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|Year : 2019
: 61 | Issue : 2 | Page
|Short test of mental status in the detection of mild cognitive impairment in India
Sreerupa Ghose1, Sanjukta Das1, Swarup Poria2, Tapolagna Das3
1 Department of Psychology, University of Calcutta, Kolkata, West Bengal, India
2 Department of Applied Mathematics, University of Calcutta, Kolkata, West Bengal, India
3 Clinical Psychology Centre of University of Calcutta, Kolkata, West Bengal, India
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|Date of Web Publication||11-Mar-2019|
| Abstract|| |
Context: Mild cognitive impairment (MCI) is an under-diagnosed health problem in the community. Cognitive screening tools are widely used for MCI detection, but many of them lack sensitivity and specificity in MCI detection. On the basis of literature review, Short test of mental status (STMS) was selected for the present purpose.
Aims: The present study purports to standardize STMS for using as a cognitive screening tool in MCI detection in the community-living elderly people in Kolkata.
Materials and Methods: Data were collected from 102 community-living elderly people from the city of Kolkata using the purposive method of sampling. MCI was diagnosed using the Peterson's criteria of MCI as the gold standard. A semi-structured demographic proforma, clock-drawing test (CDT), and the Groningen Activity Restriction Scale (GARS) were used for the purpose. Finally, STMS was administered.
Statistical Analysis: Statistical computation was done using the SPSS 21. Descriptive statistics, receiver operating curve analysis, and binary logistic regression were used for statistical analysis of the data.
Results and Conclusion: STMS emerged as a sensitive and specific cognitive screening tool in the detection of MCI in the current cultural setting. It was also found to be more suited for the purpose than CDT. A score of 34.5 with a sensitivity of 97.5 and a specificity of 90.3% were selected for the optimum cutoff score for the detection of MCI in the present population. With a unit increase in STMS score, the odds of getting diagnosed with MCI were found to be reduced by 48.5%.
Keywords: Cognitive screening test, India, mild cognitive impairment
|How to cite this article:|
Ghose S, Das S, Poria S, Das T. Short test of mental status in the detection of mild cognitive impairment in India. Indian J Psychiatry 2019;61:184-91
| Introduction|| |
Past two decades have seen rise in the interest in the disorders related to cognitive degeneration. An increasing interest on predementia clinical features of the cognitive degenerative disorders is on the rise with the hope that in the near future the earliest symptoms of the disorder will be evident long before the gross functional impairment becomes manifest. This has lead to the emergence of the construct of mild cognitive impairment (MCI) which purports to describe the predementia phase of the cognitive dysfunction. The term MCI was introduced by Reisberg et al., in 1988, and the first diagnostic criteria of MCI was published in the Archives of Neurology in 1999. These criteria were examined, criticized, and validated, and there has been a rapid increase in the literature in the field worldwide resulting in the modification of the diagnostic criteria a few times. The research in the field is still considered to be in its nascent phase and it is the source of one of the principal interest points of the clinical professionals and researchers working in the field of dementia.
Although variations in definitions have been noted, MCI, in brief, refers to a syndrome with greater cognitive decline in comparison to an individual's age and education but exhibits no notable decline in activities of daily life (ADL). Individuals with MCI have variable and subtle cognitive changes. MCI symptoms include almost normal general impairment in cognitive functioning (thinking, understanding, and decision-making), near normal ADL, little or no changes in personality, subtle memory impairment as well as deterioration in other cognitive domains, and decision-making. MCI diagnostic criteria proposed by an international working committee on MCI include the following four conditions for MCI diagnosis: “(i) The person is neither normal nor demented; (ii) There is an evidence of cognitive deterioration shown by either objectively measured decline over time and/or subjective report of decline by self and/or informant in conjunction with objective cognitive deficits; and (iii) The ADL are preserved, and the complex instrumental functions are either intact or minimally impaired.”
As stated above, this rapid growth in the interest in the MCI research is the consequence of the recognition of the importance of early detection of predementia symptoms. The early identification of MCI is necessary principally because of three reasons: (1) Certain features of MCI overlap with irreversible and progressive dementing illnesses-like Alzheimer's disease (AD) (2) There is also a higher likelihood of progression from MCI to AD. (3). MCI is also difficult to differentiate from normative age-related cognitive changes posing a difficulty in differential diagnosis. In India, growing interest in MCI research is becoming apparent.,,
The necessity for detection of the cognitive degenerative disease process at an early-stage has also lead to the development of different diagnostic methods. Like other disorders with neurocognitive features, the diagnosis of MCI requires validating the clinical diagnosis with neuroimaging and neuropsychological findings. However, detailed neuropsychological testing is difficult to conduct due to monetary and time constraint. Hence, the use of screening tests is largely popular in the diagnosis of degenerative cognitive impairment due to its brevity and the chance of bedside application in the patient population. In the community studies, screening test applications are preferred for the same reason. The most widely used tool is the Mini-Mental Status Examination (MMSE). Several previous researches have showed that MMSE lacks sensitivity in detecting the early signs of dementia and present ceiling effect, resulting in false-negative diagnosis mostly., Hence, in spite of its wide use MMSE has been criticized and other cognitive screening tests are being developed in accordance with particular clinical and research requirement. Some of the tests that are widely used all over the world are the Montreal Cognitive Assessment (MoCA), Addenbrooke's Cognitive Examination, Short Portable Mental Status Questionnaire, and Short Test of Mental Status (STMS). A search of existing literature shows that STMS, developed by Kokmen et al., is short yet comprehensive and shows higher sensitivity in MCI detection; thus, STMS was selected for the present purpose. STMS includes subtests that try to measure and screen for most of major cognitive functions that includes: orientation to self, time and place, attention, learning, memory, visuoconstructional ability, and executive functioning. STMS includes seven subtests; these subtests are orientation, attention, learning, arithmetic calculation, abstraction, information, construction, and recall.
Keeping in mind that MCI is a significant health problem under recognized and under diagnosed in the community; the present study purports to standardize STMS for using as a cognitive screening tool in the community-living elderly people in Kolkata. It also purports to compare the effectiveness of STMS as a screening tool with the clock-drawing test (CDT), as CDT is also widely used as a cognitive screening tool for assessing cognitive functioning and screening for dementia, especially in the primary care setting., The specific aims of the present study are summarized below:
- To assess the optimum cutoff score of STMS against Petersen's criteria (as gold standard) to detect MCI in a sample of Indian elderly patients from Kolkata
- To compare the effectiveness of STMS in relation to CDT in the detection of undetected MCI in the community-living elderly population
- To assess the relationship of STMS scores to the risk of getting diagnosed with MCI.
| Materials and Methods|| |
The present study has been conducted on a sample of 102 community-living elderly people within the age group between 60 and 80 years with a minimum education of 10 years. All the cases have been selected from the upper middle class community-living male and female elderly people living within the jurisdiction of the city of Kolkata. Incidentally, all the cases selected belonged to the Bengali community. In the study, initially, we did not focus on the Bengali community of Kolkata only and hence, other communities were also approached. Consent was obtained from some families, and a few data were also collected from different communities (e.g., Gujarati) other than Bengali. However, at the end of the study, those data were screened out during analysis as the scores of STMS were very low and indicative of the presence of dementing illness. Hence, ultimately, the data included in the study were exclusively on Bengali elderly people. In future, the variation in cut-off score of STMS in other provincial communities living in Kolkata will be investigated). The study was commenced after the design was duly approved by the University Committee of Institutional Research Ethics. Cases with a history suggestive of the current or past psychiatric illness, presence of any physical and mental disability, congenital conditions or life-threatening illness (any kind of terminal illness and complicated medical conditions, e.g., diabetes nephropathy) were excluded from the study; cases were selected using the purposive method of sampling. Specifically, the inclusion and exclusion criteria of the study are presented as follows:
- Community-living male and female elderly people
- Aged between 60 and 80 years
- Minimum 10 years of education.
- History suggestive of current or past psychiatric illness
- Presence of any physical and mental disability
- Presence of congenital conditions
- Presence of terminal illness.
Semi structured Performa
Sociodemographic schedule has been used for obtaining the sociodemographic details as well as the past and present clinical status. The sociodemographic form attempted to gain information about the participants' name, age, sex, education, marital status, occupation, family type, family income, the presence of significant life event in the past 6 months, and medical history.
Short test of mental status
STMS developed by Emre Kokmen, James Naessens, and Kenneth Offord in 1987 in the Department of Neurology, Mayo Clinic, Rochester, New York, consists of eight subtests: orientation, attention, calculation, abstraction, information, construction, and recall. Orientation subtest of STMS enquires about: (1) Full name, (2) Address, current location, that is, (3) Building, (4) City, and (5) State and the current date, (6) Either the day of the week or the day of the month, (7) The month, and (8) The year. Attention subtest consists of forward digit span (minimum = 4; maximum = 7). Learning subtest requires learning four items through a maximum of four trials. Arithmetic calculation contains one item each for the functions: addition, subtraction, multiplication, and division. Abstraction subtest requires interpretation of similarities. Information subtest seeks general information about miscellaneous areas. Construction subtest requires a free clock-drawing task and a cube-copying task. Recall subtests require remembering the four words learnt previously. The test requires giving general instruction to the patient initially and specific instruction is required for each subtest.
Kokmen et al. found that a threshold of 29 points (with sensitivity values of 90% or higher) was useful in classifying dementia patients from the nondemented individuals. Kokmen et al. found a score of 29 on STMS yielded a specificity of 91.4 and 95.5 in distinguishing 67 patients with Alzheimer's–Dementia from 93 neurology outpatient department patients without dementia; Again in a sample of 87 patients with dementia and 93 patients without dementia, a score of 29 on STMS yielded a sensitivity of 92.0 and a specificity of 91.4. In another analysis, it was found that in order to distinguish 76 patients with dementia and 33 patients without dementia all of whom were 60 years of age or older a score of 29 yielded a sensitivity of 94.7 and specificity of 87.9.
Clock drawing test
CDT was used as an objective measure to detect MCI according to the gold standard. Here, CDT was not administered separately as Construction subtest of STMS demands free-hand construction of a clock face at 11.15. It is a very simple task; it requires auditory and visual comprehension, concentration, and memory and executive functioning such as planning and decision-making.
The same data were assessed using the Freedman's scoring criteria. Additional analysis of CDT was performed as in a single sitting it is not logical to administer CDT twice. Moreover, we did not want to tax the subject unnecessarily.
Groningen activity restriction scale
The Groningen activity restriction scale (GARS) is a strong unidimensional hierarchical scale which contains 18 items. It measures both ADL and instrumental ADL (IADL). The reliability coefficient rho is 94. The H coefficient of 0.47 from Mokken analysis indicates that it is strong scale. GARS was found to be a valid scale with correlation ranging between 0.25 and 0.78 with several other instruments measuring physical problems and subjective health.
The section of community approached consisted of individuals with a minimum education of class 10 were found to be conversant in the level of the English language required for the administration of the above-mentioned tests. Hence, the tests used in the present study were used in their standard English version and were not translated.
Peterson's criteria used as the gold standard in MCI diagnosis requires the following conditions to the MCI diagnosis:
- The presence of a subjective memory complaint
- Preserved general intellectual functioning as estimated by the performance on a vocabulary test
- Demonstration of memory impairment by cognitive testing
- Intact ability to perform ADL, and
- Absence of dementia.
The study was conducted on elderly people living within the metropolitan area of Kolkata as well as the adjoining areas. They were reached through different contacts (e.g., local clubs, associations, housing complexes, and personal references). Elderly people meeting the exclusion and inclusion criteria were approached. Verbal consent from the prospective test taker and his immediate family member were taken over the telephone. With consent the screening interview was conducted. Initially, sociodemographic information was obtained using the schedule devised for the study. The presence of undiagnosed major psychiatric disorders was ruled out using the International Classification of Diseases-10 diagnostic criteria. MCI was diagnosed by the visiting psychologist using Peterson's criteria (2004). To identify undetected MCI, the subjective report of memory, ADL and IADL, the participants were assessed and the information was corroborated with at least one family member with whom the participant spends most of the time. Cognitive functioning along with ADL and IADL were also assessed using objective measures: CDT (with Freedman's 15 point scoring) and GARS, both of which have been widely used in the Indian population. At the next level, STMS along with activity level questionnaire (in original English format) was administered (other tests were also administered which do not fall in the purview of the present study). The obtained data were scored according to the standard method and subjected to statistical analysis.
IBM SPSS version 21 (IBM Corp., Armonk, NY, USA) was used for statistical computation. Descriptive statistics was used to analyze sociodemographic variables as well as test variables. Receiver operating curve (ROC) was used to determine the optimal cutoff score of STMS and CDT based on sensitivity and specificity values. Binary logistic regression was used for identifying the change in the odds of getting a diagnosis of MCI with respect to Peterson's criteria.
| Results|| |
The data obtained from the sample of 102 elderly participants from the community in the city of Kolkata wwas analyzed initially for the sociodemographic variables using descriptive statistics. The analysis shows that the mean age of the total study group was 67.18 years (standard deviation [SD] = 6.12) and the mean education (in number of years) was 15.47 (SD = 2.5). The sample consisted of a total of 64 males (62.75%) and 38 females (37.25%). Using the gold standard of the international diagnostic criteria of MCI diagnosis, 39.2% (n = 40) of the elderly participants in total were detected with the presence of MCI. The mean age and mean education of the subsample diagnosed with MCI were 68.13 years (SD = 6.39) and 14.23 years (SD = 2.63), respectively; the mean age and mean education of the subsample without diagnosis of MCI were 66.21 years (SD = 6.15) and 16.35 years (SD = 2.59), respectively. Of the subsample that tested positive for MCI with Peterson's criteria (2004), 52.5% (n 1 = 21) were male and 47.5% (n 2 = 19) were female. The rest of the sample without diagnosis of MCI consisted of 71% (n 1 = 44) male and 29% (n 1 = 18) female. The details of descriptive analysis are presented in [Table 1].
Receiver operating curve analysis: Sensitivity and specificity
ROC analysis of STMS suggests that a score of 34.5 on STMS has the highest sensitivity (0.975) and specificity (0.903). Scores of 31.5, 32.5, and 33.5 also shows sensitivities >90% and considerable specificities of 41.9%, 54.8%, and 67.7%, respectively. The sensitivities and specificities of STMS are presented in [Table 2].
|Table 2: Coordinates of the receiver operating curve analysis of short test of mental status scores|
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To compare the effectiveness of STMS in relation to CDT in the detection of undetected MCI in community-living elderly population an ROC analysis of CDT was also computed, and a score of 14.5 was found to have the highest sensitivity (0.8) and specificity (0.645) indicating that a score of 15 may be considered as the optimum cutoff score of CDT against Petersen criteria (as gold standard) to detect MCI in a sample of the Indian elderly patients. The sensitivities and specificities of STMS are presented in [Table 3].
|Table 3: Coordinates of the receiver operating curve analysis of clock-drawing test scores|
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Binomial logistic regression with backward likelihood ratio
Binomial logistic regression with backward likelihood ratio was used to find out the odds ratio of getting MCI diagnosis in the present sample. Binomial logistic regression with backward likelihood ratio (with a P = 0.05 for entry and a P = 0.01 for removal from model) was used with STMS scores as the independent variable and MCI diagnosis by Peterson criteria as the dichotomous dependent variable. [Table 4].
| Discussion|| |
MCI is a condition which is a result of multiple organic as well as functional causes; it reduces one's quality of life and may progress to dementia-an irreversible and degenerative condition. A meta-analytic study on 41 cohort studies reports that following Mayo Clinic defined guidelines, 21.9%, 28.9%, and 5.2% of MCI cases progressed to dementia, AD, and vascular dementia (VaD), respectively, in population setting. Using the same guidelines, adjusted annual conversion rate of MCI to dementia, AD and VaD in community settings were found to be 4.9%, 6.8%, and 1.6%, respectively.
As aging is associated with decline in cognitive functioning,, most of the studies which have addressed the issue of MCI have focused on the elderly population. Studies also report that the prevalence of MCI increases with age., Both clinical and population-based studies show a MCI prevalence of 10%–20% in people >65 years of age. Keeping in the same line, most of the commonly used cognitive screening tools have been standardized for administration in the elderly population. Of the commonly used cognitive screening tools, only a few have been found to be sensitive in the detection of MCI. Currently, MoCA test has gained much attention with respect to its role in the detection of MCI in comparison to other existing tests like MMSE which is a sensitive test for dementia but not sensitive enough for detection of MCI. Although MoCA was found to be a sensitive tool for MCI detection, it was found to have certain limitations. Although the literature documents that requirement for higher education as the principal limitation, for the present study education was not the impeding factor as the sample selected for the present study has ≥10 years of formal education. Prior experience of administration of MoCA in the population under the study shows that some of the items under the naming test show cultural bias, and on that basis, MoCA was not selected for the present study. On the contrary, STMS was appeared to have less cultural bias and less time consuming. Like MoCA, it is also a test designed for people with >9 years of education and it was deemed relatively more appropriate for the present study. Further, though STMS has gained little attention outside Mayo Clinic setting, it has been established as a highly sensitive and specific test of cognitive impairment with both sensitivity and specificity >90%. Tang-Wai found STMS to be more sensitive in comparison to MMSE (though at the modest level) in the detection of cognitive impairment in people with normal cognition and individuals with MCI. It was also found to be “superior to MMSE” in the detection of cognitive deficits in individuals who have currently normal cognition but later on developed MCI or AD. Overall STMS reported to be “less stringent” than MoCA and “less lenient” than MMSE. Keeping this in mind, STMS was selected for the study and an attempt has been made to standardize it in the present population.
The results obtained from the analysis of data show that a score of 34.5 on STMS has the highest sensitivity 97.5% and specificity 90.3%. However, STMS scores of 31.5 onward show very high sensitivities (above 90%) but relatively low specificities that range between 41.9% and 67.7%. The only score that show both sensitivity and specificity above 90% is 34.5 indicating that a score of 34.5 may be considered as the optimum cutoff score of STMS against Petersen criteria (as gold standard) to detect MCI in a sample of Indian elderly patients. On CDT, a score of 14.5 was found to have the highest sensitivity of 80% and specificity of 64.5% indicating that a score of 14 may be considered as the optimum cutoff score of CDT against Petersen criteria (as gold standard) to detect MCI in a sample of Indian elderly patients. Other CDT scores show considerably lower sensitivities that range between 55% and 70% as well as considerably lower specificities that range between 35.5% and 53.2%. Thus, in the context of the present study, both the tests-CDT (with Freedman scoring) and STMS (Kokmen et al. 1987) were found to be sensitive in differentiating MCI from the rest of the sample. The mean difference in CDT scores between the groups was found to be 2.36, and the mean difference on the STMS scores was 6.71. This represents a difference of 15.73% on CDT with a total score of 15, and 18.14% difference on STMS with a total score of 37. It is also evident that the area under the curve (AUC) or the AUC for both the tests were above 0.5. An AUC of. 918 for STMS in comparison to an AUC of. 642 for CDT shows that STMS is more sensitive in detection of MCI in the present sample. Further, the probable optimal cutoff score of CDT (14.5) with a sensitivity of 80% and specificity of 64.5% show ceiling effect indicating that CDT is less sensitive in differentiating undetected MCI from no-MCI group in the elderly people. [Figure 1] shows the sensitivities and specificities of CDT and STMS in differentiating MCI from no-MCI group as found in the present study.
|Figure 1: Receiver operating curves showing sensitivities and specificities of short test of mental status and clock-drawing test in differentiating mild cognitive impairment from nonmild cognitive impairment participants|
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A subanalysis for gender on the present sample shows that AUC for STMS is 0.877, and for CDT the value for the same is 0.676 in male elderly participants [Figure 2]. [Figure 3] shows that AUC for the female elderly participants is 0.961 for STMS and 0.636 for CDT. ROC analysis for gender also shows that STMS is more effective for the current purpose.
|Figure 2: Receiver operating curves showing area under the curve for short test of mental status and clock-drawing test in male elderly participants|
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|Figure 3: Receiver operating curves showing area under the curve for short test of mental status and clock-drawing test in female elderly participants|
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A median test for both tests also shows that for both the screening tests, the group diagnosed with MCI (using Peterson's criteria, 2004) exhibits lower median scores for STMS and CDT indicating that on both the tests the MCI group has lower median score, with MCI scores being considerably lower in MCI group than nonMCI group. It is represented in the box plots [Figure 4] and [Figure 5].
|Figure 4: A box plot showing median score for short test of mental status in mild cognitive impairment (0) and no-mild cognitive impairment Group (1)|
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|Figure 5: A box plot showing median score for clock-drawing test in mild cognitive impairment (0) and no-mild cognitive impairment Group (1)|
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Binomial logistic regression with backward likelihood ratio was used to find out the odds ratio of getting MCI diagnosis in the present sample. Binomial logistic regression with backward likelihood ratio (with a P = 0.05 for entry and a P = 0.01 for removal from model) was used with STMS scores as the independent variable and MCI diagnosis by Peterson criteria as the dichotomous dependent variable. STMS predicted probable MCI in 85.3% of cases in Block 1 of analysis which is an increase of 24.5% from 60.8% in Block 0 of binomial regression. A cox and Snell's R square of 0.492 and a Nagelkerke's R square of 0.666 are found which indicates that a variation between 49.2% and 66.6% in MCI diagnosis can be explained by the model in Block 1. Finally, an exponential B value of 0.485 for STMS indicates that with a unit increase in STMS score the odds of getting diagnosed with MCI is reduced by 48.5% (odds of getting MCI diagnosis decreases as B value is negative).
Going by the present findings, it is evident that STMS is a highly effective instrument for the detection of MCI in the current population. The high cutoff score of STMS found in the present study can be justified with the fact that the only available published studies in the western countries on STMS were conducted on dementia patients who were compared to patients with different neurological problems in the hospital by Kokmen et al. and Tang-Wai et al. (as mentioned in the present study). However, the present study was conducted in the community in India with no history of any psychiatric or neurological disorder. In addition, it is a study that focused exclusively on the educated middle class male and female elderly people from Kolkata. Hence, as per expectation the average STMS score was considerably higher in the present study.
Besides, as no published study on STMS has been found (in particular no standardization study in India), we have endeavored to standardize it to suite the present purpose. Moreover, the standardization of STMS is important beyond the present study since before this study; we have used STMS for clinical purpose and found it to be a suitable instrument–its difficulty level being higher than MMSE and lower than other tests like MoCA. However, the study has certain limitations. First, the sample size was relatively small. Second, the participants were evaluated on the basis of the standardized diagnostic criteria by the visiting psychologist. However, the tests were scored and rated by three different professionally qualified raters. Besides, the tests were administered only once in a single session and no repeat measure was taken. Finally, in the present study, our target population was elderly people from educated upper middle class family living in Kolkata which represents only a particular section of elderly people living in Kolkata. It is to be mentioned that the present findings reported here were obtained as a part of a larger study, and the tests used were not applicable to the illiterate and people with very low educational level. Hence, the selected sample consisted of educated upper middle class elderly people only. However, in future, sincere endeavors will be made to address the community-living elderly people with no formal education and very little education.
| Conclusion|| |
In the present study, STMS emerged as a sensitive and specific cognitive screening tool in the detection of MCI in the current cultural setting. With a unit increase in STMS score, the odds of getting diagnosed with MCI were found to be reduced by 48.5%. A score of 34.5 with a sensitivity of 97.5 and a specificity of 90.3% were selected for the optimum cutoff score for the detection of MCI in the present population. Finally, it was also evident that STMS was also more effective in MCI detection in comparison to CDT for the purpose.
This work has been done under an interdepartmental research project funded by UGC-CPEPA, University of Calcutta. All the authors gratefully acknowledge the financial support provided by the UGC-CPEPA center.
Financial support and sponsorship
The financial support was provided by University Grants Commission under the Scheme of Center with potential for Excellence (CPEPA) to the University of Calcutta for an interdepartmental research program.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Reisberg B, Ferris S, de Leon MJ, Sinaiko E, Franssen E, Klugger A, et al
. Stage-specific Behavioral, Cognitive, and In-vivo
Changes in Community Residing Subjects with Age-associated Memory Impairment and Primary Degenerative Dementia of the Alzheimer Type. Drug Dev Res 1988;15:101-14.
Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, et al.
Mild cognitive impairment: Ten years later. Arch Neurol 2009;66:1447-55.
Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, et al.
Mild cognitive impairment. Lancet 2006;367:1262-70.
Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO, et al.
Mild cognitive impairment – Beyond controversies, towards a consensus: Report of the international working group on mild cognitive impairment. J Intern Med 2004;256:240-6.
Das SK, Bose P, Biswas A, Dutt A, Banerjee TK, Hazra AM, et al.
An epidemiologic study of mild cognitive impairment in Kolkata, India. Neurology 2007;68:2019-26.
Gambhir IS, Khurana V, Kishore D, Sinha AK, Mohapatra SC. A clinico-epidemiological study of cognitive function status of community-dwelling elderly. Indian J Psychiatry 2014;56:365-70.
] [Full text]
Tripathi R, Kumar K, Balachandar R, Marimuthu P, Varghese M, Bharath S, et al.
Cognitive markers of mild cognitive impairment: An Indian experience. Ann Indian Acad Neurol 2016;19:164.
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189-98.
Srinivasan S. The concise cognitive test for dementia screening: Reliability and effects of demographic variables as compared to the mini mental state examination. Neurol India 2010;58:702-7.
] [Full text]
Mitchell AJ. The Mini-Mental State Examination (MMSE): An update on its diagnostic validity for cognitive disorders. In: Cognitive Screening Instruments. London: Springer; 2013. p. 15-46.
Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al.
The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005;53:695-9.
Mathuranath PS, Nestor PJ, Berrios GE, Rakowicz W, Hodges JR. A brief cognitive test battery to differentiate Alzheimer's disease and frontotemporal dementia. Neurology 2000;55:1613-20.
Pfeiffer E. A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. J Am Geriatr Soc 1975;23:433-41.
Kokmen E, Naessens JM, Offord KP. A short test of mental status: Description and preliminary results. Mayo Clin Proc 1987;62:281-8.
Kirby M, Denihan A, Bruce I, Coakley D, Lawlor BA. The clock drawing test in primary care: Sensitivity in dementia detection and specificity against normal and depressed elderly. Int J Geriatr Psychiatry 2001;16:935-40.
Milne A, Culverwell A, Guss R, Tuppen J, Whelton R. Screening for dementia in primary care: A review of the use, efficacy and quality of measures. Int Psychogeriatr 2008;20:911-26.
Freedman M, Leach L, Kaplan E, Winocur G, Shulman KI, Delis CD. Clock Drawing: A Neuropsychological Analysis. New York: Oxford University Press; 1994.
Suurmeijer TP, Doeglas DM, Moum T, Briançon S, Krol B, Sanderman R, et al.
The Groningen activity restriction scale for measuring disability: Its utility in international comparisons. Am J Public Health 1994;84:1270-3.
Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256:183-94.
Ritchie K, Artero S, Touchon J. Classification criteria for mild cognitive impairment: A population-based validation study. Neurology 2001;56:37-42.
Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia – Meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 2009;119:252-65.
Rubin EH, Storandt M, Miller JP, Kinscherf DA, Grant EA, Morris JC, et al.
A prospective study of cognitive function and onset of dementia in cognitively healthy elders. Arch Neurol 1998;55:395-401.
Ghose S, Das S, Sadhu A. Cognitive Dysfunction in Urban Elderly People: An Exploratory Study Using Neuro-psychological and Neuro-imaging Perspective. Open J Psychiatry Allied Sci 2017;8:141-7.
Katz MJ, Lipton RB, Hall CB, Zimmerman ME, Sanders AE, Verghese J, et al.
Age-specific and sex-specific prevalence and incidence of mild cognitive impairment, dementia, and Alzheimer dementia in blacks and whites: A report from the Einstein aging study. Alzheimer Dis Assoc Disord 2012;26:335-43.
Petersen RC, Roberts RO, Knopman DS, Geda YE, Cha RH, Pankratz VS, et al.
Prevalence of mild cognitive impairment is higher in men. The Mayo clinic study of aging. Neurology 2010;75:889-97.
Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: A clinical review. JAMA 2014;312:2551-61.
Hoops S, Nazem S, Siderowf AD, Duda JE, Xie SX, Stern MB, et al.
Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease. Neurology 2009;73:1738-45.
Tang-Wai DF, Knopman DS, Geda YE, Edland SD, Smith GE, Ivnik RJ, et al.
Comparison of the short test of mental status and the mini-mental state examination in mild cognitive impairment. Arch Neurol 2003;60:1777-81.
Dr. Sanjukta Das
Department of Psychology, University of Calcutta, 92, A. P. C. Road, Kolkata - 700 009, West Bengal
Source of Support: None, Conflict of Interest: None
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4]