Which Mini-Mental State Exam Items Can Be Used to Screen for Delirium and Cognitive Impairment?

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1 Vol. 30 No. 1 July 2005 Journal of Pain and Symptom Management 41 Original Article Which Mini-Mental State Exam Items Can Be Used to Screen for Delirium and Cognitive Impairment? Peter M. Fayers, PhD, Marianne J. Hjermstad, PhD, Anette H. Ranhoff, MD, PhD, Stein Kaasa, MD, PhD, Laila Skogstad, RN, Pål Klepstad, MD, PhD, and Jon H. Loge, MD, PhD Department of Public Health (P.M.F.), University of Aberdeen, Aberdeen, Scotland; Unit for Applied Clinical Research (P.M.F., M.J.H., S.K., J.H.L.), Faculty of Medicine, The Norwegian University of Technology and Science, Trondheim, Norway; The Norwegian Cancer Society (M.J.H), Oslo, Norway; Department of Behavioral Sciences in Medicine (M.J.H., J.H.L.), University of Oslo, Oslo, Norway; Research Group of Geriatric Medicine (A.H.R., L.S.), and Oncological Department (J.H.L.), Ullevål University Hospital HF, Oslo, Norway; and Palliative Medicine Unit (S.K.), Department of Oncology, and Department of Anesthesiology (P.K.), St. Olav s Hospital HF, Trondheim, Norway Abstract Cognitive impairment is common in palliative care patients, but it is frequently undetected. The clinical consequence is that psychiatric states such as delirium, which often present with cognitive impairment, are inadequately treated. A short and simple questionnaire for screening of cognitive impairment is required for these patients, in order to proceed with more advanced testing if necessary. In this study, we explored the results from two samples of patients (n 290 and n 217) who had completed the Mini-Mental State Examination (MMSE). Cases of cognitive impairment are considered indicated by an MMSE score of less than 24 of the total 30. We found that caseness could be fairly accurately screened by using four of the original 20 MMSE items, and that a six-item questionnaire further greatly improved the discrimination. J Pain Symptom Manage 2005;30: U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved. Key Words Mini-Mental State Examination, MMSE, palliative care, delirium, cognitive impairment Introduction Cognitive functions include orientation, attention, language, construction, memory, arith- Address reprint requests to: Peter M. Fayers, PhD, Department of Public Health, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, United Kingdom. Accepted for publication: December 22, U.S. Cancer Pain Relief Committee Published by Elsevier Inc. All rights reserved. metical calculation, reasoning, and executive functioning. According to the latest version of the Diagnostic and Statistical Manual of Mental Disorders-IV Text Revision (DSM-IV-TR), impairment of cognitive functions is an essential feature of three separate conditions: delirium, dementia, and amnestic disorders. 1 Each of these has additional, separate diagnostic characteristics and cognitive impairment is consequently not specific for any of these conditions. Cognitive impairment is frequent in cancer patients, and has been demonstrated to be an /05/$ see front matter doi: /j.jpainsymman

2 42 Fayers et al. Vol. 30 No. 1 July 2005 independent survival predictor. 2 Cognitive impairment associated with delirium is a common cause for admission to clinical institutions, and delirium is the most frequently cited psychiatric disorder in palliative care, with over 80% of advanced cancer patients experiencing delirium in their final days. 3 Earlier in the disease trajectory, delirium is potentially reversible but associated with increased mortality if untreated. 3 Detection of cognitive impairment and correct classification of the underlying condition is, therefore, of great importance. However, the non-detection rate of cognitive impairment and delirium by nurses and doctors is reported as high as 70%. 4,5 Lack of adequate and easy-to-use instruments for assessment of cognitive impairment may contribute to this. In palliative care, where patients have advanced disease and poor life expectancy, it is particulary important that a short and simple instrument be used. It is also a basic requirement that the instrument should be sufficiently sensitive and specific, validated in cancer populations, and be suitable for repeated assessments of patients. The Mini-Mental State Exam (MMSE) questionnaire is designed for assessment of cognitive impairment. 6 It is the most commonly used instrument for assessment of cognitive impairment and delirium in palliative care, 7 and a recent review demonstrated that the MMSE had been used in 13 of 22 studies on cognitive impairment/delirium in palliative care. 8 However, the MMSE is not specifically designed for assessment of delirium, but measures cognitive functions with 20 items. Despite the common usage in palliative care, in studies of delirium and in assessment of cognitive impairment in general, the MMSE has not been validated in a palliative population. Little is known about its properties in delirious patients, or when it is used in patients with cancer or in those receiving palliative care. 8 The first purpose of this paper is, therefore, to investigate the performance of the MMSE items for predicting delirium in delirious patients. For the recognition of early stages of delirium, one might want to look for cognitive changes as a first step to identify patients requiring more comprehensive assessment. The MMSE is also a commonly used assessment tool in this respect and has become a reference against which other instruments have been judged. It has been suggested that the MMSE is suitable for routine cognitive assessment of palliative patients, although with these patients it may be too lengthy and cumbersome for regular use. 9,10 Furthermore, little is known about the rate of false-negatives and false-positives in a palliative population or in patients with advanced cancer. 8 Thus, the second objective of this paper is to explore whether a shortened version of the MMSE has suitable properties as a screening tool for cognitive impairment in cancer patients and in palliative care. Methods Patients Two hospital-based, all-caucasian patient data sets (Sample A and Sample B) were available (Table 1). Sample A consisted of 150 patients between 70 and 90 years who were admitted to the general medical unit in Ullevål University Hospital HF, Oslo, Norway for somatic diseases and who were also diagnosed with delirium during their hospital stay according to the ICD-10 research criteria for delirium. 11 A group of 150 consecutive patients admitted at the same hospital and of similar age, but with no diagnosis of delirium or other cognitive impairment, was recruited as a control. All patients were originally enrolled in a clinical study on delirium. Infection, mainly pneumonia, was the most common main diagnosis for admittance and was found in almost 50%, followed by cardiovascular (about 30%) and cerebrovascular diseases (10%), drug-related problems (5%), and anemia (5%). The median age was 80 years, and the majority was female (58%). Informed consent was given by both patient and proxy in the delirium group, and by patient in the control group. Missing informed consent from patient or proxy was the main reason for withdrawal. One of three Table 1 Patient Characteristics (n 507) Sample A: Sample B: Ullevål Trondheim No. of patients Age: median (range) 80 (70 90) 62 (29 89) Gender female 167 (58%) 101 (47%) Diagnosis of delirium 142 (49%) Not available MMSE score (44%) 51 (23.5%)

3 Vol. 30 No. 1 July 2005 Screening for Delirium and Cognitive Impairment 43 specially trained research nurses performed the inclusion procedure. They collected information about cognitive status from nurses and doctors in the wards, interviewed and observed the patients, and also administered the MMSE and the clock drawing test. 12 This information was used to identify delirium according to the diagnostic criteria. Patients for the control group needed to have an MMSE score of 24 or higher, a normal clock drawing test, and no signs of delirium. Complete MMSE scores were obtained for 290 patients with median age 80, of whom 167 (58%) were female. Sample B was derived from two studies of opioid pharmacology, which recruited 300 and 29 patients, respectively, all of whom were cancer patients admitted to St. Olav s Hospital HF, Trondheim, Norway. 13,14 For both studies, inclusion criteria were verified malignant disease, age over 18 years, and scheduled morphine treatment that had been started at least three days prior to inclusion. In those studies, assessment of cognitive function by the MMSE was one of multiple factors used to analyze potential for predicting the variability of pain intensity. A researcher administered the MMSE to the patients the day after hospital admission. Seventy-one patients (22%) declined to answer the MMSE questions or were not able to do so, most commonly because they felt too exhausted. Another 41 patients (12%) had one or more missing items on the MMSE questionnaire, most commonly the final two MMSE items, which both involved writing. Thus, there were complete MMSE forms for a total of 217 patients (66%). The median age of the respondents was 62 years, and 46% were female. The predominant cancer diagnoses were prostate 22%, breast 20%, lung 16%, and GI cancer 12%. Mean Karnofsky performance score was 67 (SD 12). No information on educational or prior vocational levels was available for any of the samples, as these were not associated with the outcome measures in the original studies. Ethics Both studies were conducted according to the guidelines of the Helsinki Declaration. The Regional Committee for Medical Research Ethics, Health Regions I and IV approved the studies in Samples A and B, respectively. Appropriate informed consent was obtained from all patients or proxies. Questionnaire The MMSE consists of a series of questions and tests that score points if answered correctly. 6 The MMSE consists of 20 items and has a total score of 30 points. The items are clustered into 11 subscores: measuring orientation to time (5 points); orientation to location (5 points); memory Part 1: immediate recall of three named objects (3 points), attention and calculation (5 points); memory Part 2: recall three objects (3 points), naming two objects (2 points), repeating a phrase (1 point), following a three-stage command (3 points), reading (1 point), writing a sentence (1 point), and copying a pentagon (1 point). A score of less than 24 of 30 was used as the screening threshold indicating cognitive impairment. 6 Education-related cut-off points for abnormal cognitive function have been recommended: for patients with a middle school education, 23 for a high school education and 24 for graduate school education. However, because data on educational background was not available to us, and to avoid subgroup analyses on small samples when subdividing the material, we used the originally proposed higher cut-off of 24. In the Norwegian version of the MMSE, there are a few modifications because some items could not be translated directly. 16 Thus, the item state was changed to name this country, and county was translated as which part of the country are we in. Also, for item 12 (abstract thinking, tested by ability to spell backwards), the five-character word world was changed to the Norwegian for sword. Statistical Analyses Stepwise logistic regression was used to identify the items that best discriminated the diagnosis of delirium. Caseness, defined as an MMSE score less than 24, was also analyzed by logistic regression, while linear regression was used to explore prediction of the overall MMSE score. These analyses were carried out on the Sample A (Ullevål) dataset. The items identified as the best discriminators in the model were validated using the Sample B (Trondheim) dataset, to confirm their ability to predict MMSE scores less than 24. Item response theory (IRT) enables estimation of item difficulty and discrimination, 17 and was also used to determine items that were

4 44 Fayers et al. Vol. 30 No. 1 July 2005 optimal at different levels of cognitive functioning. Thus, IRT identifies those items that are best for distinguishing subjects with very high functioning from those with slightly less high functioning ( difficult items), or those items that are more suited to discriminating between subjects with low functioning from those with very low functioning ( easy items). If the principal interest is in the binary classification of delirium versus no-delirium, the relevant items should be those that have difficulties in the corresponding region of the cognitive dimension: for example, very easy items that nearly all patients answer correctly provide little information about delirium these items serve only to separate the most seriously confused patients from the slightly less confused, which is not the concern when performing a binary classification into case or non-case. IRT, which explicitly estimates item difficulty, provides an ideal method for analyzing this. It also enables estimation of item discrimination, which is the important property that, ideally, if an item has a difficulty level corresponding to the division between delirium and no-delirium, then the probability that a delirious person gives the correct answer should be close to zero, while the probability that a less-delirious person correctly answers this should rapidly approach one. That is, we want to select items in which estimated discrimination is largest, and in which estimated difficulty corresponds to the region of interest. When selecting several items for use in a test, IRT also provides graphical displays of the overall test information function, showing how the combined items perform over the range of cognitive functioning, and the standard error (SE) that is associated with estimates of cognition at various scale points (the SE should be lowest around the threshold of delirium/ no-delirium). These displays facilitate comparison between the perfect test, as represented by the full MMSE, and shortened forms of the MMSE. Technically, a two-parameter IRT model was fit using marginal maximum likelihood. For the polytomous items (MMSE items 11, 12, 13, and 16, which can have scores greater than 1), the generalized partial credit model (GPCM) and the graded response model (GRM) were applied. 17 Item fit was assessed by comparing the expected a posteriori (EAP) estimates of respondent scores against the grouped observed patterns. 18 Item fit indices, together with exploratory factor analysis, confirmed that the unidimensional IRT model provided adequate fit for most MMSE items. This is in accordance with findings by other investigators Because IRT is generally considered to require sample sizes of 500 or more patients for reliable estimation (e.g., Muraki and Bock 23 ), the combined dataset was used for these analyses. The IRT analyses were carried out using PARSCALE version 4, 23 and all other statistical analyses used STATA version Results The patient characteristics are given in Table 1. In Sample A, 290 patients recruited at Ullevål completed the MMSE. Of these, 127 (44%) had an MMSE score below 24. They were all diagnosed as having delirium according to the ICD-10 research criteria for delirium, and an additional 15 patients with higher MMSE scores were also rated as having delirium, giving a total of 142 delirious patients (49%). In Sample B, 51 (23%) of the 217 patients had an MMSE score below 24. Figure 1 shows the distribution of the MMSE scores and cases in the two samples. Stepwise logistic regression in Sample A with delirium as the outcome variable resulted in the selection of four MMSE items, as shown in Table 2. The items were: the current year (Item 1), date (Item 4), backward spelling of sword (Item 12), and copy a design (Item 20). Adding additional MMSE items did not appreciably improve the overall model fit. Three of these items are scored 1 for a correct response, but Item 12 (backward spelling of sword ), is rated 0 to 5 according to the number of correct letters. However, 147 of the 148 non-cases scored at least three or more correct letters. Further inspection of the response percentages indicated that in this sample of patients, the main information derived from Item 12 is whether or not there were fewer than two correct responses, or greater than two. Coding Item 12 as 1 for a score of two correct subtractions, and 2 for three or more, the sum of all four items ranges from 0 to 5. Using these items, a threshold score of 4 or less correctly identified 131 of the 142 delirious

5 Vol. 30 No. 1 July 2005 Screening for Delirium and Cognitive Impairment Sample A Sample B 40 Frequency MMSE cases MMSE cases MMSE Score Fig. 1. Distribution of MMSE scores in the two samples. MMSE cases are defined as a score less than 24 of 30. patients (92%), with only two of the other 148 being assessed as false positives. Similarly, defining MMSE caseness in the conventional manner of an MMSE score less than 24, the four-item version identified 126/127 (99%) cases, and 156/163 (96%) non-cases. Applying these results to the second (validation) sample of patients (Sample B), the threshold of four correctly identified 53/55 (96%) MMSE cases and 160/176 (91%) non-cases. The analyses were repeated using item response models. Although the MMSE is intended to assess a number of higher-level dimensions (such as orientation in time and space, abstract thinking, etc.), exploratory factor analysis indicated that it is reasonable to regard the MMSE as unidimensional for the IRT analyses. Table 3 summarizes the slope (discrimination) and difficulty of the items. Inspecting the IRT model fit, the dichotomous items all provided very good fit (output not shown). However, none of the IRT models (GPCM and GRM) provided good fit for the four polytomous items. Although the MMSE is conventionally scored as a simple summated scale from 0 to 30, IRT uses a different approach. As is conventional with IRT analyses, MMSE ability is calibrated in terms of standard deviations about the mean, ranging from about 3to 3. For each possible score over this range, patients are rated according to their probability of obtaining that score. Although IRT scores do not equate exactly to sum-scores, for this data,athreshold MMSE scoreof less than 24 corresponded approximately to an IRT score of 0.3; therefore, we can assume that an Table 2 Logistic Regression of MMSE Items on the Outcome Delirium (290 patients from Ullevål) 95% Confidence Delirium Odds Ratio Standard Error z P Interval mms mms mms mms mms01 What year is it? mms04 What date is it? mms12 Spell SWORD backwards; mms20 Copy diagram. LR χ 2 (4) Prob χ Log likelihood Pseudo R

6 46 Fayers et al. Vol. 30 No. 1 July 2005 Table 3 MMSE Item Locations and Slopes: Combined Data MMSE Item Difficulty SE Discrimination SE 01 Year Month Season Date Day Country Region Town Hospital (or home address) Department (or zip code) a Remember three words a Spell sword backwards (0.66 b ) a Recall three words Pencil Clock No ifs and buts a Fold paper Close eyes Write a sentence Copy design a Items with multiple response options were modeled using the generalized partial credit model; average values are tabulated. b The discrimination after categories were combined (see text). MMSE case is roughly expected to score about 0.3 or less. Furthermore, the sample invariance property of IRT indicates that a similar equivalence should apply to other samples or data sets. Inspecting Table 3, we see that the four items identified by logistic regression do indeed have difficulty thresholds close to 0.3. Table 3 also shows the slope (discrimination) parameters, and it is important that the slope be high. As noted above, the categories of Item 12 could be reduced, and this was confirmed by inspecting the category trace lines produced for Item 12 using the GPCM; in the simplified model this item had a discrimination of Figure 2 shows the test information (dashed curve) for the full 20-item MMSE test. Again, an IRT score of 0.3 corresponds approximately to an MMSE score of 24. Thus, we can see that most of the MMSE items are too easy, because the greater part of the information curve lies below 0.5. The figure also shows the standard errors (continuous curve) associated with the estimates of ability. This shows that the MMSE provides the most accurate assessment for patients with ability much less than 24, because the standard errors are lowest between 2 and 1. This confirms that the majority of MMSE items are not optimal for discriminating cases/non-cases of delirium as defined by a threshold of 24. Figure 3 contrasts the standard errors of the full MMSE (from Fig. 2) with the standard errors from the four-item test. The four items provide excellent prediction in the region of interest, although, not surprisingly, they carry little information outside the range of 1.5 to 0.5. However, if it is desired to improve the test characteristics outside this range, IRT facilitates the choice of a pair of easy and difficult items to supplement the other four. Thus, Fig. 4 shows the effect of adding Items 13 ( recall 3 objects ) and 15 (repeat no ifs, ands, Standard error Standard error Test Information Cognitive function ("Ability") Test information Fig. 2. Test information of the full MMSE, and the standard error of the score estimates associated with different levels of cognitive function.

7 Vol. 30 No. 1 July 2005 Screening for Delirium and Cognitive Impairment 47 Standard error All MMSE 4 items Cognitive function Fig. 3. Standard error of MMSE score estimates: four-item tests versus full MMSE. or buts ). The standard errors of this third curve have much better properties over much of the range. Discussion The MMSE is one of the most widely used measures for assessment of cognitive impairment, dementia, and delirium, although many authors have questioned the performance of some of the items. In our study investigating a range of Norwegian patients, we explored the ability of the various items of the MMSE to identify delirium and cognitive impairment, and Standard error All MMSE 6 items 4 items Cognitive function Fig. 4. Standard error of MMSE score estimates, showing that a six-item test has similar properties to the full MMSE. found that many of the items did not perform very well. We also found that a small subset of between four to six items is adequate, to screen for delirium. The smallest set of items consisted of the current year (Item 1), date (Item 4), backward spelling (Item 12), and copy a design (Item 20). The final item (Item 20) had been identified as a problematic item in Sample B, because the investigators found that the writing and drawing questions were cumbersome and difficult to perform in this population. These tasks needed much explaining, and hence decreased the feasibility of the MMSE. Several patients were, for a variety of reasons, unable to use a pencil easily (decreased strength, supine position, attached to IV lines). Therefore, we explored using alternative MMSE items, but found that no other items were as effective at discrimination. However, for these patients, a single drawing task following three previous questions may be more acceptable than the full MMSE. Reducing the length of the MMSE is particularly beneficial in palliative patients, many of whom are near the end of life and too weak for lengthy questionnaires. Another benefit of using a short version is that this will reduce the need for presenting silly questions to the cognitively intact patients, which should improve the face validity of the questionnaire. Although other investigators have explored dementia rather than delirium, our results are in accordance with previously reported results. 16,19 25 It has been found that the orientation-to-time items are the most effective in discriminating cognitive impairment, and that recall three items is a very effective question for predicting the overall MMSE score. This item specifically tests short-term memory, which is the main characteristic of dementia and a prerequisite for orientation. There is general agreement that orientation to space, learn three items and repeat immediately, and name a watch/pencil are not very effective items in detecting dementia. 16,20 25 Our findings are also in accordance with previous studies, which indicated that the MMSE can be shortened without loss of sensitivity and specificity. Thus, Jacqmin-Gadda et al. compared 2544 normal subjects at least 65 years old, randomly sampled in southwest France, and 248 diagnosed as demented. 25 Using stepwise logistic regression, they found that the most

8 48 Fayers et al. Vol. 30 No. 1 July 2005 discriminant MMSE items were orientation to time and recall three objects (in addition to items from two other questionnaires, for visual retention and ability to verbalize). These results were validated in a further sample of 985 subjects. Together, these six items provide low standard errors over much of the range. Solfrizzi et al. applied item response theory to MMSE data from a sample of 451 healthy elderly Italian subjects, comparing them to a small (n 37) sample of demented patients. 22 They reported that orientation-to-time items were the most useful in distinguishing between normal and demented subjects, although orientation in space was also useful. Although the polytomous items were not included in their statistical analyses, they noted that recall three objects had the highest correlation with total MMSE score. Braekhus et al. examined 831 Norwegian patients age 54 years and older, comparing the MMSE to a psychogeriatrician s assessments of cognitive impairment. 16 Logistic regression and factor analysis were applied. They suggested that a 12-item version of the MMSE, with only dichotomous items, was equally effective to the standard 20-item MMSE. In particular, they proposed dropping the items for orientation (location) of country/state and town, name a pencil/watch, the read-and-obey task, and the item learn three objects and repeat immediately ; however, like other investigators, they found that recall three objects was a useful discriminatory item. Several earlier studies have also confirmed that orientation-in-time items and recall three objects provide the best discrimination for dementia in Alzheimer s disease, and these items also have been reported to be the best predictors for development of Alzheimer s disease. 30 Many of the same items have been used in the WHO World Mental Health 2000 surveys (WMH2000), which assesses cognition in a prescreening set of items derived from the U.S. National Comorbidity Survey. The first step comprises one recall item (interviewer s middle name), and six items for orientation in time and space. Respondents who score six or seven correct answers proceed to the second step, where they are given three attempts to recall ten words. The extensive WMH2000 interview only continues with those who correctly recall at least five separate words. Our study, by applying both logistic regression and IRT analysis, was able to show firstly that these findings apply similarly to patients with cancer or in palliative care, and to general medical patients with delirium. Secondly, our analysis explains the contribution that individual items make at different ability levels, enabling an efficient choice to be made for deriving different minimal sets of items according to whether the full range of assessment is of interest or merely the ability to screen for cognitive impairment (dementia or delirium) corresponding to an MMSE score of 24. A score of less than 24 of 30 is frequently regarded as the threshold representing cognitive impairment, although this has been widely criticized in terms of specificity, and diagnostic bias with education, because educated demented patients may have a higher score; 20,21,31,32 age bias, because younger patients may have a higher score; 33 and ethnic bias. 20,21,34 The fixed cut-off of 24 might, to some extent, have limited our findings, because age and educational level were not taken into account. 15,33,35 This mostly affects the identification of certain types of mild impairment, and favors false negatives; thus, it has been suggested that the MMSE is only applicable for patients who have more than primary school education. 36 However, our results were convincing for the four identified items and, because the normal population in Sample B was agematched with the delirious patients, it seems unlikely that the age gradient significantly influenced the results. It has been pointed out that there might be significant inter-rater variability with the MMSE, 36 and this cannot be completely ruled out in the present study. On the other hand, in our study, the raters, who were specially trained research nurses, were few (three and two in Sample A and B, respectively), which leads us to believe that this was not a significant bias. In clinical practice, as in research, dementia and delirium are mainly separated on the basis of duration and the fluctuation of symptoms in delirium (i.e., temporal patterns). Delirium may also be superimposed upon dementia, and again, duration provides the key information for establishing a correct diagnosis. For the purpose of the present study, we consider these points of lesser relevance because a diagnosis

9 Vol. 30 No. 1 July 2005 Screening for Delirium and Cognitive Impairment 49 of delirium was established as an external criterion in Sample A. The four MMSE items that predicted delirium reflect the main diagnostic criterion for delirium (disturbed consciousness), and should, therefore, be of clinical value. The patients with MMSE scores of 24 or lower in Sample B were not further classified into dementia, delirium, or other conditions associated with cognitive impairment. The MMSE was developed as a screening tool for cognitive failure, and should be used for these purposes only. A low MMSE score indicates the need for a thorough mental-status examination. This examination should employ specific diagnostic tools that discriminate between delirium and dementia. The validity of our findings should be tested against background variables that have been shown to impact on MMSE scores, such as age, educational level, and ethnicity. 15,20,21,31 37 The use of the abbreviated version of the MMSE needs to be replicated in a larger, prospective study, together with delirium diagnostic tools such as the Delirium Rating Scale, the Confusion Assessment Method, or the Memorial Delirium Assessment Scale, and with cognitive screening tools such as the Blessed Orientation Memory Concentration test. These tools may be more clinically applicable than the full MMSE, and demand less effort from the patient. A prospective treatment study with repeated assessments would also allow evaluation of the relative sensitivity of scores from the full versus the abbreviated MMSE, when detecting response to therapy. Further prospective studies are needed to establish whether a four- or six-item version of the MMSE is preferable as a screening tool for dementia and/or delirium in ordinary clinical settings. Conclusions Logistic regression and IRT analyses are powerful and complementary tools for examining the properties of tests. Our results showed that the MMSE, when applied to patients in palliative care and with delirium, appears to function similarly to what has been observed in dementia, and that it provides a useful tool when screening for possible delirium although, of course, it cannot distinguish between cognitive impairment in dementia and in delirium. We also found that for screening purposes, a set of four items provides most of the power that is found in the full 20-item MMSE. References 1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 4th ed. Text revision (DSM IV TR). Washington, DC: American Psychiatric Association, Vigano A, Dorgan M, Buckingham J, Bruera E, Suarez-Almazor ME. Survival prediction in terminal cancer patients: a systematic review of the medical literature. Palliat Med 2000;14: Centeno C, Sanz A, Bruera E. Delirium in advanced cancer patients. Palliat Med 2004;18: Pisani MA, Redlich C, McNicoll L, Ely EW, Inouye SK. Under-recognition of preexisting cognitive impairment by physicians in older ICU patients. Chest 2003;124: Inouye SK, Foreman MD, Mion LC, Katz KH, Cooney LM Jr. Nurses recognition of delirium and its symptoms: comparison of nurse and researcher ratings. Arch Intern Med 2001;161: 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: Robinson J. Cognitive assessment of palliative care patients. Prog Palliat Care 1999;7: Hjermstad MJ, Loge J-H, Kaasa S. Methods for assessment of cognitive impairment and delirium in palliative care patients implications for practice and research. Palliat Med 2004;18: Bruera E, Franco JJ, Maltoni M, Watanabe S, Suarez-Almazor M. Changing pattern of agitated impaired mental status in patients with advanced cancer: association with cognitive monitoring, hydration, and opioid rotation. J Pain Symptom Manage 1995;10: Pereira J, Hanson J, Bruera E. The frequency and clinical course of cognitive impairment in patients with terminal cancer. Cancer 1997;79: World Health Organization. Mental disorders: Glossary and guide to their classification in accordance with the 10th revision of the International Classification of Diseases. Geneva: World Health Organization, Sunderland T, Hill JL, Mellow AM, et al. Clock drawing in Alzheimer s disease. J Am Geriatr Soc 1989;37: Klepstad P, Borchgrevink PC, Dale O, et al. Routine drug monitoring of serum concentrations of morphine, morphine-3-glucuronide and morphine- 6-glucuronide do not predict clinical observations in cancer patients. Palliat Med 2003;17:

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