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A pilot study to measure cognitive impairment in patients with severe schizophrenia with the Montreal Cognitive Assessment (MoCA)
Schizophrenia Research 158 (2014) 151–155
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A pilot study to measure cognitive impairment in patients with severe schizophrenia with the Montreal Cognitive Assessment (MoCA) Caili Wu a,⁎, Paul Dagg a,b, Carmen Molgat a,b a b
Hillside Psychiatric Centre, Interior Health, Kamloops, BC V2C 2T1, Canada Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
a r t i c l e
i n f o
Article history: Received 28 March 2014 Received in revised form 30 June 2014 Accepted 3 July 2014 Available online 1 August 2014 Keywords: Schizophrenia Cognitive impairment Cognitive assessment Montreal Cognitive Assessment (MoCA)
a b s t r a c t Cognitive impairment has been suggested to be a core feature of a schizophrenia diagnosis. Many comprehensive neuropsychological batteries and experimental procedures have been used to assess cognitive impairment in schizophrenia. A few brief performance-based cognitive assessments have been developed to fulfill the need of a more feasible cognitive assessment for schizophrenia in clinical settings. However, their usability is in question. The Montreal Cognitive Assessment (MoCA), a brief cognitive assessment tool, has been used widely in different clinical settings because of its high sensitivity and specificity for detecting cognitive impairments. This study assessed cognitive function in patients with schizophrenia by using the MoCA tool. The results showed that the MoCA was sensitive enough to detect cognitive impairment in patients with schizophrenia. It also provided normative data for the MoCA in schizophrenia patients. Furthermore, the results revealed that cognitive impairment measured by the MoCA was correlated with their education level, severity of illness, and negative symptoms. Lastly, the MoCA total score could be a significant predictor of patients' length of stay in the facility. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Cognitive impairment has been recognized to be a core feature of a schizophrenia diagnosis (Elvevag and Goldberg, 2000; O'Carroll, 2000; Wilk et al., 2005). A wide range of cognitive functions, including attention/vigilance, visual feature processing, working memory, speed of processing, learning, motor skills, executive function, language, spatial ability, intelligence, and social cognition, have been found to be impaired in patients with schizophrenia. A considerable amount of evidence demonstrates that cognitive impairment often presents before the onset of the illness and is relatively stable across psychotic state changes and time (Heinrichs and Zakzanis, 1998; Pietrzak et al., 2009; Rund, 1998; Sharma and Antonova, 2003). Many studies have revealed associations between cognitive deficits and functional outcomes such as occupational outcome and independent living (Green et al., 2000, 2004; Green, 2006). The importance of assessing cognitive impairment in schizophrenia is well recognized. Many comprehensive neuropsychological batteries have been used to assess cognitive impairment in schizophrenia. Typically those assessments are time consuming
Abbreviations: ANOVA, analysis of variance; CGI, Clinical Global Impression scale; MCI, mild cognitive impairment; MoCA, the Montreal Cognitive Assessment; PANSS, Positive and Negative Symptom Scale; SPSS, Statistical Package for the Social Sciences. ⁎ Corresponding author at: Interior Health Hillside Psychiatric Centre, 311 Columbia St., Kamloops, BC V2C 2T1, Canada. Tel.: +1 250 314 2700x3663; fax: +1 250 314 2812. E-mail address: [email protected] (C. Wu).
http://dx.doi.org/10.1016/j.schres.2014.07.006 0920-9964/© 2014 Elsevier B.V. All rights reserved.
(take hours to days to complete) and require a licensed neuropsychologist to administer, score, and interpret. Applying comprehensive neuropsychological tests is not usually probable in routine clinical practice. A brief yet effective cognitive assessment tool that does not require a neuropsychologist to interpret would be more practical for most clinical settings. A few short performance-based cognitive assessments, for example, the Brief Assessment of Cognition in Schizophrenia (BACS), the Brief Cognitive Assessment (BCA), and the Brief Cognitive Assessment Tool for Schizophrenia (B-CATS), have been developed to evaluate the general cognitive function in schizophrenia (Gold et al., 1999; Hurford et al., 2011; Keefe et al., 2004; Velligan et al., 2004). Nonetheless, the clinical usage, reliability or validity is not well established. There are a few publications to validate the BACS in other languages such as French and Japanese, however, the use of the three assessments in clinical settings were barely reported. The highest correlation between BACS composite score and standard battery domains was .76 for patients. The test–retest reliability of test items ranged from .12 to .93, with the composite score ranging from .86 to .92 for the patient group. The BCA includes three existing neuropsychological tests: verbal fluency (letters and categories), trails A and B, and the Hopkins verbal learning test. The test–retest reliability was .84, based on the data from a subsample. The validity was .72. The B-CATS was constructed mainly for its brevity. It also consists of three existing neuropsychological tests, i.e. the trail making test B, digital symbol, and category fluency. The correlations of the B-CATS with the larger battery total scores were based on evaluating
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data from three previous published trials for other research purposes. The validity was reported as .73 to .86 for different trials, with reliability ranging from .84 to .94. In addition, the BACS needs about 35 min to administer and some extra time to score, which is tiresome to patients and clinicians. The BCA and B-CATS require shorter administration time (about 12 to 15 min). However, both assessments are basically a selection of a small number of standardized neuropsychological tests. The scores are item/cognitive domain based and need to be converted into z-scores according to the existing appropriate normative sample. No direct total score or cut-off score are available for a quick and easy assessment of the general cognitive function/impairment in schizophrenia. Furthermore, the neuropsychological tests are copyrighted and forms need to be purchased so there are costs involved when using the BCA and B-CATS, which further limit their usability in clinical settings. The Montreal Cognitive Assessment (MoCA), a recently developed brief cognitive screening instrument, has been used in different clinical settings because of its high sensitivity and specificity for detecting cognitive impairments (Nasreddine et al., 2005; Popović et al., 2007; Copersino et al., 2009; Luis et al., 2009). With a test–retest reliability of .92 and validity to MMSE of .87, a considerable body of research shows that the MoCA seems to be superior to the Mini-Mental State Examination (MMSE), which is one of the gold-standard cognitive assessments in dementia, in detecting mild cognitive impairment (Popović et al., 2007; Hoops et al., 2009; Pendlebury et al., 2010). The main difference between the MoCA and MMSE is that the MoCA also includes tasks assessing executive function and abstraction. It reduces weight on orientation to time and place while adding weight on recall, attention, and calculation. Specifically, the MoCA assesses the following cognitive domains. The visual spatial abilities are assessed with a clock-drawing task (3 points) and a three-dimensional cube copy (1 point). Executive functions are evaluated using an alteration task adapted from the Trail Making B task (1 point), a phonemic fluency task (1 point), and a two-item verbal abstraction task (2 points). The short-term memory recall task (5 points) includes two learning trials of five nouns and delayed recall after approximately 5 min. The attention, concentration, and working memory are assessed with a target detection using taping task (1 point), a serial subtraction task (3 points), and digits forward and backward tasks (2 points). The language task involves a confrontation animal naming task (3 points), repetition of two syntactically complex sentences (2 points), and abovementioned fluency task. Lastly, orientation to time (date, month, year, and day) and place (names of the place and city) is evaluated (6 points). All those cognitive domains are also known to be impaired in schizophrenia. The MoCA can be administered within 10 min and the total possible score is 30 points. It has a cut-off score of 26 to distinguish patients with mild cognitive impairment (MCI) from patients with intact cognition or normal adults. Furthermore, it has been translated into 36 languages and used in over 100 countries. It is free of charge to patients. It appears to be an ideal candidate for a short, efficient, and effective cognitive assessment tool for patients with schizophrenia. Besides patients with dementia and Alzheimer's disease, normative data has been established in different clinical and non-clinical populations such as patients with cerebrovascular disease and post-stroke, patients with MCI, patients with known or suspected brain pathology, and healthy adults stratified by age, education, and ethnicity (Bernstein et al., 2011; Rossertti et al., 2011). The main objectives of this study were to provide preliminary normative data for the MoCA in schizophrenia patients, to assess its feasibility in detecting cognitive impairment in patients with schizophrenia, and to identify the cognitive domains that were impeded in patients with schizophrenia. Furthermore, the relevance of cognitive impairment to psychopathological symptoms has attracted extensive attention in schizophrenia research. However, the results are mixed (Addington et al., 1991; Carter et al., 1996). This study examined whether the cognitive impairment measured by the MoCA associated with patients' demographic
characters, i.e., age, gender, and education, psychopathology features, i.e., symptoms measured by the Positive and Negative Symptom Scale (PANSS) and severity of illness measured by the Clinical Global Impression scale (CGI). In addition, a considerable amount of evidence has found some cognitive deficits are predictors of functional outcome such as employment and independent living (Sharma and Antonova, 2003; Keefe and Harvey, 2012). In this study, we investigated whether cognitive deficit measured by the MoCA correlated to the length of stay in hospital/facility, a commonly employed outcome measure in health care service, to probe whether cognitive impairment could be a predictor of length of stay. 2. Methods 2.1. Clinical setting Hillside Centre is a tertiary mental health facility, located in Kamloops, British Columbia, Canada. It has three programs with 47 beds: 25 beds in Neuropsychiatry, 11 beds in Geriatric Psychiatry, and 11 beds in a general adult psychiatry program. This centre mainly provides mental health services to patients with acute illness and/or severely dysfunctional behaviors who cannot be cared for in the secondary or general hospital psychiatric system within the Interior Health Authority and Northern Health Authority of British Columbia. Lengths of stay are prolonged due to the refractory nature of many patients' illnesses. At this centre, patients admitted into the general adult psychiatry program are assessed with a variety of psychiatric measurements at admission and discharge to evaluate their symptom severity, psychiatric symptoms, and cognitive impairments. The assessments include CGI, PANSS, and MoCA. The CGI and PANSS are completed by their psychiatrist. The MoCA is administered by a clinician, who is trained by a registered neuropsychologist to administer the MoCA. On average, there are about 80 admissions in the adult program each year. 2.2. Design and participants A retrospective data review of medical records of patients admitted to Hillside Centre from October 1, 2008 to December 31, 2012, was conducted. The retrospective data review identified 121 patients whose primary diagnoses was schizophrenia or schizoaffective disorder according to Diagnostic and Statistical Manual of Mental Disorder (DSM IV) criteria and who had completed the MoCA assessment. The data review also included their demographic information, as well as the scores from the CGI and PANSS, including its sub-scale scores, at admission. In addition, length of stay in days of each patient at the facility was also obtained. 2.3. Statistical analysis Data analyses were conducted using SPSS version 17. The mean and standard deviation (SD) were reported for each MoCA item. Descriptive statistics of patient demographic and clinical characteristics assessed by CGI and PANSS were also presented. In addition, Pearson correlations were employed to investigate relationships between MoCA performance and demographic variables (i.e., age, gender, education), illness severity (i.e., CGI score), positive and negative symptoms (i.e., PANSS scores), and length of stay (in days) in the facility. 3. Results 3.1. Demographic, cognitive, and clinical features The demographic characteristics, i.e., age, gender, education, general cognitive function, i.e., the CGI score, and clinical features, i.e., the PANSS total and subtest scores, as well as the length of stay, are shown in
C. Wu et al. / Schizophrenia Research 158 (2014) 151–155 Table 1 Demographic characteristics, cognitive and clinical features. Measure
Mean
SD
Age Education (years) Gender N (%) Female Male CGI score PANSS Total PANSS Positive PANSS Negative PANSS General Length of stay (days)
37.96 10.96
12.65 1.92
44 (36%) 77 (64%) 5.13 98.43 24.40 27.62 46.41 51.79
0.99 19.96 6.46 7.77 10.44 34.81
Table 1. The average age of the patients was 38 years (SD = 12.65), ranging from 18 to 64 years. Forty-four out of 121 patients (about 36%) were female. The average years of education was 11 years (SD = 1.92), ranging from 5 to 16 years. According to the CGI, all patients were at least “mildly ill”, with 3% mildly ill, 23% moderately ill, 41% markedly ill, 23% severely ill, and 10% among the most extremely ill. The mean CGI score was 5.13 (SD = .99), indicating patients were “markedly ill” on average. The mean PANSS total score was 98.43 (SD = 19.96). The mean PANSS positive, negative, and general subtest scores were 24.40 (SD = 6.46), 27.62 (SD = 7.77), and 46.41 (SD = 10.44), respectively. The mean subscale scores were higher than the scores of the group of patients with schizophrenia reported in the original publication on the PANSS scale, in which the mean positive, negative, and general scores were 18.20 (SD = 6.08), 21.01 (SD = 6.17), and 37.74 (SD = 9.49), respectively (Kay et al., 1987). It seems overall our patient group expressed more severe symptoms. Their average length of stay at the facility was 52 days (SD = 34.81), ranging from 8 to 190 days. 3.2. Cognitive function assessed by MoCA With the education correction for the MoCA (i.e., 1 point was added to the total score when education was not more than 12 years), the average MoCA total score was 20.26 (SD = 5.63), ranging from 3 to 29. About 85% of the scores fell below the suggested 26 cut-off score, indicating most of the patients had at least mild cognitive impairment. The descriptive statistics of each MoCA item are shown in Table 2. On average, patients with schizophrenia performed worse on every single item as well as the total score compared to existing MoCA normative data of normal controls (Nasreddine et al, 2005; Bernstein et al., 2011). In addition, patients performed the worst on the delayed recall task with most patients (92%) not scoring full points on this item. Other items that were not done well, with the percentage of patients
Table 2 Descriptive statistics of each MoCA item. MoCA items/maximum score (n = 121)
Minimum
Maximum
Mean
SD
Trail making/1 Cube/1 Clock drawing/3 Naming/3 Attention digits/2 Attention letter A/1 Attention subtraction/3 Sentence repetition/2 Verbal fluency/1 Abstraction/2 Delayed recall/5 Orientation/6 Total/30
0 0 0 0 0 0 0 0 0 0 0 0 3
1 1 3 3 2 1 3 2 1 2 5 6 29
0.51 0.55 2.45 2.76 1.51 0.56 1.96 1.21 0.50 1.01 2.17 4.74 20.26
.50 .50 .79 .63 .65 .50 1.16 .79 .50 .85 1.62 1.41 5.63
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who did not achieve full points in the parentheses, included abstraction (64%), orientation (62%), sentence repetition (56%), serial 7 subtractions (53%), verbal fluency (50%), and trail making (49%). On the other hand, most of (84%) the patients scored the full three points on the “Naming” item. 3.3. Correlations between MoCA score and demographic and clinical characteristics Correlation coefficients were calculated among MoCA score, demographic variables (i.e., age, gender, and education), and clinical variables (i.e., CGI, PANSS, PANSS Positive, PANSS Negative, and PANSS General scores). The results are reported in the correlation matrix in Table 3. 3.3.1. Correlations between MoCA score and age, gender, and education As shown in Table 3, the MoCA score did not correlate with age (r = − .12, p = .18), but significantly associated with gender and education, r = .19, p b .05, and r = .25, p b .01, respectively. The female patient group (n = 44) had lower mean MoCA score than male patient group (n = 77), 18.82 vs. 21.08, t = − 2.16, p b .05. In addition, higher education is associated with higher MoCA scores. Further comparison of the female and male patient groups reveals that both groups have similar education of 11 years, t = .48, p N .05. However, the male groups have a significantly younger mean age (35 years) than the female group (44 years), t = 3.98, p b .01. To exclude possible confounding effect from age, partial correlation analysis between the MoCA score and gender, with age being controlled, was conducted. The result showed that the MoCA score was not significantly correlated with gender, r = .163, p = .075. 3.3.2. Correlations between MoCA score and CGI, PANSS scores, and length of stay As shown in Table 3, the MoCA total score negatively correlated with the CGI and PANSS scores, r = −.227, p b .01, and r = −.268, p b .01, respectively. The higher the score on the CGI, the lower the score on the MoCA, suggesting a more severe clinical impression is related to more cognitive impairments. Similarly, a higher PANSS score is related to a lower MoCA score, indicating more clinical symptoms are related to more severe cognitive impairment. Further correlation analyses showed that the MoCA score is related to the PANSS negative subscale score, but not with the positive or general subscale scores. Therefore, it seems the more PANSS negative symptoms the patient has, the more impaired his cognitive function is. The correlation between the MoCA total score and the length of stay was negative, r = − .362, p b .01. A higher MoCA score associated with shorter length of stay. A multiple regression analysis, with MoCA total score, CGI, PANSS subscale scores as predictors, was conducted to evaluate how well those assessments could predict the length of stay. The results indicated that the five predictors explained 50% of the variance (R = .249, F (5, 110) = 7.299, p b 0.1). It was found that only MoCA total score significantly predicted length of stay, B = −.253, p b .01. The CGI was a marginally significant predictor, B = .19, p = .072. 4. Discussion Compatible with recent evidence, our results revealed that most patients demonstrated at least mild cognitive impairment and multiple cognitive domains were affected (Heinrichs and Zakzanis, 1998; Elvevag and Goldberg, 2000; O'Carroll, 2000). In reviewing cognitive impairment in schizophrenia, O'Carroll (2000) summarized that up to 75% of patients were affected with significant cognitive impairment. A higher percentage of (86%) patients showed at least MCI in our study. One possibility is that the MoCA is very sensitive and is able to detect mild cognitive impairment that is not picked up with other neuropsychological tests. Another potential explanation might be that our patients, who needed tertiary services and had severe symptoms,
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Table 3 Correlation among MoCA score, age, gender, education, CGI score, PANSS and its subscale scores.
Age Sex CGI Education PANSS Total PANSS Positive PANSS Negative PANSS General MoCA Total
Age
Sex
CGI
Education
PANSS Total
PANSS Positive
PANSS Negative
PANSS General
MoCA Total
1 −.343⁎⁎ −.003 .020 −.068 .011 −.090 −.071 −.123
1 −.195⁎ −.044 −.084 −.085 −.020 −.093 .194⁎
1 −.034 .540⁎⁎ .523⁎⁎ .339⁎⁎ .456⁎⁎ −.292⁎⁎
1 −.154 −.065 −.193⁎ −.107 .254⁎⁎
1 .683⁎⁎ .740⁎⁎ .939⁎⁎ −.239⁎⁎
1 .139 .583⁎⁎ −.154
1 .584⁎⁎ −.251⁎⁎
1 −.176
1
⁎ Correlation is significant at the 0.05 level (2-tailed). ⁎⁎ Correlation is significant at the 0.01 level (2-tailed).
were more likely to show cognitive impairment than patients with relatively mild symptoms. Confirming previous findings with other patient groups, the MoCA total score positively correlates with education. The relationship between MoCA and gender has been found to be inconsistent. Some studies showed that the MoCA total score did not correlate with gender while one study found female patients had a slightly higher MoCA total score (Bernstein et al., 2011; Rossertti et al., 2011). Yet our results showed that female patients had lower MoCA total scores. The different findings might be due to the different clinical populations involved in those studies. In our study, the female group happened to have more severe illness severity according to the CGI score than the male group, t (119) = 3.165, p b .05, which could be the reason why the female patients had lower MoCA score than the male patients. They were also older than the male group. After controlling the age factor, the MoCA score was not correlated with gender anymore. The relationship between MoCA total score and age is not congruent either. Some research found worse performance on the MoCA was associated with older age (Nazem et al., 2009; Rossertti et al., 2011), while others found there was no relationship between MoCA score and age (Luis et al., 2009; Bernstein et al., 2011). Our results support that there is no correlation between MoCA score and age. Our results suggested that cognitive impairment is associated with illness severity and negative symptoms, which is in agreement with the existing observation that cognitive impairment is significantly related to negative symptoms (Addington et al., 1991; Kibel et al., 1993). However, a few studies failed to find a relationship between cognitive impairment and psychopathological symptoms (Faustman et al., 1988). Our results demonstrated that the MoCA total score was a significant predictor of length of stay. Thus cognitive impairment could be an important factor when considering care/discharge planning for inpatients with schizophrenia. Our preliminary results demonstrated that the MoCA seemed to be a quick, feasible, and sensitive cognitive assessment tool for patients with schizophrenia. Further studies are recommended to validate the MoCA in patients with schizophrenia with appropriate neuropsychological batteries. Though normative data has been established in non-clinical populations stratified with age and education, it would be ideal to include a control group that has closely matched demographic characteristics as the schizophrenia group. In addition, the MoCA was administered to a group of patients who needed tertiary mental health services, i.e., their symptoms were relatively severe than inpatients in secondary hospitals or outpatients. Further research is required to probe whether the MoCA can easily detect cognitive impairment in patients with relatively mild illness severity in other clinical settings. Ganguli et al. (1998) provided norms for performance of partially remitted community-dwelling patients with schizophrenia on the MMSE and most patients scored in the un-impaired range. It will be interesting to see whether the MoCA has superiority over the MMSE in detecting cognitive impairment with similar patient group.
5. Conclusion The MoCA appears to be a brief, feasible and sensitive cognitive measurement for patients with severe schizophrenia. Role of funding source N.A. Contributors Author Caili Wu designed the study, undertook the statistical analysis, and prepared the first draft of the manuscript. Authors Paul Dagg and Carmen Molgat screened the schizophrenic patient diagnosis, performed clinical assessments, and reviewed the draft. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that there are no conflicts of interest involved in this study. Acknowledgments We thank Brenda Nadeau and Anne Fox for their assistance in chart review.
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A pilot study to measure cognitive impairment in patients with severe schizophrenia with the Montreal Cognitive Assessment (MoCA) Caili Wu a,⁎, Paul Dagg a,b, Carmen Molgat a,b a b
Hillside Psychiatric Centre, Interior Health, Kamloops, BC V2C 2T1, Canada Department of Psychiatry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
a r t i c l e
i n f o
Article history: Received 28 March 2014 Received in revised form 30 June 2014 Accepted 3 July 2014 Available online 1 August 2014 Keywords: Schizophrenia Cognitive impairment Cognitive assessment Montreal Cognitive Assessment (MoCA)
a b s t r a c t Cognitive impairment has been suggested to be a core feature of a schizophrenia diagnosis. Many comprehensive neuropsychological batteries and experimental procedures have been used to assess cognitive impairment in schizophrenia. A few brief performance-based cognitive assessments have been developed to fulfill the need of a more feasible cognitive assessment for schizophrenia in clinical settings. However, their usability is in question. The Montreal Cognitive Assessment (MoCA), a brief cognitive assessment tool, has been used widely in different clinical settings because of its high sensitivity and specificity for detecting cognitive impairments. This study assessed cognitive function in patients with schizophrenia by using the MoCA tool. The results showed that the MoCA was sensitive enough to detect cognitive impairment in patients with schizophrenia. It also provided normative data for the MoCA in schizophrenia patients. Furthermore, the results revealed that cognitive impairment measured by the MoCA was correlated with their education level, severity of illness, and negative symptoms. Lastly, the MoCA total score could be a significant predictor of patients' length of stay in the facility. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Cognitive impairment has been recognized to be a core feature of a schizophrenia diagnosis (Elvevag and Goldberg, 2000; O'Carroll, 2000; Wilk et al., 2005). A wide range of cognitive functions, including attention/vigilance, visual feature processing, working memory, speed of processing, learning, motor skills, executive function, language, spatial ability, intelligence, and social cognition, have been found to be impaired in patients with schizophrenia. A considerable amount of evidence demonstrates that cognitive impairment often presents before the onset of the illness and is relatively stable across psychotic state changes and time (Heinrichs and Zakzanis, 1998; Pietrzak et al., 2009; Rund, 1998; Sharma and Antonova, 2003). Many studies have revealed associations between cognitive deficits and functional outcomes such as occupational outcome and independent living (Green et al., 2000, 2004; Green, 2006). The importance of assessing cognitive impairment in schizophrenia is well recognized. Many comprehensive neuropsychological batteries have been used to assess cognitive impairment in schizophrenia. Typically those assessments are time consuming
Abbreviations: ANOVA, analysis of variance; CGI, Clinical Global Impression scale; MCI, mild cognitive impairment; MoCA, the Montreal Cognitive Assessment; PANSS, Positive and Negative Symptom Scale; SPSS, Statistical Package for the Social Sciences. ⁎ Corresponding author at: Interior Health Hillside Psychiatric Centre, 311 Columbia St., Kamloops, BC V2C 2T1, Canada. Tel.: +1 250 314 2700x3663; fax: +1 250 314 2812. E-mail address: [email protected] (C. Wu).
http://dx.doi.org/10.1016/j.schres.2014.07.006 0920-9964/© 2014 Elsevier B.V. All rights reserved.
(take hours to days to complete) and require a licensed neuropsychologist to administer, score, and interpret. Applying comprehensive neuropsychological tests is not usually probable in routine clinical practice. A brief yet effective cognitive assessment tool that does not require a neuropsychologist to interpret would be more practical for most clinical settings. A few short performance-based cognitive assessments, for example, the Brief Assessment of Cognition in Schizophrenia (BACS), the Brief Cognitive Assessment (BCA), and the Brief Cognitive Assessment Tool for Schizophrenia (B-CATS), have been developed to evaluate the general cognitive function in schizophrenia (Gold et al., 1999; Hurford et al., 2011; Keefe et al., 2004; Velligan et al., 2004). Nonetheless, the clinical usage, reliability or validity is not well established. There are a few publications to validate the BACS in other languages such as French and Japanese, however, the use of the three assessments in clinical settings were barely reported. The highest correlation between BACS composite score and standard battery domains was .76 for patients. The test–retest reliability of test items ranged from .12 to .93, with the composite score ranging from .86 to .92 for the patient group. The BCA includes three existing neuropsychological tests: verbal fluency (letters and categories), trails A and B, and the Hopkins verbal learning test. The test–retest reliability was .84, based on the data from a subsample. The validity was .72. The B-CATS was constructed mainly for its brevity. It also consists of three existing neuropsychological tests, i.e. the trail making test B, digital symbol, and category fluency. The correlations of the B-CATS with the larger battery total scores were based on evaluating
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data from three previous published trials for other research purposes. The validity was reported as .73 to .86 for different trials, with reliability ranging from .84 to .94. In addition, the BACS needs about 35 min to administer and some extra time to score, which is tiresome to patients and clinicians. The BCA and B-CATS require shorter administration time (about 12 to 15 min). However, both assessments are basically a selection of a small number of standardized neuropsychological tests. The scores are item/cognitive domain based and need to be converted into z-scores according to the existing appropriate normative sample. No direct total score or cut-off score are available for a quick and easy assessment of the general cognitive function/impairment in schizophrenia. Furthermore, the neuropsychological tests are copyrighted and forms need to be purchased so there are costs involved when using the BCA and B-CATS, which further limit their usability in clinical settings. The Montreal Cognitive Assessment (MoCA), a recently developed brief cognitive screening instrument, has been used in different clinical settings because of its high sensitivity and specificity for detecting cognitive impairments (Nasreddine et al., 2005; Popović et al., 2007; Copersino et al., 2009; Luis et al., 2009). With a test–retest reliability of .92 and validity to MMSE of .87, a considerable body of research shows that the MoCA seems to be superior to the Mini-Mental State Examination (MMSE), which is one of the gold-standard cognitive assessments in dementia, in detecting mild cognitive impairment (Popović et al., 2007; Hoops et al., 2009; Pendlebury et al., 2010). The main difference between the MoCA and MMSE is that the MoCA also includes tasks assessing executive function and abstraction. It reduces weight on orientation to time and place while adding weight on recall, attention, and calculation. Specifically, the MoCA assesses the following cognitive domains. The visual spatial abilities are assessed with a clock-drawing task (3 points) and a three-dimensional cube copy (1 point). Executive functions are evaluated using an alteration task adapted from the Trail Making B task (1 point), a phonemic fluency task (1 point), and a two-item verbal abstraction task (2 points). The short-term memory recall task (5 points) includes two learning trials of five nouns and delayed recall after approximately 5 min. The attention, concentration, and working memory are assessed with a target detection using taping task (1 point), a serial subtraction task (3 points), and digits forward and backward tasks (2 points). The language task involves a confrontation animal naming task (3 points), repetition of two syntactically complex sentences (2 points), and abovementioned fluency task. Lastly, orientation to time (date, month, year, and day) and place (names of the place and city) is evaluated (6 points). All those cognitive domains are also known to be impaired in schizophrenia. The MoCA can be administered within 10 min and the total possible score is 30 points. It has a cut-off score of 26 to distinguish patients with mild cognitive impairment (MCI) from patients with intact cognition or normal adults. Furthermore, it has been translated into 36 languages and used in over 100 countries. It is free of charge to patients. It appears to be an ideal candidate for a short, efficient, and effective cognitive assessment tool for patients with schizophrenia. Besides patients with dementia and Alzheimer's disease, normative data has been established in different clinical and non-clinical populations such as patients with cerebrovascular disease and post-stroke, patients with MCI, patients with known or suspected brain pathology, and healthy adults stratified by age, education, and ethnicity (Bernstein et al., 2011; Rossertti et al., 2011). The main objectives of this study were to provide preliminary normative data for the MoCA in schizophrenia patients, to assess its feasibility in detecting cognitive impairment in patients with schizophrenia, and to identify the cognitive domains that were impeded in patients with schizophrenia. Furthermore, the relevance of cognitive impairment to psychopathological symptoms has attracted extensive attention in schizophrenia research. However, the results are mixed (Addington et al., 1991; Carter et al., 1996). This study examined whether the cognitive impairment measured by the MoCA associated with patients' demographic
characters, i.e., age, gender, and education, psychopathology features, i.e., symptoms measured by the Positive and Negative Symptom Scale (PANSS) and severity of illness measured by the Clinical Global Impression scale (CGI). In addition, a considerable amount of evidence has found some cognitive deficits are predictors of functional outcome such as employment and independent living (Sharma and Antonova, 2003; Keefe and Harvey, 2012). In this study, we investigated whether cognitive deficit measured by the MoCA correlated to the length of stay in hospital/facility, a commonly employed outcome measure in health care service, to probe whether cognitive impairment could be a predictor of length of stay. 2. Methods 2.1. Clinical setting Hillside Centre is a tertiary mental health facility, located in Kamloops, British Columbia, Canada. It has three programs with 47 beds: 25 beds in Neuropsychiatry, 11 beds in Geriatric Psychiatry, and 11 beds in a general adult psychiatry program. This centre mainly provides mental health services to patients with acute illness and/or severely dysfunctional behaviors who cannot be cared for in the secondary or general hospital psychiatric system within the Interior Health Authority and Northern Health Authority of British Columbia. Lengths of stay are prolonged due to the refractory nature of many patients' illnesses. At this centre, patients admitted into the general adult psychiatry program are assessed with a variety of psychiatric measurements at admission and discharge to evaluate their symptom severity, psychiatric symptoms, and cognitive impairments. The assessments include CGI, PANSS, and MoCA. The CGI and PANSS are completed by their psychiatrist. The MoCA is administered by a clinician, who is trained by a registered neuropsychologist to administer the MoCA. On average, there are about 80 admissions in the adult program each year. 2.2. Design and participants A retrospective data review of medical records of patients admitted to Hillside Centre from October 1, 2008 to December 31, 2012, was conducted. The retrospective data review identified 121 patients whose primary diagnoses was schizophrenia or schizoaffective disorder according to Diagnostic and Statistical Manual of Mental Disorder (DSM IV) criteria and who had completed the MoCA assessment. The data review also included their demographic information, as well as the scores from the CGI and PANSS, including its sub-scale scores, at admission. In addition, length of stay in days of each patient at the facility was also obtained. 2.3. Statistical analysis Data analyses were conducted using SPSS version 17. The mean and standard deviation (SD) were reported for each MoCA item. Descriptive statistics of patient demographic and clinical characteristics assessed by CGI and PANSS were also presented. In addition, Pearson correlations were employed to investigate relationships between MoCA performance and demographic variables (i.e., age, gender, education), illness severity (i.e., CGI score), positive and negative symptoms (i.e., PANSS scores), and length of stay (in days) in the facility. 3. Results 3.1. Demographic, cognitive, and clinical features The demographic characteristics, i.e., age, gender, education, general cognitive function, i.e., the CGI score, and clinical features, i.e., the PANSS total and subtest scores, as well as the length of stay, are shown in
C. Wu et al. / Schizophrenia Research 158 (2014) 151–155 Table 1 Demographic characteristics, cognitive and clinical features. Measure
Mean
SD
Age Education (years) Gender N (%) Female Male CGI score PANSS Total PANSS Positive PANSS Negative PANSS General Length of stay (days)
37.96 10.96
12.65 1.92
44 (36%) 77 (64%) 5.13 98.43 24.40 27.62 46.41 51.79
0.99 19.96 6.46 7.77 10.44 34.81
Table 1. The average age of the patients was 38 years (SD = 12.65), ranging from 18 to 64 years. Forty-four out of 121 patients (about 36%) were female. The average years of education was 11 years (SD = 1.92), ranging from 5 to 16 years. According to the CGI, all patients were at least “mildly ill”, with 3% mildly ill, 23% moderately ill, 41% markedly ill, 23% severely ill, and 10% among the most extremely ill. The mean CGI score was 5.13 (SD = .99), indicating patients were “markedly ill” on average. The mean PANSS total score was 98.43 (SD = 19.96). The mean PANSS positive, negative, and general subtest scores were 24.40 (SD = 6.46), 27.62 (SD = 7.77), and 46.41 (SD = 10.44), respectively. The mean subscale scores were higher than the scores of the group of patients with schizophrenia reported in the original publication on the PANSS scale, in which the mean positive, negative, and general scores were 18.20 (SD = 6.08), 21.01 (SD = 6.17), and 37.74 (SD = 9.49), respectively (Kay et al., 1987). It seems overall our patient group expressed more severe symptoms. Their average length of stay at the facility was 52 days (SD = 34.81), ranging from 8 to 190 days. 3.2. Cognitive function assessed by MoCA With the education correction for the MoCA (i.e., 1 point was added to the total score when education was not more than 12 years), the average MoCA total score was 20.26 (SD = 5.63), ranging from 3 to 29. About 85% of the scores fell below the suggested 26 cut-off score, indicating most of the patients had at least mild cognitive impairment. The descriptive statistics of each MoCA item are shown in Table 2. On average, patients with schizophrenia performed worse on every single item as well as the total score compared to existing MoCA normative data of normal controls (Nasreddine et al, 2005; Bernstein et al., 2011). In addition, patients performed the worst on the delayed recall task with most patients (92%) not scoring full points on this item. Other items that were not done well, with the percentage of patients
Table 2 Descriptive statistics of each MoCA item. MoCA items/maximum score (n = 121)
Minimum
Maximum
Mean
SD
Trail making/1 Cube/1 Clock drawing/3 Naming/3 Attention digits/2 Attention letter A/1 Attention subtraction/3 Sentence repetition/2 Verbal fluency/1 Abstraction/2 Delayed recall/5 Orientation/6 Total/30
0 0 0 0 0 0 0 0 0 0 0 0 3
1 1 3 3 2 1 3 2 1 2 5 6 29
0.51 0.55 2.45 2.76 1.51 0.56 1.96 1.21 0.50 1.01 2.17 4.74 20.26
.50 .50 .79 .63 .65 .50 1.16 .79 .50 .85 1.62 1.41 5.63
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who did not achieve full points in the parentheses, included abstraction (64%), orientation (62%), sentence repetition (56%), serial 7 subtractions (53%), verbal fluency (50%), and trail making (49%). On the other hand, most of (84%) the patients scored the full three points on the “Naming” item. 3.3. Correlations between MoCA score and demographic and clinical characteristics Correlation coefficients were calculated among MoCA score, demographic variables (i.e., age, gender, and education), and clinical variables (i.e., CGI, PANSS, PANSS Positive, PANSS Negative, and PANSS General scores). The results are reported in the correlation matrix in Table 3. 3.3.1. Correlations between MoCA score and age, gender, and education As shown in Table 3, the MoCA score did not correlate with age (r = − .12, p = .18), but significantly associated with gender and education, r = .19, p b .05, and r = .25, p b .01, respectively. The female patient group (n = 44) had lower mean MoCA score than male patient group (n = 77), 18.82 vs. 21.08, t = − 2.16, p b .05. In addition, higher education is associated with higher MoCA scores. Further comparison of the female and male patient groups reveals that both groups have similar education of 11 years, t = .48, p N .05. However, the male groups have a significantly younger mean age (35 years) than the female group (44 years), t = 3.98, p b .01. To exclude possible confounding effect from age, partial correlation analysis between the MoCA score and gender, with age being controlled, was conducted. The result showed that the MoCA score was not significantly correlated with gender, r = .163, p = .075. 3.3.2. Correlations between MoCA score and CGI, PANSS scores, and length of stay As shown in Table 3, the MoCA total score negatively correlated with the CGI and PANSS scores, r = −.227, p b .01, and r = −.268, p b .01, respectively. The higher the score on the CGI, the lower the score on the MoCA, suggesting a more severe clinical impression is related to more cognitive impairments. Similarly, a higher PANSS score is related to a lower MoCA score, indicating more clinical symptoms are related to more severe cognitive impairment. Further correlation analyses showed that the MoCA score is related to the PANSS negative subscale score, but not with the positive or general subscale scores. Therefore, it seems the more PANSS negative symptoms the patient has, the more impaired his cognitive function is. The correlation between the MoCA total score and the length of stay was negative, r = − .362, p b .01. A higher MoCA score associated with shorter length of stay. A multiple regression analysis, with MoCA total score, CGI, PANSS subscale scores as predictors, was conducted to evaluate how well those assessments could predict the length of stay. The results indicated that the five predictors explained 50% of the variance (R = .249, F (5, 110) = 7.299, p b 0.1). It was found that only MoCA total score significantly predicted length of stay, B = −.253, p b .01. The CGI was a marginally significant predictor, B = .19, p = .072. 4. Discussion Compatible with recent evidence, our results revealed that most patients demonstrated at least mild cognitive impairment and multiple cognitive domains were affected (Heinrichs and Zakzanis, 1998; Elvevag and Goldberg, 2000; O'Carroll, 2000). In reviewing cognitive impairment in schizophrenia, O'Carroll (2000) summarized that up to 75% of patients were affected with significant cognitive impairment. A higher percentage of (86%) patients showed at least MCI in our study. One possibility is that the MoCA is very sensitive and is able to detect mild cognitive impairment that is not picked up with other neuropsychological tests. Another potential explanation might be that our patients, who needed tertiary services and had severe symptoms,
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Table 3 Correlation among MoCA score, age, gender, education, CGI score, PANSS and its subscale scores.
Age Sex CGI Education PANSS Total PANSS Positive PANSS Negative PANSS General MoCA Total
Age
Sex
CGI
Education
PANSS Total
PANSS Positive
PANSS Negative
PANSS General
MoCA Total
1 −.343⁎⁎ −.003 .020 −.068 .011 −.090 −.071 −.123
1 −.195⁎ −.044 −.084 −.085 −.020 −.093 .194⁎
1 −.034 .540⁎⁎ .523⁎⁎ .339⁎⁎ .456⁎⁎ −.292⁎⁎
1 −.154 −.065 −.193⁎ −.107 .254⁎⁎
1 .683⁎⁎ .740⁎⁎ .939⁎⁎ −.239⁎⁎
1 .139 .583⁎⁎ −.154
1 .584⁎⁎ −.251⁎⁎
1 −.176
1
⁎ Correlation is significant at the 0.05 level (2-tailed). ⁎⁎ Correlation is significant at the 0.01 level (2-tailed).
were more likely to show cognitive impairment than patients with relatively mild symptoms. Confirming previous findings with other patient groups, the MoCA total score positively correlates with education. The relationship between MoCA and gender has been found to be inconsistent. Some studies showed that the MoCA total score did not correlate with gender while one study found female patients had a slightly higher MoCA total score (Bernstein et al., 2011; Rossertti et al., 2011). Yet our results showed that female patients had lower MoCA total scores. The different findings might be due to the different clinical populations involved in those studies. In our study, the female group happened to have more severe illness severity according to the CGI score than the male group, t (119) = 3.165, p b .05, which could be the reason why the female patients had lower MoCA score than the male patients. They were also older than the male group. After controlling the age factor, the MoCA score was not correlated with gender anymore. The relationship between MoCA total score and age is not congruent either. Some research found worse performance on the MoCA was associated with older age (Nazem et al., 2009; Rossertti et al., 2011), while others found there was no relationship between MoCA score and age (Luis et al., 2009; Bernstein et al., 2011). Our results support that there is no correlation between MoCA score and age. Our results suggested that cognitive impairment is associated with illness severity and negative symptoms, which is in agreement with the existing observation that cognitive impairment is significantly related to negative symptoms (Addington et al., 1991; Kibel et al., 1993). However, a few studies failed to find a relationship between cognitive impairment and psychopathological symptoms (Faustman et al., 1988). Our results demonstrated that the MoCA total score was a significant predictor of length of stay. Thus cognitive impairment could be an important factor when considering care/discharge planning for inpatients with schizophrenia. Our preliminary results demonstrated that the MoCA seemed to be a quick, feasible, and sensitive cognitive assessment tool for patients with schizophrenia. Further studies are recommended to validate the MoCA in patients with schizophrenia with appropriate neuropsychological batteries. Though normative data has been established in non-clinical populations stratified with age and education, it would be ideal to include a control group that has closely matched demographic characteristics as the schizophrenia group. In addition, the MoCA was administered to a group of patients who needed tertiary mental health services, i.e., their symptoms were relatively severe than inpatients in secondary hospitals or outpatients. Further research is required to probe whether the MoCA can easily detect cognitive impairment in patients with relatively mild illness severity in other clinical settings. Ganguli et al. (1998) provided norms for performance of partially remitted community-dwelling patients with schizophrenia on the MMSE and most patients scored in the un-impaired range. It will be interesting to see whether the MoCA has superiority over the MMSE in detecting cognitive impairment with similar patient group.
5. Conclusion The MoCA appears to be a brief, feasible and sensitive cognitive measurement for patients with severe schizophrenia. Role of funding source N.A. Contributors Author Caili Wu designed the study, undertook the statistical analysis, and prepared the first draft of the manuscript. Authors Paul Dagg and Carmen Molgat screened the schizophrenic patient diagnosis, performed clinical assessments, and reviewed the draft. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that there are no conflicts of interest involved in this study. Acknowledgments We thank Brenda Nadeau and Anne Fox for their assistance in chart review.
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