Convergent Validity of the Cognitive Performance Scale of the interRAI Acute Care and the Mini-Mental State Examination

Abstract. Nurses’ inherently stressful occupation leaves them at a higher risk of developing negative mental states (stress, anxiety, and depression). However, research examining the effect of negative mental states on these health professionals’ cognitive performance is sparse. Thus, the present study aimed to assess the link between negative mental states and cognitive performance in nurses ( n = 53). Negative mental state data was obtained using the Depression Anxiety Stress Scale, brain activity was measured using electroencephalography, and finally, cognitive performance was assessed using the Cognistat and the Mini-Mental State Examination. Significant negative correlations ( p < .05) were observed between anxiety and attention, and all three negative mental states and memory performance. Electroencephalographic changes indicated that increases in anxiety were significantly associated ( p < .05) with decreases in gamma reactivity at fronto-central sites. The current study suggests that higher levels of negative mental states are associated with domain-specific cognitive impairments, and variations in gamma reactivity; possibly reflecting less optimal cortical functioning.

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Machine Learning Algorithms in the Prediction of Early Death in Elderly Cancer Patients (Preprint)

10.2196/preprints.13528 ◽

2019 ◽

Author(s):

Gabrielle Sena ◽

Tiago Pessoa Lima ◽

Jurema Telles Lima ◽

Maria Julia Mello ◽

Luiz Claudio Thuler

Keyword(s):

Machine Learning ◽

Cancer Patients ◽

Mental State ◽

Mini Mental State Examination ◽

Early Death ◽

Machine Learning Algorithms ◽

Karnofsky Performance Scale ◽

Elderly Cancer Patients ◽

State Examination ◽

Performance Scale

BACKGROUND The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of Machine Learning (ML) algorithms. The International Society of Geriatric Oncology (SIOG) recommends the use of the Comprehensive Geriatric Assessment (CGA), a multidisciplinary tool to evaluate health domains, for the follow-up of elderly cancer patients. However, none ML application have been proposed using CGA to classify elderly cancer patients. OBJECTIVE To propose and develop predictive models, using ML and CGA, to estimate the risk of early death in elderly cancer patients. METHODS The ability of ML algorithms to predict early mortality in a cohort involving 608 elderly cancer patients was evaluated. The CGA was conducted during admission by a multidisciplinary team and included the following questionnaires: Mini-Mental State Examination, Geriatric Depression Scale, International Physical Activity Questionnaire, Timed Get-Up and Go, Katz Index, Charlson Comorbidity Index, Karnofsky Performance Scale, Polypharmacy, Mini Nutritional Assessment. The K-fold Cross Validation algorithm was used to evaluate all possible combinations of these questionnaires to estimate the risk of early death, considered when occurring within six months of diagnosis, in a variety of ML classifiers, including Naive Bayes (NB), Decision Tree (J48) and Multilayer Perceptron (MLP). On each fold of evaluation, tie-breaking is handled by choosing the smallest set of questionnaires. RESULTS It was possible to select CGA questionnaire subsets with high predictive capacity for early death, either statistically similar (NB) or higher (J48 and MLP) compared to the use of all questionnaires investigated. These results show that CGA questionnaire selection can improve accuracy rates and decrease the time spent to evaluate elderly cancer patients. The only questionnaire selected in all folds was the Mini Nutrition Evaluation. The Karnofsky Performance Scale was selected in all folds by the NB and MLP, while the Mini Mental State Examination was selected in all folds by the NB. CONCLUSIONS A simplified predictive model aiming to estimate the risk of early death in elderly cancer patients is proposed herein, minimally composed by the Mini Nutrition Evaluation accompanied or not by the Karnofsky Performance Scale and/or the Mini-Mental State Examination.

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Abnormality in saccadic eye movement in dementia

European Psychiatry ◽

10.1016/s0924-9338(99)80716-0 ◽

1999 ◽

Vol 14 (1) ◽

pp. 52-53 ◽

Cited By ~ 15

Author(s):

H.J. Schewe ◽

R. Uebelhack ◽

K. Vohs

Keyword(s):

Vascular Dementia ◽

Eye Movement ◽

Cognitive Performance ◽

Mental State ◽

High Frequency ◽

Mini Mental State Examination ◽

Saccadic Eye Movement ◽

Alzheimer Type ◽

Saccadic Intrusions ◽

State Examination

SummaryIn a small (n = 15) clinically heterogeneous group of patients with dementia of the Alzheimer-type or vascular dementia, abnormally high frequency of saccadic intrusions during fixation was significantly correlated to the Mini-Mental-State-Examination (MMSE) scores. In addition, the latency of saccades and hypometric saccades also correlated significantly to MMSE-scores. The results point to the possible use of saccadic eye movement as a physiological marker of cognitive performance.

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The effect of duration of dependence and daily dose of tramadol in tramadol dependent patients on cognitive performance

Middle East Current Psychiatry ◽

10.1186/s43045-019-0005-8 ◽

2019 ◽

Vol 26 (1) ◽

Author(s):

Hossam Eddin Khalifa Ahmad ◽

Alaa Eldin Mohamed Darweesh ◽

Shehab Hassan Mahmoud Hassaan ◽

Mostafa Nooman ◽

Islam Shaaban ◽

...

Keyword(s):

Cognitive Performance ◽

Negative Correlation ◽

Mental State ◽

Mini Mental State Examination ◽

Significant Negative Correlation ◽

Adult Intelligence Scale ◽

Wechsler Adult Intelligence Scale ◽

Daily Dose ◽

Wechsler Adult Intelligence ◽

State Examination

Abstract Background Tramadol dependence represents a major medical and legal hazardous phenomenon in the last decade. It is a synthetic opiate analgesic which exerts its therapeutic effect by its action on μ opioid receptors. It has a weak dependence ability. The present study investigated the effect of duration of dependence and daily dose of tramadol on cognitive performance. Cognitive functions were assessed using the following: the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA) test, Brief Visuospatial Memory Test–Revised (BVMT-R), Wechsler Adult Intelligence Scale-Third Edition (WAIS-III), the P300 (ERP), and conventional electroencephalogram. Results There was a non-significant negative correlation between the daily dose of tramadol and cognitive performance as regards IQ, Mini-Mental State Examination, MoCA score, P300 reaction time (μs), and deterioration index (r = − 0.08, P = 0.689; r = − 0.02, P = 0.896; r = − 0.11, P = 0.554; r = − 0.11, P = 0.581, r = − 0.17; P = 0.368, respectively). Additionally, the results showed non-significant negative correlation between the duration of dependence and the cognitive performance (r = − 0.19, P = 0.325; r = − 0.15, P = 0.424; r = − 0.30, P = 0.108; r = − 0.02, P = 0.909; r = − 0.02, P = 0.937, respectively). Conclusion Daily dose and duration of tramadol dependence have a negative but non-significant effect on cognitive performance.

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