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. 2019 Jul 21;35(1):1–9. doi: 10.1093/arclin/acz027

Neuropsychological Predictors of Decision-Making Capacity in Terminally Ill Patients with Advanced Cancer

Elissa Kolva 1,✉, Barry Rosenfeld 2, Rebecca M Saracino 3
PMCID: PMC7014974  PMID: 31328219

Abstract

Objective

The purpose of this cross-sectional study was to identify the neuropsychological underpinnings of decision-making capacity in terminally ill patients with advanced cancer.

Method

Participants were 108 English-speaking adults. More than half (n = 58) of participants had a diagnosis of advanced cancer and were receiving inpatient palliative care; the rest were healthy adults. Participants completed a measure of decision-making capacity that assesses four legal standards of capacity (Choice, Understanding, Appreciation, and Reasoning), and several measures of neuropsychological functioning.

Results

Patients with terminal cancer were significantly more impaired on measures of capacity and neuropsychological functioning. Surprisingly, in the terminally ill sample, there were no significant correlations between neuropsychological functioning and decision-making capacity.

Conclusion

The terminally ill sample exhibited high levels of neuropsychological impairment across multiple cognitive domains. However, few of the measures of neuropsychological functioning were significantly associated with performance on the decisional capacity subscales in the terminally ill sample. It is possible that end-of-life decisional capacity is governed by general, rather than domain-specific, cognitive abilities.

Keywords: Decision-making capacity, Palliative care, End-of-life, Cancer

Introduction

At the end of life, terminally ill patients with cancer are often responsible for making important treatment decisions including whether to accept palliative sedation, authorize a Do Not Resuscitate order, and pursue medical aid to hasten death (Campos-Calderon et al., 2016). These decisions can directly influence both their quality, and even length of life. However, a growing body of research indicates that patients with advanced cancer are at risk for impairment in their ability to make medical decisions (Kolva et al., 2014; Kolva et al., 2018; Sorger et al., 2007; Triebel et al., 2009). Patients must weigh the risks and benefits of these critical treatment choices and effectively communicate a decision.

The ability to make competent treatment decisions is generally organized around the four most commonly used legal standards: 1) ability to express a choice (Choice), 2) ability to understand information relevant to the treatment decision (Understanding), 3) ability to appreciate the relevance of the decision (Appreciation), and 4) ability to manipulate information about the risks and benefits of the decision in light of individual goals and values (Reasoning) (Grisso & Appelbaum, 1995; Roth et al., 1977). This requires a complex set of neurocognitive functions, including the ability to receive, comprehend, process, and manipulate information (Moye et al., 2004). Many of these cognitive processes are thought to fall under the domain of executive functioning (Schillerstrom et al., 2007). In addition, memory, learning, attention, and language are thought to contribute to decision-making ability (Earnst et al., 2000; Marson et al., 1996; Moye et al., 2006; Mullaly et al., 2007).

Two studies have examined neuropsychological correlates of general ability to provide informed consent in terminally ill patients with cancer, rather than making a treatment-specific decision, or examining capacity with regard to specific legal standards. In a study of elderly terminally ill patients with cancer, Sorger and colleagues (2007) found that general cognitive functioning was associated with a measure of ability to provide informed consent, the Hopkins Competency Assessment Test (Janofsky et al., 1992). Similarly, Burton and colleagues (2012) found that in patients with cancer receiving hospice care, general ability to provide informed consent was significantly predicted by general cognitive functioning, verbal learning, verbal memory, verbal reasoning, and semantic fluency.

An emerging research literature has used more sophisticated measures of decision-making, but this research has not focused on patients at the very end of life. Triebel and colleagues (2009) and Marson and colleagues (2010) both investigated the neuropsychological correlates of specific standards of decision-making capacity in ambulatory patients (median Karnofsky Performance Scale = 90; range, 80–100) with malignant glioma. The three more complex consent standards (Understanding, Appreciation, and Reasoning) were predicted by measures of similar cognitive processes: verbal learning and memory, semantic fluency, and executive functioning. Taken together, these studies demonstrate a significant relationship between neurocognitive deficits and impaired decision-making capacity in patients with cancer.

Models of neurocognitive functioning and decision-making capacity have been developed in patients with other diagnoses such as dementia and acquired brain injury (Moye et al., 2006; Triebel et al., 2016). A review of the relationship between capacity and neuropsychological functioning found significant relationships between neuropsychological abilities and capacity to consent to treatment (Palmer & Savla, 2007). Although there is evidence of the impact of chemotherapy (Ahles & Correa, 2010; Ahles et al., 2010; Bender et al., 2006; Collins et al., 2009; Quesnel et al., 2009) and central nervous system tumors (Fox et al., 2006; Laack & Brown, 2004; Meyers & Brown, 2006) on cognitive functioning, researchers have yet to develop such models in patients with cancer. Data are lacking on the exact nature, causes, and prevalence of cognitive impairment in cancer (Horowitz et al., 2018). Furthermore, little research has focused specifically on the ability of terminally ill patients to make specific end-of-life medical decisions (Ahles & Correa, 2010).

The purpose of this study was to identify the neuropsychological underpinnings of decision-making capacity with regard to one such treatment decision that is common to patients at the end of life: the decision to accept artificial nutrition and hydration (ANH). We examined decision-making capacity with regard to the four most commonly used legal standards to evaluate competence, in cancer patients admitted to a palliative care hospital for end-of-life care. As factors unrelated to cancer (i.e., age) can also affect decisional capacity, we included a comparison group of healthy adults. Given the results of previous research, we expected that learning, memory, and executive functioning would be significantly associated with standards of medical decision-making capacity.

Materials and Methods

Informed consent was provided by 108 English-speaking adults between the ages of 50 and 89. Two groups of participants were recruited (Fig. 1). Eligibility criteria for the terminally ill group (n = 58) included diagnosis of advanced cancer, a life expectancy of less than 6 months, admission to an inpatient palliative care hospital, free of documented dementia or audio or visual impairment, and able to communicate with the research team. Eligible patients were approached for participation by members of the study team. Median participant survival time was 33 days from the time of study participation to death (M = 45.35, SD = 43.37). Healthy (not diagnosed with a life-limiting illness) adults who were free of visual or auditory impairment were eligible for participation in the comparison group (n = 50). Average participant age was 69.6 (SD = 10.1). The majority of participants were female (n = 63, 58.3%) and White (n = 64, 59.2%). Patients in the terminally ill sample had a variety of cancer diagnoses with the most prevalent being lung (n = 10, 18.2%), pancreas (n = 8, 14.5%), breast (n = 8, 14.5%), and gynecologic (n = 8, 14.5%). The terminally ill group had a significantly higher percentage of White participants (74.5%) than the comparison sample (46.0%), whereas the comparison sample had a higher percentage of Black participants (44.0%) than the terminally ill sample (23.6%), χ2 (df = 4) = 13.16, p = .01. In addition, the terminally ill sample was significantly older than the comparison sample (M = 69.60 vs. M = 65.26), t(103) = 2.07, p = .04.

Fig. 1.

Fig. 1

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CONSORT diagram.

Participants were able to provide informed consent to participate in the interview. In order to consent participate in the study, participants were able to express a choice about participating in an interview about medical decisions and completing cognitive tasks. This standard is consistent with case law on informed consent, as the acceptable threshold for participation in research can be quite low when the risks are also very low. The institutional review boards of all relevant institutions (names withheld for blind review) approved this study. We aimed to recruit a total of 74 participants (37 per group) in order to conduct analyses on differences in decision-making capacity between groups.

Missing Data

Approximately 13% of all data was missing (see Table 1). Within the comparison sample, only four participants (8.0%) were missing any of the variables of interest. In contrast, 63.7% of the terminally ill sample (n = 35) had missing data, as 15.5% (n = 9) died before completing the study, 15.5% (n = 9) became too ill, 5.2% (n = 3) were discharged prior to study completion, 19.0% (n = 11) asked to discontinue (e.g., due to fatigue, arrival of visitors, etc.), and 5.1% (n = 3) had previously undisclosed visual impairments. Thus, missing data was partly a result of disease progression and death (31.0%). Multiple imputation, using five imputed datasets based on linear regression models, was used to estimate missing values. Multiple imputation creates imputed data sets based on a series of random draws from different estimated underlying distributions (Donders et al., 2006; Schafer & Graham, 2002). This form of analysis is recommended to minimize the bias and loss of information due to missing data in clinical research (Sterne et al., 2009). In order to account for the non-random nature of some of the missing data, the reason for incomplete data (i.e., death, requested to discontinue) was included as a variable in the estimation of missing values in addition to demographic and outcome variables (Sterne et al., 2009).

Table 1.

Complete participant data

Variable Terminally ill sample
n (%)		 Comparison sample
n (%)
MacCAT-T 48 (87.3) 50 (100)
MMSE 54 (98.2) 49 (98)
HVLT-R 46 (83.6) 49 (98)
WTAR 39 (70.9) 47 (94)
CWI 29 (52.7) 46 (92)
Verbal fluency 29 (52.7) 49 (98)
Category fluency 29 (52.7) 49 (98)
Switching 29 (52.7) 49 (98)
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Note: MacCAT-T= MacArthur Competence Assessment Tool for Treatment; MMSE= Mini Mental State Exam; HVLT-R= Hopkins Verbal Learning Test—Revised; WTAR= Wechsler Test of Adult Reading; CWI= Color-Word Interference.

Measures

Decision-making capacity was assessed using the MacArthur Competence Assessment Tool, Treatment version (MacCAT-T) (Grisso et al., 1997), a semi-structured interview that assessed the four major areas of decision-making capacity (Choice, Understanding, Appreciation, and Reasoning). The MacCAT-T has demonstrated adequate interrater reliability; however, assessing validity of decisional capacity instruments is challenging, as experts often conclude that clinician assessment can be insensitive to decisional impairment (Dunn et al., 2006). Nonetheless, the MacCAT-T is well-accepted and is considered to be the gold standard of capacity assessment instruments (Melton, 2007). Participants were presented with a hypothetical treatment decision of whether to accept ANH for cachexia in the context of incurable disease. This version of the MacCAT-T was found to be feasible and acceptable in this population of terminally ill patients with cancer (Kolva et al., 2014). The measure took approximately 10 min to complete and was administered by psychology doctoral students who were also administering the neuropsychological measures. An advantage of this method of assessing decision-making capacity is that it allows for the assessment of capacity based on risk of false-positive and false-negative errors in the context of a specific decision. In the case of impaired capacity, it is important to understand if patients are choosing to accept or refuse treatment.

Although the MacCAT-T authors purport that subscale scores represent degrees of capacity and should be combined with other methods of assessment (i.e., physician interview) in making a formal determination of capacity, cut-off scores have been used in research using this and other commonly used measures of decisional capacity (Kim et al., 2001; Triebel et al., 2009). Cutoff scores were derived from the comparison group’s performance on the MacCAT-T, with impairment on each subscale based on scores that fell more than one (moderate) or two (severe) standard deviations below the mean for the comparison sample.

General cognitive functioning was assessed using the Mini-Mental State Examination (MMSE) (Folstein et al., 1975), a brief measure of cognitive functioning. Participants also completed measures of pre-morbid intellectual functioning (Wechsler Test of Adult Reading; WTAR) (Holdnack, 2001), executive functioning (Verbal Fluency, Sorting, and Color-Word Interference subtests of the Delis–Kaplan Executive Function System) (Delis et al., 2001), and learning and memory (Hopkins Verbal Learning Test-Revised; HVLT-R) (Benedict et al., 1998). Participant performance on these measures are presented as standard scores, scaled scores, or t-scores, which were adjusted for age, depending on the standard used for each measure.

Data Analysis

Many predictor and dependent variables differed significantly between the two study groups, and there was little evidence of decisional impairment in the comparison sample, data analysis focused solely on the terminally ill subgroup in order to avoid identifying proxy variables that simply reaffirmed the group differences, rather than identifying predictors of impairment within the terminally ill sample. As none of the demographic variables were strongly correlated with performance on the MacCAT-T in that subgroup, no demographic variables were included as covariates in the inferential analyses.

Two-tailed independent-samples t-tests, using Welch’s t-test due to violations of the homogeneity of variance assumption, were used to determine whether the two subgroups differed significantly in their performance on neuropsychological measures. We also compared participants who accepted treatment to those who refused. Cohen’s d was used as the measure of effect size and interpreted as small = 0.2, medium = 0.5, and large = 0.8. The correlations, using Spearman’s rho, were used to identify relationships between MacCAT-T subscale scores and neuropsychological functioning in the terminally ill sample. To reduce potential for error due to multiple comparisons, we utilized corrected significance levels (Benjamini & Hochberg, 1995). This method of controlling the false discovery rate considers the total number of rejections of H0 when balancing risk of Type I and Type II error (Chen et al., 2017)

Results

Within the terminally ill sample, performance on the measure of premorbid cognitive functioning (WTAR) indicated that participants were generally functioning within the average range (M = 100.86, SD = 14.48), and were comparable to the healthy comparison sample (M = 96.89, SD = 21.11; see Table 2). The terminally ill sample had significantly lower scores on measures of general cognitive functioning (MMSE), verbal learning and memory (HVLT-R Total Recall and Delayed Recall), processing speed (CWI Trials 1 and 2), verbal fluency (Letter Fluency and Category Fluency), and executive functioning (CWI Trial 3 and Category Switching). The effect sizes for each of the significant comparisons ranged from medium to very large (see Table 2).

Table 2.

Group differences on neuropsychological measures

Measure Terminally ill sample
(n = 55)		 Comparison sample
(n = 50)		 df Welch’s t p Cohen’s d
WTAR 100.86 (14.48) 96.89 (21.11) 103 1.10 .27 0.07
MMSE 24.31 (4.68) 27.75 (2.15) 103 −4.87 <.001 0.94
HVLT-R Total recall 27.75 (12.67) 37.47 (12.50) 103 −4.19 <.001 0.77
HVLT-R Delayed recall 27.75 (11.90) 35.63 (12.50) 103 −3.13 <.001 0.65
CWI–Trial 1 5.27 (2.91) 8.99 (3.54) 103 −5.38 <.001 1.15
CWI–Trial 2 5.65 (3.17) 9.84 (3.11) 103 −6.32 <.001 1.19
CWI–Trial 3 4.49 (3.36) 9.20 (3.45) 103 −6.86 <.001 1.33
Letter fluency 6.76 (2.48) 10.54 (4.54) 103 −5.16 <.001 1.03
Category fluency 5.26 (2.73) 10.06 (3.72) 103 −7.28 <.001 1.47
Switching 5.46 (2.51) 10.90 (3.88) 103 −8.30 <.001 1.66
MacCAT-T Choice 1.75 (0.58) 1.99 (0.09) 103 −2.70 .007 0.58
MacCAT-T Understanding 3.46 (1.35) 4.87 (0.90) 103 −6.30 <.001 1.22
MacCAT-T Appreciation 3.08 (1.15) 3.88 (0.40) 103 −4.68 <.001 0.91
MacCAT-T Reasoning 4.45 (1.83) 7.42 (0.86) 103 −9.98 <.001 2.05
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Note: MMSE= Mini Mental State Exam; HVLT-R= Hopkins Verbal Learning Test—Revised; WTAR= Wechsler Test of Adult Reading; CWI= Color-Word Interference; Switching= Category Switching. Bolded values are significant at p < .05. Cohen’s d effect sizes, small: d= 0.2, medium d= 0.5, large: d= 0.8. Corrected significance level= 0.046.

Decisional impairment was highly prevalent in the terminally ill sample. Although most participants in that sample were able to express a treatment choice (86.7%), nearly half were impaired on the Understanding (n = 23, 44.2%) and Appreciation (n = 24, 49.0%) subscales, and most (n = 44, 89.8%) were impaired on the Reasoning subscale (redacted for blind review). Three participants (5.5%) were impaired on all four subscales, and 89.8% (n = 44) were impaired on at least one.

The majority of participants in the terminally ill sample who completed the MacCAT-T stated that they would choose treatment (ANH; n = 31, 64.6%). The remaining participants either refused treatment (n = 11, 22.9%) or did not provide an answer (n = 6, 10.9%). We compared the MacCAT-T subscale scores between these three groups (Table 3). The groups differed significantly in their ability to provide a treatment choice (F(8, 54) = 36.75, p < .001). However, the groups did not significantly differ on the Understanding (F(8, 54) = 0.74, p = .66), Appreciation (F(8, 54) = 1.76, p = .11), or Reasoning (F(8, 54) = 1.50, p = .18) subscales. Of the participants who refused ANH (n = 11), one was moderately impaired on Choice (9.1%), one (9.1%) was moderately impaired and two (18.2%) were severely impaired on Understanding, six (54.5%) were severely impaired on Appreciation, and three (27.3%) were moderately impaired and six (54.5%) were severely impaired on Reasoning. Thus, using more stringent standards of capacity assessment revealed higher levels of impairment in this patient subgroup. However, similar patterns of impaired capacity were also found in the participants who chose to accept ANH as 10 (32.3%) were moderately impaired and 6 (19.4%) were severely impaired on Understanding, 13 (41.9%) were severely impaired on Appreciation, and 10 (32.3%) were moderately impaired and 16 (51.6%) severely impaired on Reasoning.

Table 3.

MacCAT-T subscale scores by treatment choice

MacCAT-T subscale Accept ANH
(n = 31)		 Refuse ANH
(n = 11)		 No choice
(n = 6)
Choice 2.00 1.91 0.33
Understanding 3.60 3.72 3.00
Appreciation 3.25 3.18 2.50
Reasoning 4.90 4.45 2.50
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Within the terminally ill sample, no significant correlations were observed between the MacCAT-T subscales and neuropsychological measures. A medium effect size was noted for the correlation between understanding and verbal memory (HVLT-R Delayed Recall; r = .31, p = .04; see Table 4), but this was not significant under our adjusted significance level. Due to the paucity of significant correlations in the terminally ill sample, we did not run any further analyses.

Table 4.

Correlations between MacCAT-T subscale scores and neuropsychological measures in the terminally ill sample

Choice Understanding Appreciation Reasoning
WTAR −.04 .12 .05 .08
MMSE .04 .18 −.06 .18
HVLT-R Total recall .06 .19 .15 .14
HVLT-R Delayed recall −.04 .29* .11 .17
CWI Trial 1 .11 0.05 −.07 .12
CWI Trial 2 −.05 .04 −.10 .05
CWI Trial 3 .10 −.15 −.09 .07
Letter fluency .02 −.13 −.13 −.01
Category fluency −.06 −.06 −.08 −.06
Switching .05 .08 .03 .14
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Note: MMSE= Mini Mental State Exam; HVLT-R= Hopkins Verbal Learning Test—Revised; WTAR= Wechsler Test of Adult Reading; CWI= Color-Word Interference; Switching= Category Switching. * p= .04, but was not significant due under adjusted significance level.

Discussion

The present study examined the neuropsychological correlates of medical decision-making capacity in a sample of terminally ill patients with advanced cancer receiving inpatient palliative care. This study builds upon earlier studies of decisional capacity in cancer patients by focusing on patients at the end of life. We used a well-established measure of capacity, the MacCAT-T. The MacCAT-T assesses decisional capacity based on the most commonly used legal standards. Patients made a hypothetical decision about whether to accept ANH for cachexia in the context of incurable disease. Most participants in the terminally ill group chose to accept ANH. Among those participants who chose to refuse treatment, many evidenced some impairment on the more rigorous standards of capacity assessment (Understanding, Appreciation, and Reasoning). However, a similar pattern of impairment was also found among those who would have accepted ANH, which also carries risks to quality of life. This highlights the importance of comprehensive capacity assessment for patients at the end of life followed by implementation of shared decision-making models as appropriate.

The two groups were not significantly different on a measure of premorbid cognitive functioning, suggesting similar overall intellectual functioning across groups. However, the terminally ill sample was significantly more impaired than the comparison sample on all other study measures of neuropsychological functioning. This is reflective of high levels of neuropsychological impairment across multiple cognitive domains. Despite these findings, and the high prevalence of cognitive impairment in this sample, none of the terminally ill participants had documented cognitive impairment in their medical record.

Although impaired decision-making was common in the sample of terminally ill patients, with nearly 90% displaying impairment on at least one of the four standards of capacity, we did not find any significant associations between neurocognitive impairment and decision-making. A medium effect size, which was not statistically significant, was identified for the association between Understanding, the ability to recall and paraphrase relevant diagnostic and treatment information, and performance on a task of delayed verbal memory. This finding lends support for the role of memory in decision-making capacity, which is unsurprising as the ability to understand information relevant to a treatment decision necessarily involves verbal learning and memory (Moye et al., 2004). Prior research with patients with malignant glioma (Marson et al., 2010; Triebel et al., 2009) also found significant associations between Understanding and verbal learning and memory, but also found associations with semantic fluency and executive functioning.

The study results depart from those of other studies of decision-making capacity and neuropsychological functioning both in patients with advanced cancer and dementia that relied on broad, general indices of decisional impairment (Burton et al., 2012; Sorger et al., 2007). The lack of significant associations between neurocognitive predictors and the other decision-making standards was unexpected. Previous studies of neuropsychological functioning and decisional capacity in less severely ill cancer patients found that Choice was significantly predicted by simple auditory comprehension (Marson et al., 1996) and attention (Okonkwo et al., 2008), neither of which were specifically assessed in this study. In fact, most studies of these relationships identified significant relationships (Palmer & Savla, 2007). Future studies of decision-making capacity and neuropsychological functioning at the end of life should include measures of these domains. These findings also diverged from previous studies of patients with cancer that found strong associations between both Appreciation (Casarett et al., 2003; Marson et al., 2010; Triebel et al., 2009) and Reasoning (Casarett et al., 2003; Dymek et al., 2001; Marson et al., 1995; Marson et al., 2010; Okonkwo et al., 2008; Triebel et al., 2009) with executive functioning, verbal learning and memory, and semantic fluency. The paucity of significant predictors is even more surprising given that a substantial number of participants were severely impaired on measures of decisional capacity and neuropsychological functioning. However, the fact that neuropsychological impairment was so widespread may have resulted in a restricted range of ability, limiting the ability to identify significant associations in the terminally ill sample.

It is possible that at the end of life, decisional capacity is not domain-specific, except for the relationship between understanding and delayed verbal memory. In fact, researchers posit that the methods used to investigate neuropsychological impairment in other types of brain injury, by identifying focal deficits, may not be as appropriate to clarify the exact nature, causes, and prevalence of cognitive impairment in cancer, and that cancer-related cognitive impairment may be more diffuse in nature (Horowitz et al., 2018). At the end of life, maximizing patient benefit from assessments while minimizing patient burden is an ever-present consideration for both researchers and clinicians. Thus, the inclusion of neuropsychological assessments, beyond brief cognitive or memory screens, may not provide additional information that helps the clinician to assess decisional capacity, adding unnecessary burden on already vulnerable patients.

Limitations

There are several limitations that affect the interpretation of the findings. The only disease-related information gathered about the terminally ill participants concerned their cancer diagnoses and prevalence of any other serious comorbid illnesses. Time since diagnosis and treatment history were not assessed, nor were disease-related characteristics (i.e., metastases to the brain). It may have been useful to assess differences in decisional capacity and cognitive functioning between participants who had chemotherapy and radiation and those who did not. Because participants had all been treated at other healthcare facilities, comprehensive medical records were not available for review. Additionally, the study did not account for the presence of medical interventions, such as analgesics, that could have influenced performance on the study measures.

The assessment battery used in this study was not as extensive as that used in other studies of the neuropsychological correlates of decisional capacity. In an effort to limit patient burden (which is paramount for patients at the end of life), the neuropsychological assessment battery was designed to be as short as possible while still providing a relatively comprehensive assessment of cognitive functioning. Unfortunately, it was not possible to include every measure of interest. For example, there was no specific measure of attention included in the battery. Given that attentional abilities underlie many other domains of cognitive functioning, it would have been useful to have this information about study participants. Despite efforts to design a brief battery to minimize patient burden, participants in the terminally ill sample had a high level of missing data.

Conclusion

This study is innovative because it is the first to compare performance on a measure of decisional capacity aligned with the most commonly used legal standards, the MacCAT-T, and a battery of neuropsychological measures, in a sample of patients at the end of life. Decisional ability impairment was substantial in this sample. These findings suggest that decision-making at the end of life may be best conceptualized as independent from the domain-specific cognitive abilities. Thus, neuropsychological batteries may not provide additional information to capacity assessments and hence, are an undue burden. The designation of a systematic method for capacity assessment in this vulnerable population will allow clinicians to better preserve the autonomy of their patients and protect them from harm while providing minimally invasive assessments at the end of life.

Funding

This research was supported by the National Cancer Institute [NCI F31CA165635].

Conflict of Interest

None declared.

Acknowledgements

Many thanks to Robert Brescia MD, Maryann Santasiero, Glynnis MacDonald PhD, and the Lincoln Square Neighborhood Center and Henry Settlement House for their help in collecting data and providing consultation, and managing the study. We would like to thank the study participants who gave of themselves to help us understand decision-making capacity at end of life.

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