Assessment of Cognitive Impairment and Decision Support in Cancer Treatment: cross- sectional observational study

doi:10.21203/rs.3.rs-2594146/v1

Download PDF

Research Article

Assessment of Cognitive Impairment and Decision Support in Cancer Treatment: cross- sectional observational study

https://doi.org/10.21203/rs.3.rs-2594146/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

This study aims to explore decision-making processes in cancer treatment in Japan. We investigated the percentage of patients who can make decisions, the rate of implementation of decision-making support actions, and the activities that contribute to decision-making.

Methods

The survey participants were 555 cancer patients (mean age 63.34 years, standard deviation, 12.76). The investigator observed the survey process in the actual examination room.

Results

The results of this study clarify the understanding of decision-making among cancer patients. Furthermore, it was found that doctors’ and nurses’ rates of implementation of supportive behaviors differed. In addition to supportive behaviors, patients’ ages were related to the decision-making process.

Conclusion

From these facts, it is more important for older adults to support more specific decision making. Doctors and nurses should divide roles and contribute to patient decision-making support in the medical field. Furthermore, they should endeavor to communicate according to patients’ personal values and dispositions.

Cognitive impairment

cancer treatment

decision-making

self-determination

Decision-making in the medical field has recently gained attention. Respect for a patient’s self-determination in terms of their plans and choice of treatment methods is fundamental to providing medical care. Advance Care Planning (ACP) is defined as “a process that supports adults at any age or stage of health in understanding and sharing their values, life goals, and preferences regarding future medical care” [1]. ACP is becoming mainstream internationally.

While supporting patients’ decision-making is an essential foundation in medicine, it is difficult to practice. A factor that plays a role is that patients avoid discussing their medical decisions [2]. It has been pointed out that they avoid topics that require specialized knowledge and dialogue with highly authoritative doctors. Additionally, even when there is discussion, there are cases where patients only broach decisions on whether to perform a medical procedure without exploring its values [3]. The purpose of ACP is to make decisions based on the values and goals of adult patients.

Although challenging to implement, ACP has excellent benefits. For example, improving the patient’s sense of control [4] and respecting the patient’s intentions enhances their family’s satisfaction and reduces anxiety and depression in bereaved families [5]. Moreover, ACP also reduces deaths [6] and improves medical communication in hospitals [7]. For these reasons, many healthcare workers strive to practice ACP, and patients also want to practice ACP with doctors [8, 9]. Therefore, many hospitals are making efforts to implement it.

To practice ACP, patients need to detail their values, life goals, and hope for medical care and share these with their families and healthcare providers. Patients need to make their own decisions about their preferred treatment plans. However, it is difficult for people with illnesses to make decisions [10].

It is indicated that they do not understand the consequences of making medical decisions as they have inadequate medical expertise [11]. Moreover, they do not remember previous choices and make different ones after a year or two [12]. Based on these points, it is clear that decision-making not based on patients’ values and goals can disrupt their medical care.

This outcome can occur when patients do not understand their healthcare workers' information. For example, Weeks et al. [13] pointed out that 70–80% of cancer patients who receive chemotherapy do not understand that their disease is incurable. Therefore, even if the doctor provides the correct information for informed consent, ACP cannot be performed unless the patient understands and agrees.

In addition, the patient may have insufficient ability to make decisions. It has been shown that about 70% of patients cannot make decisions at the end of their lives [14]. It is impossible to “decide” with an outlook for daily life when there is no physical or cognitive capacity.

In light of this, the importance of supporting decision-making for older patients has been identified as a crucial issue. For example, “Guidelines for Supporting Decision-Making in Daily Life and Social Life for People with Dementia” [15] have been established in Japan. They outline how support should be provided for each of the three stages of decision-making: forming, expressing, and realizing intentions. Internationally, practice is based on the World Medical Association Declaration of Lisbon on the Rights of the Patient, the Medical Ethics Manual, and legally on the Mental Capacity Act.

Although guidelines are being developed, hospitals still rely on the wisdom of their staff. Hirai et al. [16] pointed out that expert oncology doctors use support strategies based on an assessment to support decision-making. However, to the best of our knowledge, no studies have demonstrated that these strategies for supporting decision-making are effective in general clinical practice.

The purpose of this study is to clarify whether some of the behaviors indicated by Hirai et al. [16] as decision-making support influence the final decision regarding medical cancer treatment in Japan.

There are three reasons for focusing on cancer. First, cancer is the leading cause of death in Japan, accounting for more than a quarter of all deaths (376,392 deaths per year) [17]. Second, the literacy and understanding of cancer treatment methods are low, even though it takes many lives. Japan’s government revised the “Cancer Control Act” in 2016 so that schools, including elementary schools, provide cancer education. However, the implementation rate is only 50%, and the public’s understanding, including those of adults, is still lacking. The third is the characteristics of cancer treatment. There are many ways to treat cancer, such as surgery, chemotherapy, and radiation therapy. Choosing a treatment plan that matches the patient’s understanding of the disease and life goals is necessary. Therefore, it would be helpful to study ACP implementation and support decision-making in Japan’s cancer treatment settings.

This study aims to explore decision-making in cancer treatment in Japan. First, we investigate the extent to which some patients can or cannot make decisions and these patients’ characteristics. Second, we explore the extent to which doctors exhibit supportive behaviors in exam rooms. Third, we examine which supportive behaviors are effective for decision-making. Through these steps and from the observed data of the consultation rooms, we can empirically derive the effectiveness of decision-making support.

Participants

A total of 555 patients (272 men and 283 women; mean age 63.34, SD = 12.76) who met the inclusion criteria and attended a medical institution specializing in cancer treatment participated in the study.

Eligibility criteria: (1) diagnosis or suspicion of cancer (2) the patient plans to undergo treatment for a malignant tumor for resection, reduction, or maintenance, and (3) the treatment is feasible.

Exclusion criteria: (1) The doctor or researcher judged that the subject was in severe physical pain and that it was challenging to conduct the survey; or (2) when the doctor or the researcher judged that the subject was severely depressed and that it was challenging to conduct this survey.

Period

The observational study was conducted over six months, from November 2019 to May 2020.

Methods

The nurse who accompanied the patient to the examination room observed and evaluated the interaction between patient and doctor. In addition, the nurses themselves self-evaluated the support they provided during the interview with the patient. Each evaluation was conducted using a predetermined evaluation chart.

The nurses received guidance from the study’s authors on the research objectives and how to use the evaluation form. This training prevented any potential bias if the study’s authors were the evaluators. It also reduced any potential impact on patients.

Check List

The survey evaluation chart was divided into four categories, based on Hirai et al. [16]. The first concerned actions of the doctors and nurses and patients’ responses, including whether doctors and nurses ask patients questions about their current condition, patients’ understanding of their condition and treatment, and their reactions to these questions. In addition, we measured how doctors explain cancer and its treatments, how they adjust the amount of information they give patients, how patients respond to this information, and what new behaviors emerge from their responses.

The survey included questions about consultation-related tasks. Examples of these are "confirming the purpose and history of the patient's visit to the hospital (patient response: none, yes, unknown)" and "clearly explaining the purpose of the treatment," and activities of daily living-related tasks such as "asking questions about Instrumental Activities of Daily Living (IADL)" and "confirming how the patient usually manages his/her medication," as well as explanations such as "explaining the disease" and "explaining the advantages and disadvantages of the treatment." It also inquired about other tasks such as "matching the speed of speech" of the patient and "presenting diagrams."

The second was observing the patient’s condition, behavior, and posture in the consultation room, specifically their stability in standing and sitting positions and whether they needed assistance to walk.

Third, we observed the reactions of the patients’ families. Since family members’ words and actions can facilitate or prevent decision-making, we recorded whether the previously mentioned behaviors occurred.

The fourth was the outcome of the decision-making. We recorded whether the patient decided on “radical cure,” “maintenance of life,” or “other” as the treatment plan (“decision made”) or whether the consultation scene ended with uncertainty (“decision not made”). We also observed aspects such as those who strongly influenced the decision.

After the evaluation survey, the researcher interviewed the doctors and checked the diagnostic records to collect basic information about the patient (age, sex, diagnosis, stage of the disease, etc.), assessment of the patient’s physical and mental functioning (presence or absence of a diagnosis of dementia or mental illness, etc.), and the doctor's understanding of the patient’s treatment plan (treatment method supported by medical knowledge, etc.). Survey items are listed in Table 1.

Table 1

Content and Methods of the Survey
Level	Content	Method
Doctor and nurse actions and patient reactions to them	Based on the list of consultation behaviors, record whether they are performed and the patient response	Observation and Self-check
Patient’s physical functions	Assessment based on posture and gait
Family response	Patient family actions related to decision-making
Consequences of the final decision	Decision-making outcomes related to treatment, relevant people
Basic patient information	Basic information such as age, sex, diagnosis name, etc.	Review of medical records Hearing from the doctor
Psychosomatic function assessment	Information contained in medical records and referral letters
Individual patient information as perceived by the physician	Treatment plan, patient’s values, and results of assessment tools

Analysis

The data were converted into numerical data and analyzed using IBM SPSS Version 25 statistical analysis software.

Ethical Considerations and approval

This research involving human participants, human material, or human data, must have been performed in accordance with the Declaration of Helsinki. This experimental protocols were reviewed and approved by the Ethical Review Committee of National Cancer Center Hospital East. (No.2019 − 130). The website and bulletin board displayed the survey duration and methods so that patients could freely access this information. Contact information for refusal or discontinuation of the survey was provided. In addition, we distributed the statement to patients who requested a paper research manual and obtained their direct consent.

Participant Demographics, Cancer Type, and Treatment Plan

The observational survey was conducted continuously for six months, and data were obtained from 555 people (272 men and 283 women) who cooperated with the survey. Table 2 shows the age, type of cancer, and final treatment plan. The actual status of these cancer types is almost identical to that of the Japan Cancer Database. This finding suggests we could obtain cooperation from a reasonable target for research on older people with cancer patients in Japan.

Table 2

Participant Demographics, Cancer Type, and Treatment Plan
	N	%
All Participants	555
Sex
Male	272	49.0
Female	283	51.0
Age
20 − 29	5	0.90
30 − 39	12	2.16
40 − 49	68	12.25
50 − 59	95	17.12
60 − 69	146	26.31
70 − 79	185	33.33
80 − 89	40	7.21
90−	4	0.72
Cancer Sites
Breast	154	27.7
Lung	82	14.8
Head / Neck	63	11.4
Pancreas	63	11.4
Colorectum	45	8.1
Bile duct / Gallbladder	33	5.9
Esophagus	24	4.3
Liver	21	3.8
Stomach	20	3.6
Uterus / Ovarian	16	2.9
Kidney / Bladder	13	2.3
Lymph / Blood	12	2.2
Prostate	10	1.8
Other	4	0.7
Thyroid	2	0.4
Mesothelioma / Soft tissue tumor Tumor	2	0.4
Eye / Brain / Nerve	1	0.2
Bone	0	0.0
Skin	0	0.0
Unknown Primary	0	0.0
Treatment Plan
Radiotherapy	223	40.2
Other	100	18.0
Surgery	91	16.4
Chemotherapy	60	10.8
Chemoradiotherapy	49	8.8
Hormone Therapy	49	8.8
Palliative Therapy	28	5.0
Follow-up	12	2.2
Under Scrutiny	5	0.9

[Table 2 here]

The results were summarized to examine whether there were differences in whether patients could make decisions about their treatment plan during the consultation and afterward, depending on their age (Table 3). The term “able to make decisions” means that the patient’s decision was made voluntarily or in response to a prompt from a health care professional, based on the patient’s value statements, such as the decision of treatment method, cure, and maintenance of living level. On the contrary, “not able to decide” includes cases in which there was no clear answer about the future treatment plan and cases in which the treatment plan changes frequently.

The survey results showed that most of the patients could make decisions. However, 10% of the patients in their 70s could not make decisions, which was higher than others.

Table 3

The Linkage between Age and Decision-making Ability
	Able to make decisions		Not able to make decisions
Age	n	%	n	%
20 − 29	5	100.0	0	0.0
30 − 39	11	91.7	1	8.3
40 − 49	64	94.1	4	5.9
50 − 59	90	94.7	5	5.3
60 − 69	140	95.9	6	4.1
70 − 79	166	89.7	19	10.3
80 − 89	37	92.5	3	7.5
90−	4	100.0	0	0.0

Percent of Treatment Support Provided

We used observation records to calculate the percentage of treatment strategies for decision support practiced by expert doctors and nurses obtained from Hirai et al. [16]. The results are shown in Table 4. Doctors often take the initiative in the consultation room, and nurses are expected to follow up; therefore, doctors have a higher support strategy implementation rate, and nurses have lower ones. Doctors’ support included matters necessary for support decision-making, such as confirmation of the purpose of the visit and understanding of the treatment. In contrast, questions about IADL were found to be less than 30%. However, nearly half of the respondents asked nurses questions about IADL and self-care. Additionally, nurses did not speak about treatment explanations or prospects compared to doctors.

Table 4

Percentage of Support Strategies Implemented
	Doctors	Nurses
Confirmation of purpose and background	92%	42%
Confirmation of recognition of medical goals	94%	45%
Clarification of treatment policy and objectives	98%	65%
Confirmation of understanding of treatment	97%	75%
Understanding of IADL	28%	49%
Confirmation of self-administration of medication	24%	30%
Understanding of self-care	36%	56%
Explanation of medical conditions	92%	50%
Explanation of prognosis	36%	23%
Presentation of multiple options/no treatment	64%	42%
Explanation of the advantages and disadvantages of treatment	87%	61%
Explanation of the outlook for treatment	86%	59%
Explain any uncertainties about the problem or treatment	82%	59%
Repeat and emphasize important information	86%	81%
Confirmation of worry or concern	92%	69%
Ask for questions from the patient	93%	66%
Actively seek patient’s consent and participation in treatment	67%	37%
Explanation of how the health care provider receives the patient’s statement	57%	41%
Explanations tailored to the patient’s perception	77%	48%
Adjusting my speaking style (tone, ease of speech, speed, etc.)	72%	37%
Use of easy-to-understand expressions, letters, diagrams, and tables	76%	48%
Present objective data visually or verbally when requested by the patient	53%	32%

Examining the Factors that Contribute to Decision-making

We conducted a logistic regression analysis to determine the factors that affected the patient’s final decision, with the dependent variable being whether the decision was made.

Face items such as age, sex, cancer type, dementia, physical assessment (posture), treatment method, people involved in decision-making, supportive behavior by doctors, and supportive behavior by nurses were used as independent variables.

Analysis with all Data(N = 555)

Univariate logistic regression analysis was performed using all the data.

The results showed that the treatment plan was significantly associated with “other” (OR = 0.20, 95%CI = 0.06-0.67)and “Under scrutiny” (OR = 0.07, 95%CI = 0.01།0.49). In addition, “Oneself” was significantly related to the person involved in the decision-making process (OR = 4.91, 95%CI = 1.12།21.58).

Moreover, doctor’s support was significantly associated with “Actively seek patient’s consent and participation in treatment” (OR = 11.01, 95%CI = 2.82-42.97) and “Use of easy-to-understand expressions, letters, diagrams, and tables” (OR = 3.77, 95%CI = 1.44།9.87).

For nurses’ support, “Explanation of prognosis” (OR = 0.23, 95%CI = 0.07-0.83), “Presentation of multiple options/no treatment” (OR = 0.08, 95%CI = 0.02།0.38), and “Actively seek patient's consent and participation in treatment” (OR = 6.25, 95%CI = 1.25།31.13) were significantly related variables.

Multivariate logistic regression analysis was performed using the significant variables resulting from the univariate logistic regression analysis (Table 5). Age, sex, and cancer site were forced into the first block as adjustment variables, treatment plan into the second block, people involved in decision-making into the third block, support by doctors into the fourth block, and support by nurses into the fifth block using Ward’s method.

Table 5

Multivariate logistic regression analysis(N = 555)
Objective variable	Block	Variables	Regression coefficient	Odds ratio	95% CI			p
Decision-making	1 Attribute	Age	-0.05	0.95	0.91	-	0.99	0.011
	2 Treatment plan	Under scrutiny	-3.46	0.03	0	-	0.33	0.004
	3 People	Oneself	2.07	7.92	1.37	-	45.89	0.021
	4 Doctors’ support	Use of easy-to-understand expressions, letters, diagrams, and tables	1.38	3.99	1.49	-	10.71	0.006
	5 Nurses’ support	Presentation of multiple options/no treatment	-1.71	0.18	0.07	-	0.44	0.000
		Actively seek patient's consent and participation in treatment	1.22	3.38	1.18	-	9.68	0.020

The results showed that decision-making was influenced by patient’s age (OR = 0.95, 95% CI = 0.91-0.99), whether the treatment plan was under scrutiny (OR = 0.03, 95% CI = 0.00།0.33), whether the decision reflected the person’s values (OR = 7.92, 95% CI = 1.37།45.89), whether the doctor used easy-to-understand expressions, letters, and charts to explain (OR = 3.99, 95% CI = 1.49།10.71), whether the nurse presented multiple options or no treatment (OR = 0.18, 95% CI = 0.07།0.44), and whether the nurse actively confirmed the patient’s consent and encouraged the patient to participate in the treatment (OR = 3.38, 95% CI = 1.18།9.68).

Analysis Excluding Breast Cancer Patients(N = 401)

Since breast cancer accounted for a quarter of all cancer sites, we performed a logistic regression analysis excluding breast cancer site data to eliminate this bias. The results showed that “other” (OR = 0.12, 95% CI = 0.03–0.56) and “under scrutiny” (OR = 0.05, 95% CI = 0.01–0.37) were significantly related variables in the treatment plan.

For supportive behavior by doctors, “Explanation of medical conditions” (OR = 4.94, 95% CI = 1.06-23.09), “Explanation of prognosis” (OR = 0.22% CI = 0.07།0.71), “Presentation of multiple options/no treatment” (OR = 0.17, 95% CI = 0.04།0.78), “Actively seek patient’s consent and participation in treatment” (OR = 11.15, 95% CI = 2.58།48.26), and “Use of easy-to-understand expressions, letters, diagrams, and tables” (OR = 3.26, 95% CI = 1.45།9.26) were relevant variables.

For nurses’ supportive behaviors, “Explanation of prognosis” (OR = 0.25, 95% CI = 0.06-0.99), “Presentation of multiple options/no treatment” (OR = 0.14, 95% CI = 0.03།0.63), “Explain any uncertainties about the problem or treatment” (OR = 4.14, 95% CI = 1.15།16.92), and “Actively seek patient’s consent and participation in treatment” (OR = 7.23, 95% CI = 1.32།39.52) were significant.

Multivariate logistic regression analysis was performed using the significant variables resulting from the univariate logistic regression analysis (Table 6). Age, sex, and cancer site were forced into the first block as adjustment variables, treatment plan into the second block, people involved in decision-making into the third block, support by doctors into the fourth block, and support by nurses into the fifth block using Ward’s method.

Table 6

Results of analysis excluding breast cancer patients(N = 401)
Objective variable	Block	Variables	Regression coefficient	Odds ratio	95% CI			p
Decision-making	1 Treatment plan	Others	-3.03	.05	.02	-	.14	.000
		Under scrutiny	-4.00	.02	.00	-	.25	.003
	2 Doctors’ support	Explanation of medical conditions	1.78	5.92	1.36	-	25.76	.018
		Explanation of prognosis	-1.83	.16	.04	-	.58	.006
		Actively seek patient’s consent and participation in treatment	2.16	8.66	2.62	-	28.58	.000
		Use of easy-to-understand expressions, letters, diagrams, and tables	1.14	3.14	1.04	-	9.45	.042
	3 Nurses’ support	Presentation of multiple options/no treatment	-1.42	.24	.06	-	.98	.046
		Actively seek patient’s consent and participation in treatment	1.48	4.40	1.06	-	18.30	.042

The analysis excluding breast cancer patients showed that treatment plan is significantly related to “other” ( i.e., not an effective method like chemotherapy or non-surgical) (OR = 0.05, 95% CI = 0.02-0.14), and the current situation is not under scrutiny (OR = 0.02, 95% CI = 0.00-0.25) influence decision-making.

For doctor’s support, “Explanation of medical conditions” (OR = 5.92, 95% CI = 1.36-25.76), “Actively seek patient’s consent and participation in treatment” (OR = 8.66, 95% CI = 2.62།28.58), and “Use of easy-to-understand expressions, letters, diagrams, and tables” (OR = 3.14, 95% CI = 1.04།9.45) were significant.

Among the nurses’ support, not “Presentation of multiple options/no treatment” (OR = 0.24, 95% CI = 0.06-0.98) and “Actively seek patient’s consent and participation in treatment” (OR = 4.40, 95% CI = 1.06།18.300 were significant.

In this study, using an observational survey in a consultation setting, we examined the degree to which decision support behaviors are performed and how these behaviors affect patients’ decision-making. The results of this study clarify the understanding of decision-making among cancer patients. Furthermore, it was found that doctors' and nurses’ rates of implementation of supportive behaviors differed. In addition to supportive behaviors, patients’ age was related to the decision-making process.

An important insight gained clarified our understanding of decision-making among cancer patients. In general, autonomous decision-making among cancer patients was prevalent in all age groups. This finding may relate to the target facility's mission, specializing in cancer treatment where decision-making regarding treatment is essential. The percentage of patients in their 70s unable to make decisions increased. This finding is consistent with previous studies [13], highlighting the difficulties of decision-making in older people. This result indicates the need for enhanced decision-making support in this age group. The fact that the proportion of patients experiencing decision-making problems decreases after the age of 70 suggests that only patients with no serious issues in decision-making can reach specialized cancer hospitals.

Another significant finding elucidated the situation regarding the decision-making supportive behaviors of doctors and nurses. It demonstrated that the involvement of doctors and nurses had a positive impact on decision-making. Previous narrative studies (e.g., Hirai et al.) [16] have supported this conclusion. Behaviors such as “Explanation of medical conditions,” “Actively seek patients’ consent and participation in treatment,” and “Use of easy-to-understand expressions, letters, diagrams, and tables” supported patients’ decision-making. This finding is consistent with Joseph-Williams et al. [18], who noted that patients’ Shared Decision Making (SDM) is facilitated when treatment options are presented. The specific steps and possible outcomes of each option are explained. Joseph-Williams et al. [18] also pointed out that doctors’ use of medical terminology can limit SDM, whereas simple, non-medical language can facilitate SDM. This study highlighted the effectiveness of using information presentation methods that are easy for patients to understand. One of the factors that encouraged them to make decisions was “doctors encourage patients to participate in decision-making [19]”. Nurses also play an essential role in encouraging patients to participate in decision-making. This finding is consistent with the conclusion that nurses are expected to be actively involved in decision-making support [20].

First, it is essential to note that doctors and nurses exhibit different behaviors, influencing the patient. This realization is pivotal from the perspective of team medicine, which is the cooperation of various professions in healthcare. In a consultation, the physician often takes the initiative and is responsible for asking about the visit, explaining information to the patient, and listening to questions from the patient. Therefore, doctors’ ability to explain is one of the actions that positively influence a patient’s decision-making. Nurses, by contrast, are often required in a secondary role. Nurses’ average implementation rate of supportive behaviors was around 50%. The behaviors that had a higher rate than those of physicians were those related to matters of daily life, such as listening to IADL (nurses 49%, doctors 28%) and self-care (nurses 56%, doctors 36%). Behaviors that engaged patients also had a positive impact on their decision-making.

Second, it is essential to clarify the factors influencing patients’ decision-making using empirical methods. As Joseph-Williams et al. [18] pointed out, various patient characteristics such as age, sex, ethnic background, educational level, and low cognitive function are related to decision-making, including SDM, in Japan. Additionally, Say et al. [21] indicated that young people are more involved in decision-making than older people and that women are more active in decision-making than men. The results of this study are consistent with this finding. Therefore, it could be beneficial to flexibly arrange the nature and extent of decision support according to individual characteristics such as age, sex, and educational background.

Third, this study showed that some actions intended to support decision-making negatively impact patients’ decision-making. Specifically, doctors’ “Explanation of prognosis” and nurses’ “Presentation of multiple options/no treatment” negatively impacted decision-making. Ågård et al. [22] pointed out that many patients do not want to be informed about their prognosis. However, in a survey by Hirai et al. [16], expert doctors mentioned that communicating the prognosis at an early stage of treatment initiation encouraging patients to make decisions based on their outlook effectively supports decision-making. This finding suggests that careful consideration and timely communication of prognosis are essential. The prognosis should be discussed when the disease progresses slowly and the patient’s mental state is relatively good. Therefore, communication should suit the individual’s values and characteristics.

Limitations

This study had a few limitations. First, it was conducted at a single medical institution, reducing the generalizability of its findings. Future studies should be done in other hospitals and other regions to verify this study’s findings. Second, there was a lack of screening for dementia and other mental health issues that patients could have. Since cognitive function influences decision-making, future studies should use objective indicators of patients’ decision-making ability and cognitive function. Finally, because this was an observational study, it may not be possible to corroborate the narratives of patients and healthcare professionals. This study only examined observational data from a third party. Therefore, it is not possible to clarify the intentions and thoughts of the patients who received decision support and the healthcare professionals who provided support.

Advance Care Planning (ACP)

Instrumental Activities of Daily Living (IADL)

Shared Decision Making (SDM)

Ethical Approval and Consent to Participate

This study was reviewed and approved by the Ethical Review Committee of National Cancer Center Hospital East. (No.2019-130). The website and bulletin board displayed the survey duration and methods so that patients could freely access this information. Contact information for refusal or discontinuation of the survey was provided. In addition, we distributed the statement to patients who requested a paper research manual. Informed consent was obtained from all individual participants included in the study.

Consent for Publication

Not applicable.

Availability of Data and Materials

All data used or mentioned in the study that is publicly accessible.

Competing Interests

This study has not been published and is not under consideration for publication by another journal.

Funding

This work was supported by J Health Labour Sciences Research Grant Number 19GB1003.

Authors’ Contributions

Dr. Hirai planned the study and collected data, analyzed it, discussed it, and drafted the manuscript. Dr. Yamamura collected, analyzed, discussed, and wrote manuscripts. Ms. Suzuki analyzed the data and drafted the manuscript. Dr. Ogawa then planned the research, provided essential suggestions, and confirmed the manuscript.

Acknowledgmnts

Not applicable

Authors' Information

H.K. teaches health psychology at a university and counsels at a hospital as a Certified Public Psychologist; Y.A. teaches educational psychology at a university and is a Certified Public Psychologist; O.A. works at a hospital as a psychiatrist.

Sudore RL, Lum HD, You JJ, Hanson LC, Meier DE, Pantilat SZ, et al. Defining advance care planning for adults: a consensus definition from a multidisciplinary Delphi panel. J Pain Symptom Manage. 2017;53:821–832e1. 10.1016/j.jpainsymman.2016.12.331.
Evangelista LS, Motie M, Lombardo D, Ballard-Hernandez J, Malik S, Liao S. Does preparedness planning improve attitudes and completion of advance directives in patients with symptomatic heart failure? J Palliat Med. 2012;15:1316–20. 10.1089/jpm.2012.0228.
Anderson WG, Chase R, Pantilat SZ, Tulsky JA, Auerbach AD. Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26:359–66. 10.1007/s11606-010-1568-6.
Morrison RS, Chichin E, Carter J, Burack O, Lantz M, Meier DE. The effect of a social work intervention to enhance advance care planning documentation in the nursing home. J Am Geriatr Soc. 2005;53:290–4. 10.1111/j.1532-5415.2005.53116.x.
Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. BMJ. 2010;340:c1345. 10.1136/bmj.c1345.
Degenholtz HB, Rhee Y, Arnold RM. Brief communication: the relationship between having a living will and dying in place. Ann Intern Med. 2004;141:113–7. 10.7326/0003-4819-141-2-200407200-00009.
Teno JM, Gruneir A, Schwartz Z, Nanda A, Wetle T. Association between advance directives and quality of end-of‐life care: A national study. J Am Geriatr Soc. 2007;55:189–94. 10.1111/j.1532-5415.2007.01045.x.
Hancock K, Clayton JM, Parker SM, Wal der S, Buton PN, Carrick S, et al. Truth-telling in discussing prognosis in advanced life-limiting illnesses: a systematic review. Palliat Med. 2007;21:507–17. 10.1177/0269216307080823.
Dow LA, Matsuyama RK, Ramakrishnan V, Kuhn L, Lamont EB, Lyckholm L, Smith TJ. Paradoxes in advance care planning: the complex relationship of oncology patients, their physicians, and advance medical directives. J Clin Oncol. 2010;28:299–304. 10.1200/JCO.2009.24.6397.
Triebel KL, Gerstenecker A, Meneses K, Fiveash JB, Meyers CA, Cutter G, et al. Capacity of patients with brain metastases to make treatment decisions. Psychooncology. 2015;24:1448–55. 10.1002/pon.3753.
Sudore RL, Schillinger D, Knight SJ, Fried TR. Uncertainty about advance care planning treatment preferences among diverse older adults. J Health Commun 15 Supplement. 2010;2:159–71. 10.1080/10810730.2010.499982.
Wittink MN, Morales KH, Meoni LA, Ford DE, Wang NY, Klag MJ, Gallo JJ. Stability of preferences for end-of-life treatment after 3 years of follow-up: the Johns Hopkins Precursors Study. Arch Intern Med. 2008;168:2125–30. 10.1001/archinte.168.19.2125.
Weeks JC, Catalano PJ, Cronin A, Finkelman MD, Mack JW, Keating NL, Schrag D. Patients’ expectations about effects of chemotherapy for advanced cancer. N Engl J Med. 2012;367:1616–25. 10.1056/NEJMoa1204410.
Silveira MJ, Kim SY, Langa KM. Advance directives and outcomes of surrogate decision making before death. N Engl J Med. 2010;362:1211–8. 10.1056/NEJMsa0907901.
Ministry of Health, Labor and Welfare. Guidelines for decision-making support in daily life and social life of people with dementia. Guidelines for decision-making support. 2019. https://www.mhlw.go.jp/file/06-Seisakujouhou-12300000-Roukenkyoku/0000212396.pdf Accessed 16 Feb. 2023.
Hirai K, Yamamura A, Suzuki N, Ogawa A. Exploratory research on factors related to difficulty in decision making in elderly patients with cancer. Palliat Care Res. 2021;16:27–34. 10.2512/jspm.16.27.
Ministry of Health, Labor and Welfare. Summary of the annual vital statistics monthly report (approximate Fig. 7. ‘Number of deaths and mortality rate by cause of death by sex’). 2019. https://www.mhlw.go.jp/toukei/saikin/hw/jinkou/geppo/nengai19/dl/gaikyouR1.pdf Accessed 16 Feb. 2023.
Joseph-Williams N, Elwyn G, Edwards A. Knowledge is not power for patients: a systematic review and thematic synthesis of patient-reported barriers and facilitators to shared decision making. Patient Educ Couns. 2014;94:291–309. 10.1016/j.pec.2013.10.031.
Fraenkel L, McGraw S. What are the essential elements to enable patient participation in medical decision making? J Gen Intern Med. 2007;22:614–9. 10.1007/s11606-007-0149-9.
Nordgren S, Fridlund B. Patients’ perceptions of self-determination as expressed in the context of care. J Adv Nurs. 2001;35:117–25. 10.1046/j.1365-2648.2001.01828.x.
Say R, Murtagh M, Thomson R. Patients’ preference for involvement in medical decision making: a narrative review. Patient Educ Couns. 2006;60:102–14. 10.1016/j.pec.2005.02.003.
Ågård A, Hermerén G, Herlitz J. When is a patient with heart failure adequately informed? A study of patients’ knowledge of and attitudes toward medical information. Heart Lung. 2004;33:219–26. 10.1016/j.hrtlng.2004.02.003.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Assessment of Cognitive Impairment and Decision Support in Cancer Treatment: cross- sectional observational study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Participants

Period

Methods

Check List

Analysis

Ethical Considerations and approval

Result

Participant Demographics, Cancer Type, and Treatment Plan

Percent of Treatment Support Provided

Examining the Factors that Contribute to Decision-making

Analysis with all Data(N = 555)

Analysis Excluding Breast Cancer Patients(N = 401)

Discussion

Limitations

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1