. Neuropsychological tests are specifically designed tasks used to measure a psychological function known to be linked to a particular brain structure or pathway.
Tests are used for into brain function and in a setting for the diagnosis of deficits. They usually involve the systematic administration of clearly defined procedures in a formal environment.
Neuropsychological tests are typically administered to a single person working with an examiner in a quiet office environment, free from distractions. As such, it can be argued that neuropsychological tests at times offer an estimate of a person's peak level of cognitive performance.
Neuropsychological tests are a core component of the process of conducting, along with personal, interpersonal and contextual factors. Most neuropsychological tests in current use are based on traditional theory.
In this model, a person's on a test is compared to a large general population sample, that should ideally be drawn from a comparable population to the person being examined. Install deb package puppy linux live cd. Normative studies frequently provide data stratified by age, level of education, and/or ethnicity, where such factors have been shown by research to affect performance on a particular test. This allows for a person's performance to be compared to a suitable, and thus provide a fair assessment of their current cognitive function. According to Larry J. Seidman, the analysis of the wide range of neuropsychological tests can be broken down into four categories.
First is an analysis of overall performance, or how well people do from test to test along with how they perform in comparison to the average score. Second is left-right comparisons: how well a person performs on specific tasks that deal with the left and right side of the body. Third is pathognomic signs, or specific test results that directly relate to a distinct disorder. Finally, the last category is differential patterns, which are strange test scores that are typical for specific diseases or types of damage.
Contents. Categories Most forms of cognition actually involve multiple cognitive functions working in unison, however tests can be organised into broad categories based on the cognitive function which they predominantly assess.
Some tests appear under multiple headings as different versions and aspects of tests can be used to assess different functions. Intelligence testing in a research context is relatively more straightforward than in a clinical context. In research, intelligence is tested and results are generally as obtained, however in a clinical setting intelligence may be impaired so estimates are required for comparison with obtained results. Estimates can be determined through a number of methods, the most common include: comparison of test results to expected achievement levels based on prior education and occupation and the use of which are based on cognitive faculties which are generally good indicators of intelligence and thought to be more resistant to cognitive damage, e.g. Memory is a very broad function which includes several distinct abilities, all of which can be selectively impaired and require individual testing. There is disagreement as to the number of memory systems, depending on the psychological perspective taken.
From a clinical perspective, a view of five distinct types of memory, is in most cases sufficient. Semantic memory and episodic memory (collectively called or explicit memory); procedural memory and priming or perceptual learning (collectively called or implicit memory) all four of which are long term memory systems; and working memory or short term memory. Is memory for facts, is autobiographical memory, is memory for the performance of skills, is memory facilitated by prior exposure to a stimulus and is a form of short term memory for information manipulation. Cambridge Prospective Memory Test (CAMPROMPT). Memory Assessment Scales (MAS).
Rey Auditory Verbal Learning Test. Rivermead Behavioural Memory Test. Test of Memory and Learning (TOMAL). Mental Attributes Profiling System. (TOMM) Language Language functions include speech, reading and writing, all of which can be selectively impaired. Multilingual Aphasia Examination Executive function is an umbrella term for a various cognitive processes and sub-processes. The executive functions include: problem solving, planning, organizational skills, selective attention, inhibitory control and some aspects of short term memory.
Behavioural Assessment of Dysexecutive Syndrome (BADS). CNS Vital Signs (Brief Core Battery). Continuous performance task.
Controlled Oral Word Association Test (COWAT). Digit Vigilance Test.
Figural Fluency Test. Halstead Category Test. Kaplan Baycrest Neurocognitive Assessment (KBNA).
Kaufman Short Neuropsychological Assessment. Ruff Figural Fluency Test. Symbol Digit Modalities Test.
(TEA) Visuospatial Neuropsychological tests of visuospatial function should cover the areas of visual perception, visual construction and visual integration. Though not their only functions, these tasks are to a large degree carried out by areas of the.
Clock Test. Hooper Visual Organisation Task (VOT).
Dementia specific testing is often done by way of testing the cognitive functions that are most often impaired by the disease e.g. Memory, orientation, language and problem solving. Tests such as these are by no means conclusive of deficits, but may give a good indication as to the presence or severity of dementia. Dementia Rating Scale Batteries assessing multiple neuropsychological functions There are some test batteries which combine a range of tests to provide an overview of cognitive skills.
These are usually good early tests to rule out problems in certain functions and provide an indication of functions which may need to be tested more specifically. Barcelona Neuropsychological Test (BNT). Cognitive Assessment Screening Instrument (CASI).
Hooper Visual Organization Test. MicroCog.
Benefits of Neuropsychological Testing The most beneficial factor of neuropsychological assessment is that it provides an accurate diagnosis of the disorder for the patient when it is unclear to the psychologist what exactly he/she has. This allows for accurate treatment later on in the process because treatment is driven by the exact symptoms of the disorder and how a specific patient may react to different treatments. The assessment allows the psychologist and patient to understand the severity of the deficit and to allow better decision-making by both parties. It is also helpful in understanding deteriorating diseases because the patient can be assessed multiple times to see how the disorder is progressing. See also.
References.
Abstract Importance Dementia is a global public health problem. The Mini-Mental State Examination (MMSE) is a proprietary instrument for detecting dementia, but many other tests are also available.
Objective To evaluate the diagnostic performance of all cognitive tests for the detection of dementia. Data Sources Literature searches were performed on the list of dementia screening tests in MEDLINE, EMBASE, and PsychoINFO from the earliest available dates stated in the individual databases until September 1, 2014.
Because Google Scholar searches literature with a combined ranking algorithm on citation counts and keywords in each article, our literature search was extended to Google Scholar with individual test names and dementia screening as a supplementary search. Study Selection Studies were eligible if participants were interviewed face to face with respective screening tests, and findings were compared with criterion standard diagnostic criteria for dementia.
Bivariate random-effects models were used, and the area under the summary receiver-operating characteristic curve was used to present the overall performance. Main Outcomes and Measures Sensitivity, specificity, and positive and negative likelihood ratios were the main outcomes. Results Eleven screening tests were identified among 149 studies with more than 49 000 participants. Most studies used the MMSE (n = 102) and included 10 263 patients with dementia.
Brief Cognitive Assessment Tool Pdf
The combined sensitivity and specificity for detection of dementia were 0.81 (95% CI, 0.78-0.84) and 0.89 (95% CI, 0.87-0.91), respectively. Among the other 10 tests, the Mini-Cog test and Addenbrooke’s Cognitive Examination–Revised (ACE-R) had the best diagnostic performances, which were comparable to that of the MMSE (Mini-Cog, 0.91 sensitivity and 0.86 specificity; ACE-R, 0.92 sensitivity and 0.89 specificity). Subgroup analysis revealed that only the Montreal Cognitive Assessment had comparable performance to the MMSE on detection of mild cognitive impairment with 0.89 sensitivity and 0.75 specificity. Conclusions and Relevance Besides the MMSE, there are many other tests with comparable diagnostic performance for detecting dementia. The Mini-Cog test and the ACE-R are the best alternative screening tests for dementia, and the Montreal Cognitive Assessment is the best alternative for mild cognitive impairment.
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Introduction Early diagnosis of dementia can identify people at risk for complications. Previous studies, have found that health care professionals commonly miss the diagnosis of cognitive impairment or dementia; the prevalence of missed diagnosis ranges from 25% to 90%. Primary care physicians may not recognize cognitive impairment until the moderate to severe stage.
Screening tests are quick and useful tools to assess the cognitive condition of patients. The Mini-Mental State Examination (MMSE) is the most widely applied test for dementia screening. Search Strategy A list of screening tests was identified in previous systematic reviews., Literature searches were performed on the list of dementia screening tests in MEDLINE, EMBASE, and PsychoINFO from the earliest available dates stated in the individual databases until September 1, 2014. Each screening test was separately searched with general keywords of dementia, including Alzheimer, Parkinson, vascular, stroke, cognitive impairment, and dementia.
Diagnostic studies comparing accuracy of screening tests for detection of dementia were manually identified from the title or abstract preview of all search records. The selection was limited to peer-reviewed articles published in English abstracts. Because Google Scholar searches literature with a combined ranking algorithm on citation counts and keywords in each article, our literature search was extended to Google Scholar with individual test names and dementia screening as a supplementary search.
The first 10 pages of all search records were scanned. Manual searches were extended to the bibliographies of review articles and included research studies. Screening tests were classified into different categories according to the administration time: 5 minutes or less, 10 minutes or less, and 20 minutes or less. Data Extraction Two investigators (J.Y.C.C., H.W.H.) independently assessed the relevancy of search results and abstracted the data into a data extraction form.
This form was used to record the demographic details of individual articles, including year of publication, study location, number of participants included, mean age of participants, percentage of male participants, type of dementia, recruitment site, number of participants with dementia or mild cognitive impairment (MCI), diagnostic criteria, cutoff values, sensitivity, specificity, and true-positive, false-positive, true-negative, and false-negative likelihood ratios. When a study reported results of sensitivity and specificity across different cutoff values of a screening test, only the results from a recommended cutoff by the authors of the article were selected.
If the study did not have this recommendation, the cutoff used to summarize sensitivity and specificity in the abstract was chosen. When discrepancies were found regarding inclusion of studies or data extraction, the third investigator (K.K.F.T.) would make the definitive decision for study eligibility and data extraction. Risk of Bias and Study Quality Potential risks of bias in each screening test were evaluated by the Quality Assessment of Diagnostic Accuracy Studies 2 instrument, which evaluated patient selection, execution of the index test and the reference standard, and flow of patients.
All high risk of bias was counted in an Excel worksheet (Microsoft Inc) and presented as a percentage in each screening test. The quality of study was also assessed according to the methods section of the Standards for Reporting of Diagnostic Accuracy statement. An 8-point scale was designed for the evaluation of study quality, including description of the following: (1) study population, (2) participant recruitment, (3) sampling of participant selection, (4) data collection plan, (5) reference standard and its rationale, (6) technical specifications, (7) rationale for units and cutoffs, and (8) methods for calculating diagnostic accuracy with CIs.
This quality score was presented as median and range across the screening tests. Data Synthesis and Statistical Analysis Statistical analyses were performed with the Metandi and Midas procedures in STATA statistical software, version 11 (StataCorp). The overall sensitivity and specificity of each diagnostic test were pooled using a bivariate random-effects model. Forest plots were used to graphically present the combined sensitivity and specificity.
The accuracy of a screening test had to allow trade-off between sensitivity and specificity that occurs when different threshold values were used to define positive and negative likelihood ratios of the tests. Therefore, a diagnostic odds ratio was used as a single indicator of test performance. In addition, a hierarchical summary receiver-operating characteristic (HSROC) curve was generated to present the summary estimates of sensitivities and specificities along with 95% CIs and prediction region. The area under the curve (AUC) for the HSROC was also calculated, and an area between 0.9 and 1.0 indicated that the diagnostic accuracy was good. When the Hessian matrix of bivariate approach was unstable or asymmetric, a random-effects model following the approach of DerSimonian and Laird was applied to estimate the pooled sensitivity and specificity, and a summary receiver-operating characteristic (SROC) curve was generated to present the summary estimates of sensitivities and specificities with an AUC for SROC presented as a summary statistic., Statistical heterogeneity among the trials was assessed by I 2, which describes the percentage of total variation across studies due to the heterogeneity rather than the chance alone. Literature Search and Study Selection A total of 26 165 abstracts were identified from the databases, and 215 potential studies were further extracted from the bibliographies.
All titles or abstracts were screened, and 346 articles were relevant to screening tools for dementia. One hundred ninety-seven were excluded for the following reasons: studies were systematic reviews (n = 30), studies did not fulfill inclusion criteria (n = 121), studies lacked data details for meta-analysis (n = 39), and studies reported results of screening tests without comparing to a criterion standard (n = 7). The definitive analysis in this systematic review included 149 studies published from 1989 until September 1, 2014, for patients with dementia from the United States, the United Kingdom, Canada, and 30 other countries. Study Characteristics A total of 149 studies with more than 40 000 patients across the 11 screening tests were included. One hundred ten eligible studies (73.8%) reported the diagnostic performances of at least 2 screening tests, including those compared with the MMSE.
Approximately 12 000 participants were confirmed as having dementia. Most studies (68.5%) used the MMSE as the screening test for dementia in 29 regions.
The next most common screening test studied was the MoCA, which was used in 20 studies (13.4%) from 9 countries. Patients were mainly recruited from community or clinic settings (80.3%). One hundred ten (73.8%) of 149 studies had good study quality with quality scores of 7 to 8.
The quality scores were comparable across the 11 screening tests with median scores of approximately 7 (range, 3-8). The original data of each study on the true-positive, false-positive, false-negative, and true-negative likelihood ratios were presented (eTable 2 in the ).
Furthermore, risks of bias were not identified among these studies, and only the studies for the GPCOG, MoCA and modified MMSE revealed approximately 20% to 30% high risks of bias on execution for the index test and the reference standard. Diagnostic Accuracy of the MMSE There were 10 263 cases of dementia identified from 36 080 participants in 108 cohorts studying the MMSE. The most common cutoff values to define participants with dementia were 23 and 24, used in 48 cohorts (44.4%).
With different cutoff threshold values, we found considerable variation in the sensitivity and specificity estimates reported by individual studies. The sensitivities ranged from 0.25 to 1.00, and the specificities ranged from 0.54 to 1.00. The heterogeneity among studies was large, with I 2 statistics for sensitivity and specificity of 92% and 94%, respectively. The diagnostic accuracy is summarized by meta-analysis. The combined data in the bivariate random-effects model gave a summary point with 0.81 sensitivity (95% CI, 0.78-0.84) and 0.89 specificity (95% CI, 0.87-0.91).
The HSROC curve was plotted with a diagnostic odds ratio of 35.4, and the AUC was 92% (95% CI, 90%-94%) (eFigure 1 in the ). Diagnostic Accuracy of Other Screening Tests The performances of the other 10 screening tests were summarized by random-effects models. All tests presented with AUCs of at least 85%, and most of the tests had comparable performance to that of the MMSE. The Mini-Cog test and the ACE-R were the best alternative tests. Among the studies with the Mini-Cog test, - the pooled sensitivity was 0.91 (95% CI, 0.80-0.96), and the pooled specificity was 0.86 (95% CI, 0.74-0.93) (A). The heterogeneity among studies was large, with I 2 statistics for sensitivity and specificity of 89% and 97%, respectively.
Among studies that used the ACE-R, - the pooled sensitivity was 0.92 (95% CI, 0.90-0.94) and the pooled specificity was 0.89 (95% CI, 0.84-0.93) (B). The confidence regions of the HSROC curves for sensitivity and specificity of the Mini-Cog test and the ACE-R were plotted with reference to the HSROC curve of the MMSE (eFigure 2 in the ). Studies of the MMSE Only the MMSE had a sufficient number of studies to perform subgroup analysis.
For the geographic regions, studies were conducted in Europe (44.4%), Americas (31.5%), and Asia (23.1%). The diagnostic performances of the MMSE were comparable across these regions with similar AUCs (eFigure 2 in the ).
For the recruitment settings, participants were recruited in hospital (9.3%), clinic (32.4%), primary care (12.0%), community (38.9%), and other settings (7.4%). The diagnostic performances were comparable across different recruitments settings ( P.05 for all) (eTable 3 in the ). Patients With MCI Only 21 of 108 cohorts reported diagnostic performance of the MMSE for the detection of MCI. The combined data gave a summary point of 0.62 sensitivity (95% CI, 0.52-0.71) and 0.87 specificity (95% CI, 0.80-0.92). Nine of 20 studies reported diagnostic performance of the MoCA for the detection of MCI., - The combined data gave a summary point of 0.89 sensitivity (95% CI, 0.84-0.92) and 0.75 specificity (95% CI, 0.62-0.85) (C). The confidence regions of the HSROC curve for sensitivity and specificity of the MoCA were plotted with reference to the HSROC curve of the MMSE (eFigure 3 in the ). Discussion This systematic review and meta-analysis included 149 studies that assessed the accuracy of the MMSE and 10 other screening tests for the detection of dementia.
Compared with other screening tests, the Mini-Cog test and the ACE-R had better diagnostic performance for dementia, and the MoCA had better diagnostic performance for MCI. The Mini-Cog test is relatively simple and short compared with the MMSE. In a previous meta-analysis, Mitchell combined 34 diagnostic studies to evaluate the accuracy of the MMSE, but he only combined the sensitivity and specificity without mentioning the methodologic details. Mitchell and Malladi, also published 2 meta-analyses that included 45 studies to compare diagnostic performance of single-domain and multidomain tests. They found that 15 brief single-domain tests were less accurate than that of the MMSE in detecting dementia in community and primary care settings.
These studies used an uncommon approach of Bayesian curve modeling, instead of using the ROC curve to evaluate the diagnostic performance of the tests. A systematic review reported a combined diagnostic accuracy of the MMSE and summarized the sensitivity and specificity ranges of 10 other screening tests, but the literature search was limited to studies from systematic reviews conducted in primary care settings. In some other studies, dementia screening was performed in secondary or tertiary care settings. Therefore, the review combined only 14 studies with 10 185 participants using the MMSE as the screening test. The lack of a precise estimate of sensitivity has resulted in confusion among health care professionals to apply the MMSE for dementia screening. In our meta-analysis, we tried to make our findings more comprehensive, using publications from all possible sources, and included 102 studies with 36 080 participants to evaluate the diagnostic performance of the MMSE.
![Brief Brief](/uploads/1/2/3/9/123950694/355389366.jpg)
The results reported a sensitivity of 0.81 and a specificity of 0.89 for the MMSE. The diagnostic performance of MMSE is good because the AUC was 92%. Diagnostic sensitivity improves with lower cutoff values but with a corresponding decrease in specificity. High sensitivity corresponds to high negative predictive value and is the ideal to rule out dementia. We found considerable variation on the definitions of cutoff thresholds among the individual studies. According to our selection criteria, the most common cutoff scores for the MMSE for dementia were 23 and 24 (44.4% study cohorts), and approximately 20% of eligible cohorts used cutoff scores of 25 to 26 (range, 17-28). The range of scores for the Mini-Cog test is similarly 0 to 5, and 7 cohorts (77.8%) used a score of less than 3 as the cutoff for dementia, indicating disagreement on the optimal cutoff score across different screening tests.
The users of screening tests should strike a balance between sensitivity and specificity to rule in or out the participants with dementia according to the available resources. This study has several limitations. First, the screening tests were not directly compared in the same populations. Each study used different populations, and the inclusion criteria and prevalence of dementia varied. It would be preferable to directly compare screening tests using the same group of participants with similar educational levels. Second, only a few studies were included that showed head-to-head comparison between the screening tests, so the test performance could not be directly compared.
Third, the screening tests were translated into different languages, which may have unknown effects on the results. We assumed that the tests were all validated in various languages in the individual studies although this was not guaranteed, and unidentified cultural effects on the use of screening tests may still exist. Fourth, we only included studies that reported the diagnostic performance of screening tests for dementia. Although we used MCI as a secondary outcome, the definitions of MCI were heterogeneous across studies. Studies that only reported the results of MCI or cognitive impairment but not dementia (cognitive impairment no dementia) were not included in this meta-analysis. Finally, some unpublished studies may not have been identified through the literature search in OVID databases, and publication bias may exist.
Conclusions This review systematic and meta-analysis found that the MMSE is the most frequently studied test for dementia screening. However, many other screening tests have comparable diagnostic performance. The Mini-Cog test and the ACE-R had better performance than the other dementia screening tests. The MoCA had better performance than the other MCI screening tests. Although the MMSE is a proprietary instrument for dementia screening, the other screening tests are comparably effective but easier to perform and freely available. Article Information Accepted for Publication: April 8, 2015.
Corresponding Author: Timothy C. Kwok, MD, PhD, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, 9/F, Clinical Sciences Building, Prince of Wales Hospital, Ngan Shing Street, Shatin, Hong Kong. Published Online: June 8, 2015. Author Contributions: Dr Tsoi had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Tsoi, Kwok. Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Tsoi. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Tsoi, Hirai. Administrative, technical, or material support: Chan, Hirai.
Study supervision: Tsoi, Wong, Kwok. Conflict of Interest Disclosures: Dr Kwok reported lecturing in workshops sponsored by Lundbeck and Novartis and receiving donations from Novartis for a website for family caregivers of dementia. No other disclosures were reported.