Error behaviors associated with loss of competency in Alzheimer’s disease
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Abstract
Objective: To investigate qualitative behavioral changes associated with declining medical decision-making capacity (competency) in patients with AD.
Background: Qualitative measures can yield clinical information about functional changes in neurologic disease not available through quantitative measures.
Methods: Normal older controls (n = 21) and patients with mild and moderate probable AD (n = 72) were compared using a standardized competency measure and neuropsychological measures. A system of 16 qualitative error scores representing conceptual domains of language, executive dysfunction, affective dysfunction, and compensatory responses was used to analyze errors produced on the competency measure. Patterns of errors were examined across groups. Relationships between error behaviors and competency performance were determined, and neurocognitive correlates of specific error behaviors were identified.
Results: AD patients demonstrated more miscomprehension, factual confusion, intrusions, incoherent responses, nonresponsive answers, loss of task, and delegation than controls. Errors in the executive domain (loss of task, nonresponsive answer, and loss of detachment) were key predictors of declining competency performance by AD patients. Neuropsychological analyses in the AD group generally confirmed the conceptual domain assignments of the qualitative scores.
Conclusions: Loss of task, nonresponsive answers, and loss of detachment were key behavioral changes associated with declining competency of AD patients and with neurocognitive measures of executive dysfunction. These findings support the growing linkage between executive dysfunction and competency loss.
Loss of decision-making capacity (competency) is an inevitable consequence of AD.1 In the clinical setting, physicians and other health care professionals continually face the question of whether a patient with AD has the capacity to consent to treatment and research. This competency is an essential element of the doctrine of informed consent,2 and its loss has significant consequences for dementia patients and families as well as health care and legal professionals.1,3 Recent research has focused on the development of standardized quantitative instruments to guide assessment of competency.1,4,5 For example, we have developed a Capacity to Consent to Treatment Instrument (CCTI) that we have used to quantitatively assess consent capacity of AD patients under different legal standards (LSs).1
Qualitative scoring systems (error analysis) represent another approach to understanding loss of competency in dementia.6 Error analysis has been used clinically in psychiatry and neurology.7-9 For example, neuropsychological studies have compared verbal and spatial abilities in normal elderly controls and patients with AD.7,9 Other studies have examined behavioral changes in dementia patients associated with decline in everyday living tasks such as paying bills and mailing letters.10
Error analysis can also provide clinical information about loss of competency in dementia that quantitative instruments are unable to capture.6,7 Such an analysis can identify changes in language, executive, and emotional functioning associated with declining medical decision-making abilities as well as characteristic compensatory strategies adopted by dementia patients. Moreover, although quantitative scores provide information regarding an individual’s level of performance, qualitative scores have the potential to help explain that performance.6
This article investigates declining competency in AD patients using an error analysis of responses on the CCTI. We compare incidence rates of error behaviors across groups of normal older controls, mild AD patients, and moderate AD patients. Next, we examine the relationship between error behaviors and competency performance of AD patients and controls. Finally, we identify neurocognitive correlates of the error types in the AD group and the pattern of these behaviors across different stages of the disease.
Methods.
Subjects.
Patients with probable AD (n = 72) and normal older controls (n = 21) were recruited from a Program Project in Alzheimer’s Disease (NIH, NIA 1 P30 AG10163-1) and from a prior Alzheimer’s Disease Center Core (NIH, NIA 5 P01 AG06569-05). Both AD patients and controls were well characterized medically, neuropsychologically, and neuroradiologically. Diagnosis of normalcy was made by the consensus judgment of a neurologist (L.H.) and neuropsychologist (D.M.). Diagnosis of probable AD was made by consensus judgment of the neurologist and neuropsychologist using NINCDS-ADRDA criteria.11 Using the Mini-Mental State Examination (MMSE) score,12 AD patients were divided into subgroups of mild dementia (MMSE score ≥20; n = 47) and moderate dementia (MMSE score ≥10 and ≤19; n = 25). We have successfully used this method of AD staging in prior competency research.1,13 Informed consent was obtained from all participants and caregivers as part of this institutional review board–approved research.
Measurements.
Capacity to Consent to Treatment Instrument.
Patients and controls were administered the CCTI.1 As previously reported,1 the CCTI comprises two clinical vignettes (A, neoplasm; B, cardiac) and tests the capacity to consent under five LSs. After presentation of a vignette, individuals are asked a series of questions that test their capacity to consent under each of five well-established LSs.14,15 The LSs are presented below in order of increasing stringency1:
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LS1 = simply evidencing a treatment choice;
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LS2 = making the reasonable treatment choice (when the alternative is unreasonable) (vignette A only);
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LS3 = appreciating the consequences of a treatment choice;
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LS4 = providing rational reasons for a choice; and
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LS5 = understanding the treatment situation and choices.
Quantitative scoring system for the CCTI.
As previously reported,1 a detailed and well-operationalized quantitative scoring system with high inter-rater reliability was developed for the five CCTI LSs. This quantitative scoring system has demonstrated face and content validity and has successfully discriminated the LS competency performance of normal older controls as well as mild AD and moderate AD patients.1 The CCTI is a reliable and valid instrument for quantitatively assessing capacity to consent.
Qualitative scoring system for the CCTI.
We also developed a system of qualitative scores for identifying behavioral changes and errors reflected in AD patient responses on the CCTI test items. Some error codes were drawn from the Exner special scoring system for the Rorschach personality test8 (deviant verbalization, alogical reasoning, personalization),8 but were modified slightly to better represent verbal behaviors of AD patients. The majority of the error codes were developed based on the investigators’ experience with control and AD patient verbal responses produced during piloting and standardization of the CCTI. As described below, error codes were initially viewed as either benign or more pathognomonic based on this experience.
The qualitative scoring system consists of 16 mutually exclusive error codes conceptually organized into four domains: 1) language dysfunction, 2) executive dysfunction, 3) affective dysfunction, and 4) compensatory responses (table 1 provides specific definitions and actual examples for each of the codes). The individual codes were developed based on behaviors that had first been noted during pilot testing of the CCTI. They were then grouped into domains based on behavioral similarities and the authors’ initial views regarding underlying mechanisms. Codes in the language dysfunction domain identify changes in both expressive and receptive language, and range from relatively benign errors such as circumlocution and tangentiality to more serious language errors such as paraphasias and neologisms (deviant verbalization) and comprehension failures (miscomprehension). Codes in the executive dysfunction domain identify a variety of problems in verbal reasoning, processing, and production. Codes in this domain range from relatively mild factual errors (factual confusion), strained reasoning (alogical reasoning), and intrusion errors (e.g., an individual’s confusion of information between the two CCTI vignettes) to more serious processing breakdowns such as answers completely unresponsive to the question (nonresponsive answers), unintelligible responses (incoherence), loss of detachment from the hypothetical medical problem (i.e., an individual’s belief that the vignette scenario is an actual personal medical problem), and loss of task altogether. The affective dysfunction domain consists of one code for depressive verbalizations (melancholic responses). Codes in the compensatory responses domain identify behaviors that reflect potential compensatory adaptations to the competency task by both normal elderly individuals and cognitively impaired patients. These codes range from old learning adaptations such as personalized responses (personalization) and personal value statements (personal appreciation) to more problematic adaptations such as unwarranted inferences not justified by the vignette context and outright delegation of medical decision-making responsibility to others.
Error code definitions and examples by domain
Considerable effort was made to ensure that the codes identified distinct behaviors and were mutually exclusive. Detailed and well-operationalized scoring criteria were developed for each of the codes. (Because of space limitations, the scoring protocol cannot be presented here but may be obtained by contacting the first author). A conservative approach was adopted in assigning codes to verbal responses, with a maximum of one code permitted for each conceptual unit of verbal text.
Reliability of the error code scoring system.
Before the present study, we conducted a reliability study of the error codes using 23 individuals (9 controls and 14 patients with AD). Three raters trained in scoring the error codes and the CCTI LSs achieved acceptable levels of inter-rater reliability using exact agreement as a conservative criterion. Overall percentage agreement was 83.21% for vignette A (n = 322 text observations over the 23 cases), 79.79% for vignette B (n = 322 observations over 23 cases), and 81.50% for the entire protocol (n = 644 observations over 23 cases). All three raters agreed on the assignment of codes in 421 of the 644 observations. Two of the raters agreed on error codes for 155 of the remaining 223 observations, and there was no rater agreement for 68 of the observations. Because the raters were scoring complex behaviors from verbal transcripts, some classification ambiguity was expected, and we were pleased with the relatively high level of agreement obtained.
CCTI administration procedures.
The two CCTI vignettes were administered in counterbalanced order across study participants (n = 93). Each CCTI protocol was audiotaped during administration and subsequently transcribed to a written protocol before review and scoring. The results of the prior reliability study were used to train two new raters who computed LS scores and assigned error codes. All error codes for the current study’s 93 CCTI protocols were reviewed and finalized by a third, master rater (B.M.) who was blinded to diagnosis.
Neuropsychological measures.
As part of a larger study of the CCTI, neuropsychological measures commonly used in dementia evaluation were also administered to study participants. These measures represented cognitive domains linked conceptually and empirically to the capacity to consent to treatment.16,17 These domains and measures were as follows:
Attention.
Dementia Rating Scale (DRS) Attention subscale (DRS Attention),18 Wechsler Adult Intelligence Scale–Revised (WAIS-R) Digit Span,19 and DRS Construction.18
Expressive Language.
Boston Naming Test,20 Controlled Oral Word Fluency,21 Animal Naming (Animal Fluency),22 and DRS Initiation/Perseveration (DRS IP)18 (see also Executive Function domain).
Receptive Language.
Token Test,23 auditory comprehension screen,24 and reading comprehension screen.24
Short-Term Verbal Memory.
Wechsler Memory Scale–Revised (WMS-R) Logical Memory I,25 WMS-R Verbal Paired Verbal Associates I,25 and DRS Memory subscale.18
Social Comprehension/Judgment.
WAIS-R Comprehension subtest.19
Dementia Severity.
DRS total score.18
Depression.
Beck Depression Inventory.27
Statistical procedures.
Group comparisons on demographic, CCTI LS, and neuropsychological variables.
Control (n = 21) and AD (n = 72) groups were compared on demographic, CCTI LS, and neuropsychological variables using t-tests.
Group comparisons on error codes.
Incidence of error codes was compiled within-group for each LS of each vignette and for all CCTI protocols. Given the nature of the data (frequency information that was not normally distributed), nonparametric tests (Mann-Whitney28) were used. Planned comparisons of error incidence were made between control (n = 21) and AD (n = 72) groups, control and mild AD (n = 47) groups, control and moderate AD (n = 25) groups, and mild AD and moderate AD groups. These contrasts were planned so that the pattern of error behaviors across the course of AD could be examined.
Error code predictors of CCTI LS performance within group.
As previously reported,1,17 quantitative scores on the CCTI LSs were summed across vignettes A and B to create composite LS variables (LS1, LS3–LS5) (LS2 is unique to vignette A). Regression analyses were then run within control and AD groups to identify error types that predicted performance on LSs. Depending on LS score range, either logistic or linear regression was used. Because controls performed perfectly on LS1 and LS2, these LSs were not included in the analysis for controls.
Neuropsychological predictors of error codes in the AD sample.
We identified neurocognitive correlates of selected error codes in the AD sample using univariate Spearman correlation and linear regression. Error codes were selected if they were predictive of LS performance in the competency analysis (above). The neuropsychological measures with the highest univariate correlations with each specified error code were entered into regression equations to identify neurocognitive predictors of that code.
Results.
Demographic, CCTI LS, and neuropsychological variables.
Table 2 {tabft}CCTI = Capacity to Consent to Treatment Instrument; SD = standard deviation; DRS = Dementia Rating Scale; WMS-R = Wechsler Memory Scale–Revised; WAIS-R = Wechsler Adult Intelligence Scale–Revised. shows group comparisons on demographic, CCTI LS, and neuropsychological variables. The AD and control groups did not differ significantly in age. The control group had more education than the AD group (14.9 versus 13.5 years), but this difference was not thought to be clinically significant. As expected, the AD group had significantly lower scores than controls on the Mattis DRS18 total score and on the MMSE.12 Mean MMSE score was 23.5 (SD 2.2) for mild AD patients (MMSE score ≥20; n = 47) and 14.9 (SD 3.6) for moderate AD patients (MMSE score ≥10 and ≤19; n = 25). Consistent with a prior study,1 controls performed significantly better than AD patients on LS3–LS5, the three most stringent and clinically significant LSs. Controls also performed better than AD patients on the minimally stringent LS1 (evidencing a treatment choice). As expected, controls performed significantly better than AD patients on virtually all neuropsychological test measures. There was no significant difference between groups on the self-report depression inventory.
Comparisons of control and AD patient groups: Demographic, CCTI legal standard, and neuropsychological variables
Group comparisons on error codes.
Table 3 shows planned group comparisons on the error codes. Because error code occurrences are essentially frequency data, incidence rate per full CCTI protocol was thought to be the most informative manner in which to present the data. Incidence rate of an error code was defined as the total instances of that code for all CCTI protocols in a group divided by the total number of CCTI protocols in the group. Frequency of error code occurrence, and not the computed incidence rate itself, was used in subsequent analyses.
Error code incidence rates by group*
Language dysfunction codes.
Those in the AD group demonstrated more miscomprehension of questions than controls. Compared with controls, patients with moderate AD produced significantly more deviant verbalizations and miscomprehensions. The mild and moderate AD groups did not differ significantly in incidence of language dysfunction errors. There were no group differences in incidence of circumlocution or tangentiality (see table 3).
Executive dysfunction codes.
Compared with controls, patients with AD had significantly more instances of factual confusion, intrusion, incoherence, nonresponsive answer, and loss of task. Patients with mild AD demonstrated more factual confusions, intrusions, nonresponsive answers, and losses of task than controls. Patients with moderate AD produced more intrusions, incoherent responses, nonresponsive answers, and losses of task than controls. Compared with mild AD patients, moderate AD patients also demonstrated more incoherence, loss of detachment, nonresponsive answers, and loss of task. Nonresponsive answer and loss of task were the only error behaviors that discriminated all groups. There were few instances of alogical reasoning and no significant group differences on this error code (see table 3).
Affective dysfunction code.
Moderate AD patients made more melancholic responses than controls or mild AD patients (see table 3).
Compensatory response codes.
The AD group produced more delegation responses than controls, with moderate AD patients producing more delegation responses than either controls or mild AD patients. No group differences were found for personal appreciation, personalization, or unwarranted inference.
Error code predictors of LS performance within group.
Table 4 shows error type predictor models for LS competency performance in the AD group. Patients who made delegation responses were more likely to have difficulty on the minimally stringent LS1 (evidencing a treatment choice).1 Errors from the executive dysfunction domain were the primary predictors of LS2–LS5. In particular, AD patients who lost task or made nonresponsive answers were more likely to perform poorly on LS3–LS5, the most stringent and clinically relevant of the five CCTI LSs.1
Error code predictor models for CCTI legal standard (LS) performance
Similar analyses were conducted for the control group (see table 4). No models were available for LS1 and LS2 because controls performed perfectly on these LSs (see table 2). The types of errors that predicted LS3–LS5 in the control group differed from those in the AD group. Controls who demonstrated factual confusion or personalization were more likely to have difficulty with LS3 (appreciating consequences). Controls who produced more circumlocutions tended to have lower scores on LS4 (rational reasons). Controls who made the compensatory response of personal appreciation tended to score lower on the very stringent LS5 (understanding treatment situation/options).
Neuropsychological predictors of selected error codes.
Table 5 shows the neurocognitive predictors of selected error codes in the AD sample. Overall, the results offered preliminary support for the initial domain classifications of the selected error codes. Neurocognitive measures of executive function were associated with three of the four codes from the executive dysfunction domain (loss of task, loss of detachment, incoherence). A measure of spatial construction and also attention (DRS Construction18) predicted the other executive dysfunction code (nonresponsive answer). Measures of semantic memory and short-term recall predicted delegation, the only compensatory error analyzed.
Neuropsychological predictor models of error codes in the AD group (n = 72)
Discussion.
The purpose of the present study was to investigate declining medical decision-making capacity in AD patients using an error analysis of responses on a standardized competency instrument. The qualitative scoring system demonstrated acceptable reliability and provided important and unique clinical information concerning the breakdown of competency in AD. Specifically, the error analysis revealed behaviors differentially produced by older controls and AD patients, the relationship of these behaviors to competency performance, and the neurocognitive basis of these behaviors.
The error analysis indicated that older controls and AD patients produced different patterns of errors (table 3). The differences between the groups was particularly apparent for behaviors that were thought to represent executive dysfunction. Compared with controls, mild AD patients demonstrated more factual confusion, intrusions, and nonresponsive answers. In addition, mild AD patients showed problems with loss of task not seen in the control group. This finding indicates that loss of task occurs early in AD and is not simply a behavioral indication of more advanced dementia. In comparison with mild AD patients, those with moderate AD produced more nonresponsive answers and losses of task. Moderate AD patients also showed problems with incoherence and loss of detachment that were not seen in mild AD patients. Finally, moderate AD patients also showed distinctive problems in language (deviant verbalization, miscomprehension), affective function (melancholy), and compensatory responses (delegation).
The error analysis yielded insights into the emergence of pathologic behaviors across dementia stage. Even in mild AD patients, errors in the executive dysfunction domain (factual confusion, intrusion, nonresponsive answer, and loss of task) were prominent. This is consistent with recent studies demonstrating executive dysfunction early in the course of AD.29,30 In contrast, disruptive language changes (deviant verbalization, miscomprehension) emerged prominently only in moderate AD patients. Such language difficulties are consistent with the advanced anomic aphasia and the emerging receptive aphasia that are characteristic of this dementia stage.31 Similarly, delegation responses appeared primarily in moderate AD patients. These were probably compensatory adaptations for AD patients who were no longer able to encode or process information from the CCTI vignettes. Finally, patients in the moderate stage gave negative emotional responses to questions on the CCTI. We thought that such responses might be produced as the result of an underlying depression. Although symptoms of depression are commonly found in both mild and moderate AD patients,32 the patients in this study did not differ from controls in self-reported symptoms of depression. Such emotional responses may be another form of compensatory response. Patients who have lost the ability to process information cognitively might rely instead on emotional responding. The negative quality of these responses might be less related to underlying depressive symptoms than to the context of the vignette (talking about serious medical conditions and procedures might be expected to cause a negative emotional response in patients who have more difficulty remaining detached from the task).
The error analysis also identified behaviors in AD that were associated with diminished performance on the CCTI LSs (table 4). Executive dysfunction errors of loss of task and nonresponsive answers predicted AD patient performance on LS3–LS5, the most stringent and clinically relevant of the five CCTI LSs.1 Nonresponsive answers reflect information processing failures and possibly attentional dysfunction (see below). Loss of task is probably the most pathognomonic of the error behaviors because it reflects a complete disruption of task awareness and processing. Patients who lost detachment from the task were most likely to have difficulty with LS2 (capacity to make the reasonable treatment choice). Loss of detachment is also a relatively pathognomonic behavior, which reflects abstraction failure and an inability to maintain the hypothetical context of the CCTI vignette. Finally, delegation responses predicted AD patient performance on LS1 (failure to evidence a treatment choice). Although LS1 is a minimally stringent standard, moderate AD patients can have difficulty electing a treatment choice1 due to receptive aphasia and severe semantic knowledge loss.17 In such circumstances, they may compensate by delegating the treatment decision responsibility to someone else, often a physician or caregiver.
A different set of error codes predicted the performance of the older control group. Given the relatively limited number of controls (n = 21) and the number of error codes, these analyses should be considered preliminary. Here the primary predictors for LS3–LS5, the three most stringent and clinically relevant LSs,1 were relatively benign errors in the domains of executive function (factual confusion) (LS3), language (circumlocution) (LS4), and compensatory response (personal appreciation) (LS5). Specifically, difficulty correctly relating details of the vignettes’ clinical situations and reliance on personal past experiences interfered with controls’ ability to demonstrate appreciation of treatment consequences (LS3). Controls who were circumlocutious in their responding were likely to have more difficulty providing rational reasons for their treatment choice (LS4). Expression of personal, emotion-based values (rather than factual material from the vignettes) affected controls’ ability to demonstrate understanding of the treatment situation/choices (LS5). The fact that a different set of error codes predicted control performance on these three LSs indicated the sensitivity of the qualitative scoring system to both normative and pathologic cognition in older adults.
Overall, we were pleased with how well the error behaviors predicted the LS performance of the control and AD groups. Error behaviors explained a considerable amount of the variability in LS performance (30% or more for five of the eight available models) (table 4). The relatively weak prediction of performance on LS2 was not unexpected because LS2 consists of a single question that has a limited score range (0 to 1).1
A third aim of the present study was to identify neurocognitive correlates of error behaviors in the AD sample (table 5). We limited these analyses to error behaviors that were predictive of performance on the competency LSs (see above). Overall, the results offered preliminary support for the conceptual domain assignments of the error codes. First, measures of executive function were the best predictors of error behaviors in the executive dysfunction domain. Loss of task, arguably the most pathognomonic error, was predicted by Trails A.26 Trails A is a measure of visuomotor sequencing and attention that operates primarily as a measure of executive function in AD populations.17,33 Clinically, we have observed losses of task on Trails A and B that are similar to the behaviors coded as loss of task on the CCTI. Loss of detachment, another pathognomonic error behavior, was predicted by the Token Test.23 The Token Test, traditionally a test of receptive language, often functions clinically as a performance test of executive function in patients with dementia.34,35 WAIS-R Similarities,19 a measure of verbal reasoning, was a weak predictor of incoherent responses on the CCTI. Declining verbal abstraction and reasoning intuitively seem related to breakdowns in verbal responding on the CCTI task.
We were somewhat surprised that DRS Construction,18 formally a measure of simple spatial construction, was a weak predictor of nonresponsive answers. However, our clinical experience is that measures of simple construction, which tap overlearned semantic knowledge and motor learning, can behave similarly to measures of simple attention in AD patient populations.36 We interpret the present finding to suggest that nonresponsive answers may be associated, at least in part, with basic attentional lapses by AD patients on the CCTI tasks.
Delegation, a compensatory response that predicted performance on both LS1 and LS4, was associated with measures of semantic memory (Boston Naming Test20) and short-term recall (DRS Memory18). This finding supported our view of delegation as a compensatory adaptation used by moderate AD patients who have lost the conceptual basis and factual information necessary to answer the CCTI questions.
Overall, the neuropsychological results supported the conceptual group assignments of the selected error codes. Uniformly strong associations were not expected because the error analysis codes complex behaviors that are not easily captured by focal neurocognitive measures. Nonetheless, the results demonstrated the relationship of executive cognitive dysfunction to many of the executive dysfunction error codes and to the competency performance of AD patients. These findings are consistent with the growing body of literature linking the executive cognitive functions to competency.37,38
Limitations of the present study primarily involve the psychometric challenges of measuring qualitative aspects of performance. Although we tried to make our system of error codes mutually exclusive, some areas of overlap occasionally emerged during clinical usage. These scoring questions were resolved by the master rater on a best judgment basis. The nature of the data also limited the types of statistical analyses that could be performed. The error codes were essentially frequency data and were not normally distributed, and therefore nonparametric statistics were used in some instances. Finally, given the large number of error codes and neuropsychological predictors, a larger study sample would be ideal.
Acknowledgments
Supported by an Alzheimer’s Association Pilot Research Grant (PRG 91-122), an Alzheimer’s Association Investigator-Initiated Research Grant (IIRG 93-051), an Alzheimer’s Disease Center Core grant (NIH, NIA 1 P30 AG10163-1), and an Alzheimer’s Disease Program Project grant (NIH, NIA 5 P01 AG06569-05).
Acknowledgment
The authors thank Anjan Chatterjee, MD, Kelly Earnst, PhD, and Virginia Wadley, PhD, for their manuscript comments, and Dana Pender, BA, Heather Cody, BS, Lauren Hawkins, BS, and Kelly Ingram, BS, for assistance with data collection and scoring.
- Received January 25, 1999.
- Accepted July 21, 1999.
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Daniel C. Marson, Anjan Chatterjee, Kellie K. Ingram et al.Neurology, March 01, 1996