Focusing on age‐related factors, this study aimed to identify characteristics that can be used to identify patients at high risk of impaired decision‐making capacity, for whom additional safeguards would be appropriate, and to investigate the accuracy of physicians' perception of the patients' impaired decision‐making capacity.
Keywords: Cancer, Decision‐making capacity, Informed consent, Supportive care, Frailty
Abstract
Background.
The objective of this study was to assess decision‐making capacity in patients newly diagnosed with lung cancer, clinical factors associated with impaired capacity, and physicians’ perceptions of patients’ decision‐making capacity.
Materials and Methods.
We recruited 122 patients newly diagnosed with lung cancer. One hundred fourteen completed the assessment. All patients were receiving a combination of treatments (e.g., chemotherapy, chemo‐radiotherapy, or targeted therapy). Decision‐making capacity was assessed using the MacArthur Competence Tool for Treatment. Cognitive impairment, depressive symptoms, and frailty were also evaluated. Physicians’ perceptions were compared with the ascertainments.
Results.
Twenty‐seven (24%, 95% confidence interval [CI], 16–31) patients were judged to have incapacity. Clinical teams had difficulty in judging six (22.2%) patients for incapacity. Logistic regression identified frailty (odds ratio, 3.51; 95% CI, 1.13–10.8) and cognitive impairment (odds ratio, 5.45; 95% CI, 1.26–23.6) as the factors associated with decision‐making incapacity. Brain metastasis, emphysema, and depression were not associated with decision‐making incapacity.
Conclusion.
A substantial proportion of patients diagnosed with lung cancer show impairments in their capacity to make a medical decision. Assessment of cognitive impairment and frailty may provide appropriate decision‐making frameworks to act in the best interest of patients.
Implications for Practice.
Decision‐making capacity is the cornerstone of clinical practice. A substantial proportion of patients with cancer show impairments in their capacity to make a medical decision. Assessment of cognitive impairment and frailty may provide appropriate decision‐making frameworks to act in the best interest of patients.
Introduction
Decision‐making capacity is the basis for personal autonomy. Decision‐making capacity is the ability to select among treatment alternatives or to refuse treatment, and refers to a patient's cognitive and emotional capacity [1], [2], [3]. Decision‐making capacity consists of four components: (a) understanding information relevant to the decision, (b) appreciating the information (applying the information to one's own situation), (c) reasoning with the information, and (d) expressing a consistent choice [4].
Decision‐making capacity is the cornerstone of ethical clinical practice. To make informed decisions, ethically sound clinical care involves allowing patients the maximum level of autonomy and participation in their treatment decisions and protecting patients who are incapable or marginally capable of making their own medical decisions [4].
A recent systematic review of investigations showed that 26%–34% of inpatients with medical illness, excluding only those with severe mental illness, had significantly impaired decision‐making capacity [5]. Clinicians often overestimate their patient's decision‐making capacity [6], [7]. This is partly because there is limited information available to aid clinicians in estimating the expected prevalence of incapacity in their clinical settings. Formal assessments of decision‐making capacity are time‐consuming and require considerable training. Existing instruments to evaluate decision‐making capacity are research tools rather than clinical tools and are frequently burdensome. Therefore, it is important to assess the clinical characteristics associated with impaired decision‐making capacity and develop clinically relevant instruments to assess the risk factors for impaired decision‐making capacity in order to ensure that clinicians are able to identify patients for whom a capacity assessment is warranted and those for whom an enhancement approach is needed.
Cancer and aging are closely related. The cancer incidence increases with age, with most cancer diagnoses occurring in patients aged 65 years and older. Cognitive impairment and depression are highly prevalent among older adults [8]. Both factors impaired decision‐making capacity. In particular, cognitive impairment is a common factor in reduced capacity. In developed countries, as the elderly population is growing, the number of people with dementia has increased and is estimated to be over 65 million in 2030 [9]. Previous studies showed that nearly all individuals with Alzheimer's disease and approximately half of older people with mild cognitive impairment have impaired decision‐making capacity [10]. Depression is common in patients with cancer. The prevalence of depression is between 5% and 13% in cancer patients and most of these patients go untreated [11] even though depression can threaten decision‐making capacity [12].
In addition, brain metastases occur in approximately 25% of adult patients with cancer [13]. Patients with brain metastases have shown significantly poorer understanding compared with demographically matched healthy controls, with nearly 50% classified as impaired in their understanding of medical decisions [14]. Given the complexities of treating cancer in an elderly population, medical decision‐making capacity has significant implications for the patients and their physicians.
Lung cancer is the leading cause of cancer‐related deaths in the world, with more than 1 million deaths reported each year [15]. In Japan, approximately 133,200 new cases of lung cancer were diagnosed in the year 2016 [16]. Elderly patients (aged 70 years or more) account for about 60% of all lung cancers in Japan. Among these, 30% of the patients are 80 years of age or older. Because the population over 65 years constitutes the fastest growing demographic segment of advanced countries, the number of cases of lung cancer diagnosed in elderly patients is expected to increase in the near future.
Lung cancer presents at an advanced stage in a majority of patients. Despite the advent of novel therapies, metastatic lung cancer remains an incurable disease that causes significant morbidity [17], [18], [19]. Patients must confront a short life expectancy while receiving intensive chemotherapy. The prevalence of comorbid depression is higher than that for other cancer sites. In addition, brain metastases occur in approximately 16%–20% of the population with lung cancer. Systemic chemotherapy is associated with only modest survival and quality‐of‐life benefits [19], [20], [21].
To make informed decisions about whether to receive chemotherapy, patients with advanced lung cancer need a realistic understanding of their prognosis and the goals of cancer therapy [22], [23], [24]. However, some patients do not have enough decision‐making capacity to give informed consent. To our knowledge, there are few data available with respect to patients’ decision‐making capacity to consent for chemotherapy among patients newly diagnosed with lung cancer.
The purpose of this study was to identify characteristics, focused on aging‐related factors, that can be used to identify patients at high risk of impaired decision‐making capacity, for whom additional safeguards would be appropriate, and to investigate the accuracy of physicians’ perception of the patients' impaired decision‐making capacity.
Materials and Methods
Subjects
We obtained institutional review board approval for this study. All newly diagnosed patients with lung cancer from June 2011 to July 2012 were consecutively identified at a weekly multidisciplinary case conference. Inclusion criteria were as follows: being older than 20 years of age, fluency in Japanese, an Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0–2, having opted to receive chemotherapy, and demonstrating no serious physical or psychological distress as recognized by the attending physicians or researchers. We focused on the patients on ECOG PS 0–2 in order to assess the patients indicating for the standard treatments. Participants who met these criteria were approached after they were given their diagnosis and made informed choices about their treatment. We obtained written consent from all participants after full disclosure of the study purpose.
Procedure
All participants completed a consent capacity measure, a battery of neurocognitive tests, and an assessment of their frailty. We obtained demographic and medical information such as diagnosis, disease status, total medications, comorbidity, and Brinkman index (smoking history) from participant medical charts.
Measurement of Decision‐Making Capacity
The assessment of decision‐making capacity is time‐ and decision‐specific. Patients were assessed within a week of making their treatment decision.
We used the MacArthur Competence Assessment Tool for Treatment (MacCAT‐T) [25] to assess decision‐making capacity. The MacCAT‐T is a semi‐structured interview that measures decision‐making capacity by assessing four well‐established domains: (a) understanding of the disorder and its treatment, (b) appreciation of the disorder and its treatment, (c) reasoning (i.e., the processes behind the decision) and ability to compare alternatives, and (d) the ability to express a choice. Understanding, appreciation, and reasoning are scored on scales of 0–6, 0–4, and 0–8, respectively. The MacCAT‐T has been validated primarily in psychiatric illness and dementia, but was designed to be generalized to decision‐making regardless of illness type.
The strength of the MacCAT‐T is that the interview is individualized for the participant's current situation. The MacCAT‐T is not the only available assessment tool for decision‐making capacity, but has emerged as the standard with the most empirical support. The MacCAT‐T provides an indication of the adequacy in an individual's abilities to deal with information and decisions about their own illness and treatment. The MacCAT‐T shows areas of relative capacity or incapacity and is to be interpreted in the context of other clinical information.
The instrument's disclosure elements are specific to the participant's decision‐making situation and must be adapted by the evaluator. During the interview, the evaluator provided clinical information regarding the participant's condition, recommended treatment, and provided information about the associated risks and benefits of the treatment. The discussion about the diagnosis, assessments, and treatment plan that the medical team described was confirmed by the recording.
Consent Standards
As part of our assessments, we discussed information on the risks and benefits of treatment with the clinical team, which consisted of the resident or fellow and his supervisor (a senior physician). This information was provided in a structured manner during the course of the MacCAT‐T.
Interviews were audiotaped and transcribed. Two researchers (two consultant psychiatrists) independently scored a subsample of 30 transcriptions to assess inter‐rater reliability. We obtained good inter‐rater reliability (mean kappa of 0.82, range 0.61–1.00, all p values <.001) across all four standards.
The researcher (one of two consultant psychiatrists) ascertained whether participants had decision‐making capacity based on their performance in the interview. After this basic ascertainment, a multidisciplinary team consisting of two consultation psychiatrists, a clinical psychologist, and a nurse made a consensus decision based on typed transcripts of the interview. The group had transcripts of the MacCAT‐T but no information about cognitive state.
Neurocognitive Test Battery
We considered cognitive impairment, frailty, and depression as candidates for factors related to impaired decision‐making capacity.
We used the Mini‐Mental State Examination (MMSE) as a standardized basic assessment of cognitive abilities [26]. A score of 23 or less suggests cognitive impairment.
The Frontal Assessment Battery (FAB) is a brief frontal lobe function testing method developed in 2000. It is composed of six domains: similarities, lexical fluency, motor series, conflicting instructions, go/no‐go, and prehension behavior [27]. The FAB has been validated in samples of patients with various degrees of frontal lobe dysfunction and can be administered at bedside in under 10 minutes without using special equipment. An FAB cutoff of 12/18 was reported useful in the early differentiation of dementia.
In addition, we assessed depression with the Patient Health Questionnaire‐9 (PHQ‐9) [28]. The PHQ‐9 is a nine‐item self‐report instrument developed to screen for major depressive disorder in primary care settings, using Diagnostic and Statistical Manual of Mental Disorders, 4th Edition criteria. The PHQ‐9 has been validated in samples of patients with cancer and used in clinical trials of this population [29], [30].
Frailty
The Vulnerable Elders Survey‐13 (VES‐13) is a 13‐item function‐based scoring tool used to identify older people with increased degrees of frailty. A score of 3 or higher on the VES‐13 indicates frailty. Previous research has shown that vulnerable patients are at 4.2‐fold greater risk of dying during the following 2 years [31], [32].
Clinical Impression
To assess the clinicians’ impression of a patient's ability to make informed choices, we asked the clinical teams the following question: “Do you think the patient is able to make decisions regarding his or her current chemotherapy?” The respondents were unaware of the results of the cognitive ability and decision‐making capacity assessments. The sensitivity and specificity of their evaluation of decision‐making capacity to treatment were assessed.
Statistical Analysis
The MacCAT‐T, MMSE, FAB, PHQ‐9, and VES‐13 were scored according to established guidelines [25], [26], [27], [32]. The goals of our analyses were to assess the independent relationships of decision‐making capacity to other characteristics. We compared the participants who were judged with decision‐making capacity with those judged with impaired decision‐making capacity, using the chi‐square test for categorical variables and Mann‐Whitney U tests and t tests for continuous variables. Logistic regression analysis was used to identify independent associations for impaired decision‐making capacity. We left variables capable of explaining the significant variance in the capacity status by themselves and entered sociodemographic variables followed by medical variables (total score on the MMSE, total score on the VES‐13) [28]. We did not include FAB score in the analysis to assess global cognitive function and avoid collinearity. Finally, we added capacity ratings by the clinical team.
Results
Of the patients who were identified for the study, 173 met the eligibility criteria. Of these, 50 refused to participate, 1 could not be approached, and 122 patients consented to participate. Eight were not fully assessed because of a change in their physical condition. The mean (± standard deviation [SD]) age of these participating patients was 64.9 ± 9.6 years (median 67, range 25–81); 26 (22.8%) were women; 100 (87.7%) were married. The mean years of education (± SD) was 11.6 ± 3.7 years. Patient demographics did not differ significantly between study participants and eligible patients who did not enroll (mean age, 65.0 years, median 65, range 25–81).
Prevalence of Incapacity for Medical Decision‐Making
Of the participating patients (n = 114), 27 (23.7%) were judged to have impaired decision‐making capacity.
Description of the Sample
The patients’ sociodemographic characteristics are summarized in Table 1. Patients with impaired decision‐making capacity were older and had fewer years of education than those with capacity. No association was found between sex, marital status, and decision‐making capacity. In addition, no association was identified between decision‐making capacity and interview time with the physicians, accompanying family, or the interval time between discussion and assessment.
Table 1. Demographic characteristics.
Bolded values show p values ≤.05.
Abbreviation: SD, standard deviation.
Table 2 shows the clinical characteristics of the patients. No difference was found between those with decision‐making capacity and those with impaired decision‐making capacity in diagnosis, staging, or ECOG performance status. No difference was noted in rates of brain metastasis or emphysema. Patients with impaired decision‐making capacity were more likely to be prescribed polypharmacy, but no difference was noted in the rates of opioid or psychotropic prescriptions. Patients with impaired decision‐making capacity had significantly lower scores on the MMSE (mean score 25.0 vs. 27.5, p = .018), FAB (mean score 12.3 vs. 14.7, p = .03), and the VES‐13 (mean score 3.1 vs. 1.5, p = .002), indicating higher rates of cognitive impairment, deficit in executive function, and frailty, respectively. No significant association was identified between PHQ‐9 score and decision‐making capacity.
Table 2. Clinical characteristics.
Bolded values show p values ≤.05.
Abbreviations: ECOG, Eastern Cooperative Oncology Group; FAB, frontal assessment battery; MacCAT‐T, MacArthur Competence Assessment Tool for Treatment; MMSE, mini‐mental state examination; NSCLC, non‐small cell lung cancer; PHQ‐9, patient health questionnaire‐9; SCLC, small cell lung cancer; SD, standard deviation; VES‐13, the vulnerable elders survey‐13.
Capacity Ratings on the MacCAT‐T
Table 3 presents impairment ratings of the MacCAT‐T. Patients with impaired decision‐making capacity scored significantly below those with capacity in the areas of understanding, appreciation, and reasoning. We considered the opinions of the medical team (resident and supervisor) individually. Compared with the assessment of the decision‐making capacity, the opinions of the medical team showed a sensitivity and specificity of 91.9% and 22.2%, respectively, and a positive predictive value and negative predictive value of 87.0% and 42.9%, respectively. Among the patients judged to have decision‐making capacity by the medical team, 21% were considered incapable of giving consent based on the assessment.
Table 3. Individual capacity ratings and cognition, frailty.
Abbreviations: MacCAT‐T, MacArthur Competence Assessment Tool for Treatment; MMSE, mini‐mental state examination; VES‐13, the vulnerable elders survey‐13.
Associations of Incapacity
To determine which variables were independently associated with impaired decision‐making capacity, we conducted a logistical analysis (Table 4). Cognitive impairment (<24 on the MMSE) and frailty (<3 on the VES‐13) were significantly related to capacity to consent. No association was noted between age and capacity to consent.
Table 4. Association of impaired decision‐making capacity.
Abbreviations: CI, confidence interval; MMSE, mini‐mental state examination; VES‐13, the vulnerable elders survey‐13.
Discussion
We found that one quarter of the patients with lung cancer who had opted to receive first‐line chemotherapy had impaired capacity to make treatment decisions. They performed more poorly on complex and clinically relevant indicators of understanding, appreciation, and reasoning. In addition, impaired decision‐making capacity in patients was rarely detected by clinical teams (specificity: 22.2%; negative predictive value: 42.9%). Cognitive impairment and frailty were independently associated with impaired decision‐making capacity. The number of prescribed drugs was significant in univariate analysis, but not in multivariate analysis. Brain metastasis, emphysema, opioid use, and psychotropic use were not found to be significant predictive factors of impaired decision‐making capacity.
Regarding the relationship between neurocognitive function and decision‐making capacity, patients with significant cognitive impairment performed significantly worse on the assessment of decision‐making capacity than those without cognitive impairment. These results suggest that general cognitive function and frontal lobe function predict decision‐making capacity.
An interesting finding of this study was that frailty was associated with decision‐making capacity. Frailty is a biologic syndrome of decreased reserve and resistance to stressors, causing vulnerability to adverse outcomes [33]. Frailty is associated with clinical symptoms, such as unintentional weight loss, self‐reported exhaustion, weakness, slow walking speed, and low physical activity. The association between frailty and decision‐making incapacity, independent from cognitive impairment, may be due to comorbidities, sensorial impairment, factors associated with exhaustion, and social environment.
Frailty is assessed by the components of the Comprehensive Geriatric Assessment (CGA), recommended to be implemented in cancer clinical settings. CGA is defined as a multidimensional diagnostic process aimed at determining the medical, psychological, and functional capabilities of older patients in order to judge the indication for treatment, and is useful in predicting postoperative complications and overall survival. In light of most cancer diagnoses occurring in patients aged 65 years and older, evaluating the underlying causes of frailty could help clarify this association. In this way, the CGA provides the opportunity to assess the needs of enhancement of decision‐making capacity among cancer patients. Our study suggests that strategies to enhance decision‐making capacity would be beneficial for patients with frailty in clinical settings.
The prevalence of impaired decision‐making capacity among our cancer patients was consistent with previous studies in general medical situations. Raymont et al. reported that 40%–48% of acutely admitted general medical inpatients had medical incapacity, and incapacity increased with increasing age [7]. It appears that, consistent with the published investigations, impaired decision‐making capacity is a significant problem in patients with cancer.
We found that impaired decision‐making capacity is under‐recognized, consistent with previous studies. Clinical teams often assume capacity unless there is strong reason to suspect otherwise. One of the reasons for overestimation of patient decision‐making capacity may be the proposition that all patients should be treated as if they have capacity, unless proven to have impaired decision‐making capacity. It is only when capacity is actively assessed that the widespread problem becomes apparent, such as that refusal of treatment does not necessarily indicate impaired decision‐making capacity.
Currently, a standardized assessment method is unavailable to identify patients who need to be supported during decision making. On the other hand, the prevalence of cognitive impairment was high, with a strong association with a lack of capacity. Our results suggest the assessment of cognitive impairment is the key factor in impaired decision‐making capacity, consistent with previous studies. Although clinicians would expect cognitive function to reflect decision‐making capacity, cognitive function is not equal to decision‐making capacity, and the correlation is not always close. Further investigation is required to clarify the effects of reduced cognitive function.
The present study has several limitations. First, formal diagnoses of dementia or delirium were not made, and psychiatric comorbidity (except depression) was not analyzed. Second, our investigation was conducted in a single institution and the sample size was small, so the findings may not be generalizable to other lung cancer populations. Third, we did not evaluate decision‐making capacity in all patients who were identified as eligible for the study. Some patients who refused to participate may have had cognitive impairments, which made it difficult for them to understand or engage in the research study, thereby skewing the sample. In addition, the MacCAT‐T is not designed to give a definitive decision on the presence of decision‐making capacity. The MacCAT‐T should be used as an indicator for the possibility of impaired capacity in decision‐making [25]. In addition, little is known about how clinicians’ judgment of capacity is affected by their perception of the risks and benefits of the proposed treatment.
On the other hand, our study has several strengths. Specifically, we assessed decision‐making capacity by using a validated semi‐structured interview, evaluated each domain of capacity, and used recordings to confirm the details of discussions with attending physicians. Inter‐rater reliability was also confirmed in this study. Patients were consecutively recruited, and the refusal rate was within accepted parameters.
Conclusion
Decision‐making capacity is the cornerstone of clinical practice. The present findings should be helpful in informing discussions regarding the adequacy of informed consent in patients with impaired decision‐making capacity. Patients who have difficulty understanding or appreciating information may benefit from enhancement support, such as summaries of information, receiving information through multiple methods (e.g., hearing, seeing), and being provided enough time and opportunity to paraphrase what was explained and to review information again. Further studies to improve the identification of impaired decision‐making capacity are necessary to build on these findings.
Acknowledgments
We thank Kiyoko Otani, Miwa Ito, Ado Tange, Kyoko Hayashi, Aya Makino, and Akihiro Nitto for their assistance in conducting this study. This work was supported by a Grant‐in‐aid for Cancer Research from the Japanese Ministry of Labor, Health, and Welfare.
Author Contributions
Conception/design: Asao Ogawa, Kyoko Kondo, Hiroyuki Takei, Daisuke Fujisawa, Yuichiro Ohe, Tatsuo Akechi
Collection and/or assembly of data: Asao Ogawa, Kyoko Kondo, Yuichiro Ohe
Data analysis and interpretation: Asao Ogawa, Daisuke Fujisawa, Hiroyuki Takei, Yuichiro Ohe, Tatsuo Akechi
Manuscript writing: Asao Ogawa, Tatsuo Akechi
Final approval of manuscript: Asao Ogawa, Kyoko Kondo, Hiroyuki Takei, Daisuke Fujisawa, Yuichiro Ohe, Tatsuo Akechi
Disclosures
Yuichiro Ohe: Chugai (E [family member]), Ono Pharmaceutical Co. (OI [family member]), AstraZeneca, Chugai, Eli Lilly & Co., Ono Pharmaceutical Co., Bristol‐Myers Squibb, Daiichi‐Sankyo, Nipponkayaku, Boehringer Ingelheim, Bayer, Pfizer, Merck Sharp & Dohme, Taiho, Clovis, Sanofi (H), AstraZeneca, Chugai, Eli Lilly & Co., Ono Pharmaceutical Co., Novartis (C/A), AstraZeneca, Chugai, Eli Lilly & Co., Ono Pharmaceutical Co., Bristol‐Myers Squibb, Kyorin, Dainippon Sumitomo, Pfizer, Taiho, Novartis, Merck Serono (RF); AstraZeneca (ET); Tatsuo Akechi: Daiichi‐Sankyo, Eizai, Hisamitsu, Eli Lilly & Co., Merck Sharp & Dohme, Meiji, Mochida, Otsuka, Pfizer, Novartis, Terumo, Igaku‐Shoin, Nanzando, Nankodo (H). The other authors indicated no financial relationships.
(C/A) Consulting/advisory relationship; (RF) Research funding; (E) Employment; (ET) Expert testimony; (H) Honoraria received; (OI) Ownership interests; (IP) Intellectual property rights/inventor/patent holder; (SAB) Scientific advisory board
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