similar discriminative ability in control subjects. However, among the mild and the moderate-to-severe obstructive sleep apnoea groups, the Mini-Mental State Examination was not able to correctly identify subjects with mild cognitive impairment. The Montreal Cognitive Assessment's discriminant ability was acceptable in both sleep apnoea groups and was comparable to what was observed in controls.The Mini-Mental State Examination should not be used to screen for cognitive impairment in patients with obstructive sleep apnoea. The Montreal Cognitive Assessment could be used in clinical settings. However, clinicians should refer patients for neuropsychological assessment when neurodegenerative processes are suspected.

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Comparison of the Mini-Mental State Examination and Computerized Brief Cognitive Screening Test as Cognitive Screening Tools in Patients with Mild Cognitive Impairment

Current Alzheimer Research ◽

10.2174/1567205018666211215151418 ◽

2021 ◽

Vol 18 ◽

Author(s):

Che-Sheng Chu ◽

I-Chen Lee ◽

Chuan-Cheng Hung ◽

I-Ching Lee ◽

Chi-Fa Hung ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Normal Control ◽

Internal Consistency ◽

Mental State ◽

Screening Test ◽

Mini Mental State Examination ◽

Cognitive Screening ◽

Validity And Reliability ◽

State Examination

Background: The aim of this study was to establish the validity and reliability of the Computerized Brief Cognitive Screening Test (CBCog) for early detection of cognitive impairment. Method: One hundred and sixty participants, including community-dwelling and out-patient volunteers (both men and women) aged ≥ 65 years, were enrolled in the study. All participants were screened using the CBCog and Mini-Mental State Examination (MMSE). The internal consistency of the CBCog was analyzed using Cronbach’s α test. Areas under the curves (AUCs) of receiver operating characteristic analyses were used to test the predictive accuracy of the CBCog in detecting mild cognitive impairment (MCI) in order to set an appropriate cutoff point. Results: The CBCog scores were positively correlated with the MMSE scores of patients with MCI-related dementia (r = 0.678, P < .001). The internal consistency of the CBCog (Cronbach’s α) was 0.706. It was found that the CBCog with a cutoff point of 19/20 had a sensitivity of 97.5% and a specificity of 53.7% for the diagnosis of MCI with education level ≥ 6 years. The AUC of the CBCog for discriminating the normal control elderly from patients with MCI (AUC = 0.827, P < 0.001) was larger than that of the MMSE for discriminating the normal control elderly from patients with MCI (AUC= 0.819, P < .001). Conclusion: The CBCog demonstrated to have sufficient validity and reliability to evaluate mild cognitive impairment, especially in highly educated elderly people.

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