Role of pharmacists in providing information on cancer genome medicine and patient decision making：open-label randomized controlled study

doi:10.21203/rs.3.rs-4913510/v1

Download PDF

Research Article

Role of pharmacists in providing information on cancer genome medicine and patient decision making：open-label randomized controlled study

https://doi.org/10.21203/rs.3.rs-4913510/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

Cancer genome medicine based on genetic mutations is developing globally, and comprehensive genome profiling (CGP) are now routinely performed in Japan. Patient expectations are high for new treatment modalities based on genetic testing, but these are mostly unknown in Japan. The role of pharmacists in cancer genome medicine has not yet been established, but they can be a potential source of reliable information on cancer genome medicine, thus improving the health literacy of patients and supporting their decision making.

Methods

This was an open-label randomized controlled study. Subjects were randomly assigned 1:1 to the intervention and non-intervention groups, with treatment objective (preoperative and postoperative/advanced recurrence) as a stratification factor. In the non-intervention group, the pharmacist provided explanations about the drugs as done in usual care. In addition, the intervention group was provided with information on CGP. The primary endpoint was the impact of the pharmacist’s intervention on the patient’s decision making, measured by the Decisional Conflict Scale (DCS). As a secondary endpoint, factors influencing patient’s decision making were also analyzed.

Results

The analysis included responses from 180 patients. The pharmacist interview lasted 19.9 ± 6.9 and 10.4 ± 4.3 minutes for the intervention and non-intervention groups, respectively. The percentage of patients whose DCS scores decreased before and after the pharmacist interview was not significant between the intervention versus non-intervention groups (49.4% vs. 41.9%; p = 0.313, test of proportions). However, the DCS score of the intervention and non-intervention groups increased by 1.98 ± 16.5 and 3.61 ± 17.3, respectively. Both groups exhibited an increase in decisional conflict about receiving treatment, including CGP, but the change was significantly smaller in the intervention group (p = 0.026, unpaired t-test). Factors that influenced patient’s decision making through pharmacist intervention included age, chemotherapy being the primary treatment and having no history of nonsurgical treatment, such as chemotherapy, radiofrequency ablation, transcatheter arterial chemoembolization, and microwave ablation.

Conclusions

Increased conflicts is reduced when pharmacists provide information about CGP to patients undergoing cancer chemotherapy. This can help patients make informed decisions about their treatment choices.

cancer genome medicine

precision medicine

comprehensive genome profiling

pharmacist

decision making

information provision

Cancer is the leading cause of death worldwide, with approximately 10 million deaths by 2022 [1]. Lung cancer is the most common form of cancer, followed by breast, colorectal, and prostate cancers. In Japan, approximately 380,000 people died in 2021 [2], mostly from lung, colorectal, and gastric cancers, and disease progression due to distant metastasis will be the main cause of death [3]. Hereditary cancer predisposition syndromes also account for 5–10% of all cancers [4].

The main treatments for cancer include surgery and radiation therapy, forms of local therapies, along with drug therapy, a form of systemic therapy. Depending on the stage of the disease, these treatments may be administered alone or in combination with the help of a multidisciplinary team. However, in cases of advanced recurrence and where the disease has spread throughout the body, drug therapy is the mainstay treatment used. Drug therapy for metastatic, recurrent, and advanced solid tumors rarely cure the disease; hence, improving treatment outcomes is one of the major challenges in cancer treatment. One approach to overcome this challenge is the development of molecular targeted therapy based on the molecular biology of cancer. Trastuzumab [5] and imatinib [6] were developed as specific inhibitors of tumor cell-causing driver mutations in early 2000s. Many driver mutations have been identified and molecular targeted drugs been developed since then. Furthermore, it has become clear that driver mutations, which were initially identified in each organ, are commonly found across organs [7]. This gave rise to a new treatment approach based on genetic mutations, or tumor-agnostic therapy, which led to the development of various therapeutic methods. However, to determine the indication for such molecularly targeted agents, it is necessary to perform a comprehensive analysis of gene mutation profiling extracted from tumor specimens of individual patients.

Given this background, in November 2017, comprehensive genome profiling (CGP) was adopted in the United States as a method to comprehensively collect mutation profiles in tumor cells of patients with advanced recurrent solid tumors [8]. American Society of Health-System Pharmacists has published a statement on the Pharmacist’s Role in Clinical Pharmacogenomics [9]. The statement recommends the choice of drug treatment based on the results of pharmacogenomic testing. In addition, it mentions the need for information provision and empowering patients to help them performing pharmacogenomic testing and understanding the results. However, these are views on genetic discrimination by health insurers or employers [10], it is unclear the association between patient health literacy and decision making. Furthermore, the role of pharmacists in the precision oncology, as a member of a multidisciplinary team, has reported [11], on the other hand, there are little involvement for patients who consider undergoing CGP.

Subsequently, in June 2019, CGP was covered by insurance in Japan. As of 2024, cancer genome medicine is available in 268 institutions, designated by the Ministry of Health, Labour, and Welfare; with their roles divided into core cancer genome hospitals, hub hospitals, and cooperative hospitals [12]. Facilities not designated by Ministry cannot provide cancer genomic medicine. Therefore, cancer genome medicine is not yet widely known in Japan. Since CGP is intended for patients with completed standard treatment and those with rare cancers, patients have high expectations of curing from the test. However, even if a genetic mutation is found, an effective drug may not be found. Furthermore, even if a candidate drug is found, it may not be approved and administered in Japan. Drugs under development, such as investigational drugs, may also act as candidate drugs. It takes several weeks from the time of application for the test until the availability of results. Considering the general condition of patients with completed standard treatment, it is highly unlikely that treatment will be available. Currently, it is estimated that 10–15% of patients who undergo CGP actually receive treatment [13]. Some patients may be hesitant to perform CGP for financial reasons as expensive cost at 560,000 yen (about 3,700 USD). In addition, the timing of CGP is important since insurance reimbursement allows only one CGP per cancer.

Information on precautions for such tests is provided on patient-oriented websites and other sources. However, it is unclear whether patients have access to this information or correctly understand it. In a public opinion survey on cancer control conducted by the Cabinet Office in 2019, most respondents (66.4%) cited doctors and nurses at hospitals and clinics as their source of information on treatment and hospitals when diagnosed with cancer, followed by the internet (36.9%) and family, friends, and acquaintances (33.8%) [14]. The most recent survey, conducted in 2023, had a different survey methodology due to the COVID-19 pandemic, and thus, it is difficult to compare it with past reports. Nevertheless, the latest report still had majority citing doctors and nurses at hospitals and clinics (56.2%), followed again by the internet (26.2%) and by family, friends, and acquaintances (36.7%) [14]. In Japan, medical institutions have established consultation support centers and other contact points. However, it is expected that many patients will not use such contact points, but rather, rely on the doctors and nurses involved in their visit as their source of information.

In Japan, pharmacists have many opportunities to work directly with cancer patients. Both inpatients and outpatients can have their chemotherapy explained by a pharmacist. There are also many opportunities for patients and pharmacists to get involved in community pharmacies. Some pharmacies have pharmacists with cancer-related training at the hospital. They can also serve as a resource in various places when patients want to learn about CGP. However, the timing and patient population that would benefit the most from their assistance remains unclear. Considering the time required for CGP and the patient's general condition, it is desirable to inform the patient about CGP at an early stage, then the patient fully understands the contents of CGP and decides promptly to undergo it. It is anticipated that being better informed about CGP before deciding to take the test can improve patient health literacy and empowering them to participate in decisions about their own treatment. Therefore, we decided to examine the impact on patient decision making if pharmacists provide information about CGP earlier than the end of standard treatment.

A known measure of patient decisional conflict is the Decisional Conflict Scale (DCS) developed by O'Connor et al [15]–[29]. The DCS consists of 16 items and is calculated on a score of 0–100, with smaller numbers indicating less conflict. In this study, DCS scores were measured before and after information was provided by the pharmacist in order to quantitatively evaluate changes in patient awareness. A secondary search was also conducted for factors that influence changes in attitudes.

Study design

Patients who received chemotherapy at Juntendo University Hospital, Tokyo, Japan, from March 2022 to July 2023 were included in this open-label, randomized, controlled study.

Eligibility Criteria

The eligibility criteria are as follows: at least 20 years old, receiving standard insurance care, and may be eligible for CGP if standard care has been completed or is expected to be completed. Patients having difficulty making decisions on their own due to psychiatric symptoms or cognitive decline were excluded.

Sample size

The sample size was calculated to detect significant differences in the percentage decrease in the change in DCS score (the primary endpoint) before and after the pharmacist interviews. We estimated that 88 patients in each group were needed for an effect size of 0.2, a power of 80%, and a significance level of 0.05 (test of proportions).

Randomization

Patients were randomly assigned 1:1 to the intervention and non-intervention groups, with treatment objective (preoperative and postoperative/advanced recurrence) as a stratification factor. The staffs responsible for randomization will obtain the group allocation code from the Medical Technology Innovation Center Clinical Research and Trial Center Juntendo University.

Interventions

In the non-intervention group, the pharmacist provided explanations about the drugs as done in usual care. In addition, the intervention group was provided with general information on cancer genome medicine. Intervention group were guided to the Center for Cancer genomics and Advanced Therapeutics website, established for patients.

Data collection

Five pharmacists with 14.8 ± 5.8 years of experience were involved in data collection. All of them were certified in cancer chemotherapy and received training on cancer genome medicine. The time required for pharmacist interviews was also measured.

The following patient demographic information was recorded: age, gender, type of cancer, family history, clinical stage, treatment line, history of nonsurgical treatment [chemotherapy, radiofrequency ablation (RFA), transcatheter arterial chemoembolization (TACE), and microwave ablation (MWA)], marital status, presence of children, employment status, last education, income, and sources of medical-related information. Questionnaires were administered 2–6 weeks before and after the pharmacist interview to measure the DCS score, which was calculated according to the User Manual. The reason for setting the 2–6 week is that many outpatients visit for chemotherapy every 2–3 weeks. The Genomics Literacy Score (GLS) was also measured to ascertain the health literacy of patients in relation to genomics, and this was calculated according to previous reports [30].

After the pharmacist interview, patients were asked whether they wished to undergo CGP and the source of information they used in choosing their response. They were also asked how much they would be willing to pay for the provision of information on cancer genome medicine.

Supplemental data 1 and 2 were the questionnaire used before pharmacist interview, and supplemental data 2 and 3 were used after pharmacist interview. Supplemental data 2 is DCS [15]–[29].

Study outcomes

The primary endpoint of this study was the impact of the pharmacist’s intervention on the patient’s decision making, as measured by the DCS score. As a secondary endpoint, relationship between patient factors influencing decision making and DCS scores were also analyzed.

Statistical analysis

The percentage of patients with decreased DCS score before and after the pharmacist interview was determined. A test of proportions was used to compare the percentage decrease in DCS score between the intervention and non-intervention groups. Changes in DCS score were determined before and after the pharmacist interview. Changes in DCS scores between the intervention and non-intervention groups were compared using unpaired t-test.

The DCS scores of all patients prior to the pharmacist interview were divided into two groups based on the median value, and the trends of each patient’s background were examined. Additionally, the intervention group was divided into two groups (with decreased and increased scores), and the trends in each patient’s background were examined. Age between the two groups was analyzed using t-test. Relationship between patient factors influencing decision making and DCS scores were analyzed using Chi-square test or Fisher's exact test. Statistical Analysis Software (SAS) (version 9.4, SAS Institute Inc., Cary, North Carolina) was used for statistical analysis.

Registration

Written consent was obtained from 192 patients who received chemotherapy at Juntendo University Hospital from March 2022 to July 2023. However, 4 patients withdrew their consent, and 8 patients were unable to complete the entire questionnaire. The analysis included responses from 180 patients (Fig. 1).

Patient background was examined to identify factors that might influence decision making while choosing a genome-related treatment (Table 1), but no significant differences were found between the groups.

Sources of information of the patients were identified. Most common response of patients was the internet, followed by friends and acquaintances (Table 1).

The pharmacist interview lasted 19.9 ± 6.9 and 10.4 ± 4.3 minutes for the intervention and non-intervention group, respectively.

The DCS score decreased after the pharmacist interview in 49.4% and 41.9% of the intervention and non-intervention groups, respectively, a nonsignificant difference (p = 0.313, test of proportions) (Table 2).

However, the DCS score increased after the pharmacist interview by 1.98 ± 16.5 and 3.61 ± 17.3 in the intervention and non-intervention groups, respectively (Table 2). Both groups showed an increase in decisional conflict about receiving treatment, including undergoing CGP, but the change was significantly smaller in the intervention group compared to the non-intervention group (p = 0.026, unpaired t-test). GLS scores before and after the pharmacist interview were 10.1 ± 3.55 vs. 10.8 ± 3.57 in the intervention group and 10.2 ± 3.34 in the non-intervention group, with no significant difference.

The background factors which influenced the decision to receive treatment, including CGP were analyzed. All patients were divided into two groups based on the median DCS score before the pharmacist interview, and the differences in patient background between each group were analyzed (Supplemental data 4). None of the items demonstrated a significant difference between the two groups.

The patient factors that influenced decision making after the pharmacist’s intervention were assessed. The intervention group was divided into two groups (those whose DCS scores decreased and increased after the interview), and the differences in patient backgrounds were analyzed (Table 3). A trend toward younger age was observed in the group with decreased DCS scores compared to the group with increased DCS scores. In the group with decreased DCS scores, there were 21 and 18 patients in primary and secondary treatment, respectively. In comparison, the group with increased DCS scores had 33 patients in the primary treatment group; there were also more patients with no history of nonsurgical treatment (chemotherapy, RFA, TACE, and MWA) (31 patients).

On the other hand, no characteristic patient factors were observed in the non-intervention group (Supplemental data 5).

The respondents were asked about their desire to take a CGP after the pharmacist interview. There were no significant differences between the two groups (Supplemental data 6).

The cost was assessed that how much people would be willing to pay for information. In both groups, most respondents answered 600–1,000 yen (about 4–6 USD), followed by those who reported more than 1,000 yen (about 6 USD or more). There were no significant differences between the two groups (Supplemental data 7).

The number of patients in the intervention group with decreased DCS scores was higher compared to the non-intervention group after the pharmacist interview, but the difference was nonsignificant. However, DCS scores increased in both groups, but the degree of increase in DCS scores was significantly lower in the intervention group. Increased DCS scores indicate increased conflict in decision making regarding treatment, including CGP, regardless of patient wishing to receive treatments or not. Only the intervention group was briefed about CGP, but the non-intervention group heard the word “CGP” at the time of obtaining consent. Thus, it is possible that hearing a new word may have caused hesitation in imagining new possible treatment options. Information from the internet and media may also cause hesitation. This survey revealed that a high percentage of respondents receive medical information from the internet and media, along with family and friends. With recent advancements in online information, such as in social media, more people can now easily access variety of information. However, information from the internet or friends and acquaintances is often ambiguous than information from TV or newspapers, and thus the patient’s own information literacy is important.

The intervention group showed a significantly lower increase in conflict; more patients referred to the pharmacist’s explanation while choosing whether they wanted to take a CGP. Patients in the intervention group received information on eligibility of patients for CGP, treatment outcomes and other perspectives. This suggests on importance of gathering information from various sources such as the media and internet, along with a two-way exchange of information between patients and pharmacists, who are highly reliable sources of information.

The time required for the pharmacist interview was approximately 10 minutes longer in the intervention group, indicating that information about the CGP could be provided in this time. This is a reasonable amount of time in actual medical practice.

The GLS did not change after the pharmacist interview. This shows that patient health literacy did not improve. Although the information provided did not directly improve genomic knowledge, it was believed that the content could support patient decision making.

To identify patients who felt conflicted about their decision to undergo treatment, including CGP consultation, the patients’ demographic information was analyzed after dividing the patients into two groups based on median DCS score before the pharmacist interview. The results were not different between the two groups, and did not show any characteristics of patients who were more or less likely to experience conflicts in decision making. Health care providers tend to consider of factors that influence decision making, such as having a hereditary cancer or a family history, these findings suggest that it is not necessary to focus on a specific patient population when providing information about CGP.

To identify patients who were more likely to change their conflicts regarding decision making to receive treatment, including CGP consultation, the patients’ background information was analyzed after dividing the intervention group into those whose DCS scores decreased and increased after the pharmacist interview. In the group with decreased DCS scores, age was younger compared to the other group. It shows that younger age decreased their conflicts in decision making, therefore providing information from pharmacist was considered effective. Patients undergoing first-line chemotherapy tended to have increased DCS scores. These patients are often not eligible for CGP in Japan today. However, the information provided about CGP may have given the impression that there were more treatment options available, causing unnecessary confusion. Alternatively, there were patients whose DCS scores decreased despite being on first- and second-line of chemotherapy. The information provided by the pharmacist could have convinced patients that the treatment they were receiving at the time they responded to the survey was appropriate for their stage of disease, resulting in a decrease in their DCS score. There was also a tendency for DCS scores to increase in patients with no history of nonsurgical treatment. Thus, although some patients may benefit from being presented with future-oriented treatment options since the beginning of treatment, in actual medical practice, uniform information provision may cause some patients to feel more confused and therefore, should be discouraged.

No significant difference was found between the two groups in terms of patients wishing to undergo CGP. About 58.6% and 54.8% of patients answered no, whereas 26.4% and 30.1% of patients in the intervention and non-intervention groups, respectively, did not respond. These patients are expected to include those who answered no or considered were indecisive as a result of being convinced that it was not the right time to take the CGP yet.

About the cost of providing information, most respondents in both groups said 600–1,000 yen (about 4–6 USD), followed by those who said more than 1,000 yen (about 6 USD or more). In Japan, due to the universal health insurance system, pharmacists currently charge 600–1,000 yen (about 4–6 USD) for chemotherapy patients actually. Many respondents revealed their willingness to pay more than 1,000 yen (about 6 USD or more), indicating many patients are willing to pay for appropriate information.

The primary role of pharmacist in cancer care is to support drug therapy and management of side effects [31]. In cancer genome medicine, the results of CGP can be used to search for effective drugs and clinical trial information. These results can also support drug therapy while drug administration [7]–[9]. However, since many treatments performed based on CGP results in Japan are clinical trials, only pharmacists at a particular medical institution can be directly involved in many situations [32] [33]. Nevertheless, the results of this study suggest that pharmacists can assist in the decision making process through appropriate patient education, even in facilities not conducting clinical trials. Pharmacists can contribute to cancer care in future by correctly understanding CGP, which may lead to drug therapy, and by providing timely information to patients.

One limitation of this study is that it is a single-center study. In addition, differences in accessibility to genomic medical facilities, such as in urban centers versus rural areas, may influence patient backgrounds and lead to different results.

Increased conflicts are reduced when pharmacists provide information about CGP to patients undergoing cancer chemotherapy. This can assist patients in making decisions about their treatment choices.

CGP

comprehensive genome profiling

DCS

Decisional Conflict Scale

RFA

radiofrequency ablation

TACE

transcatheter arterial chemoembolization

MWA

microwave ablation

GLS

Genomics Literacy Score

SAS

Statistical Analysis Software

Ethics approval and consent to participate

This study was conducted according to the ethical guidelines for life science and medical research involving human subjects and the Declaration of Helsinki. Written informed consent was taken from all patients. The study was approved by Research Ethics Committee, Faculty of Medicine, Juntendo University (E21-0016).

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the scholarship program of Daiichi Sankyo Co., Ltd.

Author Contribution

OS designed this study, assisted in analysis, and drafted the manuscript. SKa was involved in the design of the study and assisted in drafting the manuscript. SN performed all analyses, was involved in the design of the study design, and assisted in editing the manuscript. KS, SKo, US, and SI participated in data collection. TK, and SKa provided oversaw the drafting of the manuscript. All authors read and approved the final manuscript.

Acknowledgement

I would like to thank the member of the Medical Technology Innovation Center, Juntendo University for advice on data collection.

Data Availability

The raw dataset and supplemental data 1-3 are public available in the dropboxhttps://www.dropbox.com/scl/fo/jlpifrjjg3gxx1m9riito/AAfZBhA66rQr3b795NicGRw?rlkey=a6t5vl3ub0ysh4d1v1ubgi7q5&st=9w5wourn&dl=0

Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M et al. Global Cancer Observatory: Cancer Today. Lyon: International Agency for Research on Cancer; 2020 (https://gco.iarc.fr/today, accessed February 2021).
Cancer Statistics. Cancer Information Service, National Cancer Center, Japan (Vital Statistics of Japan, Ministry of Health, Labour and Welfare).
Tanaka H, Tanaka S, Togawa K, Katanoda K. Practical Implications of the Update to the 2015 Japan Standard Population: Mortality Archive From 1950 to 2020 in Japan. J Epidemiol. 2023;33(7):372–80.
Judy E. Garber and Kenneth Offit. Hereditary Cancer Predisposition Syndromes. J Clin Oncol. 2005;23(2):276–92. 10.1200/JCO.2005.10.042.
Eiermann W, on behalf of the International Herceptin Study Group. : Trastuzumab combined with chemotherapy for the treatment of HER2-positive metastatic breast cancer: Pivotal trial data. Annals of Oncology 12 (Suppl. I): S57-S62, 2001.
Stephen GO, Guilhot RA, Larson, et al. Imatinib compared with interferon and low-dose cytarabine for newly diagnosed chronic-phase chronic myeloid leukemia. N Engl J Med. 2003;348(11):994–1004.
Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113–20.
U.S. FOOD & DRUG ADMINISTRAION. Premaret Approval (PMA) (https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P170019).
Haidar C-E, Petry N, Oxencis C, Douglas JS. Hoffman. ASHP Statement on the Pharmacist’s Role in Clinical Pharmacogenomics. Am J Health-Syst Pharm. 2022;79:704–7.
Feldman EA. The Genetic Information Nondiscrimination Act (GINA): public policy and medical practice in the age of personalized medicine. J Gen Intern Med. 2012;27(6):743–6. 10.1007/s11606-012-1988-6.
Raheem F, Kim P, Grove M, Kiel PJ. Precision Genomic Practice in Oncology: Pharmacist Role and Experience in an Ambulatory Care Clinic. Pharm (Basal). 2020;8(1):32. 10.3390/pharmacy8010032.
Ministry of Health. Labour and Welfare. (https://www.mhlw.go.jp/content/001275450.pdf).
Sunami K, Ichikawa H, Kubo T, et al. Feasibility and utility of a panel testing for 114 cancer-associated genes in a clinical setting: A hospital-based study. Cancer Sci. 2019;110:1480–90.
Cabinet Office, Government of Japan. (https://survey.gov-online.go.jp/r05/r05-gantaisaku/gairyaku.pdf).
O'Connor AM. Validation of a decisional conflict scale. Med Decis Mak. 1995 Jan-Mar;15(1):25–30.
Légaré F, Kearing S, Clay K, Gagnon S, D'Amours D, Rousseau M, O'Connor AM. Are you SURE? Assessing patient decisional conflict with a 4-item screening test. Can Fam Physician. 2010;56(8):e308–14.
Linder SK, Swank PR, Vernon SW, Mullen PD, Morgan RO, Volk RJ. Validity of a low literacy version of the Decisional Conflict Scale. Patient Educ Couns. 2011;85(3):521–4. Epub 2011 Feb 5.
Légaré F, Leblanc A, Robitaille H, Turcotte S. The decisional conflict scale: moving from the individual to the dyad level. Z Evid Fortbild Qual Gesundhwes. 2012;106(4):247–52. Epub 2012 Mar 28.
Ferron Parayre A, Labrecque M, Rousseau M, Turcotte S, Légaré F. Validation of SURE, a Four-Item Clinical Checklist for Detecting Decisional Conflict in Patients. Med Decis Mak. 2014;34(1):54–62. Epub 2013 Jun 17.
Turcotte S, Guerrier M, Labrecque M, Robitaille H, Rivest LP, Hess B, Légaré F. Dyadic validity of the Decisional Conflict Scale: common patient/physician measures of patient uncertainty were identified. J Clin Epidemiol. 2015;68(8):920–7. 10.1016/j.jclinepi.2015.03.005. Epub 2015 Mar 21.
Pecanac KE, Brown RL, Steingrub J, Anderson W, Matthay MA, White DB. A psychometric study of the decisional conflict scale in surrogate decision makers. Patient Educ Couns. 2018;101(11):1957–65. Epub 2018 Jul 7.
Garvelink MM, Boland L, Klein K, Nguyen DV, Menear M, Bekker HL, Eden KB, LeBlanc A, O’Connor A, Stacey D, Légaré F. Decisional Conflict Scale Use over 20 Years: The Anniversary Review. Med Decis Making. 2019;39(4):301–14. 10.1177/0272989X19851345.
Garvelink MM, Boland L, Klein K, Nguyen DV, Menear M, Bekker HL, Eden KB, LeBlanc A, O’Connor A, Stacey D, Légaré F. Decisional Conflict Scale Findings among Patients and Surrogates Making Health Decisions: Part II of an Anniversary Review. Med Decis Making. 2019;39(4):316–27. 10.1177/0272989X19851346.
Brodney S, Fowler FJ Jr, Barry MJ, Chang Y, Sepucha K. Comparison of Three Measures of Shared Decision Making: SDM Process_4, CollaboRATE, and SURE Scales. Med Decis Mak. 2019 Jun;21. 10.1177/0272989X19855951. [Epub ahead of print].
Pozzar RA, Berry DL, Hong F. Item response theory analysis and properties of decisional conflict scales: findings from two multi-site trials of men with localized prostate cancer. BMC Med Inf Decis Mak. 2019;19(1):124. 10.1186/s12911-019-0853-5.
Boland L, Légaré F, McIsaac DI, Graham ID, Monica T, Decary S, Stacey D. SURE Test Accuracy for Decisional Conflict Screening among Parents Making Decisions for Their Child. Med Decis Mak. 2019 Nov;15. 10.1177/0272989X19884541.
Antunes CMTB, Bampi LNDS, Filho FMDA, Hua FY. Adaptation and cross-cultural validation of the Decisional Conflict Scale for use in Brazil. Revista Eletrônica Acervo Saúde (REAS). 2021;13(12):1–9. 10.25248/REAS.e9136.2021.
Guerrero-Peral ÁL, Porta-Etessam J, Rodríguez-Vico J, Núñez M, Ciudad A, Díaz-Cerezo S, Garí-Peris C, Pérez-Sádaba FJ, Lizán L, Santos-Lasaosa S. Adaptation and validation of the Spanish version of the Decisional Conflict Scale in people with migraine in Spain. Patient Prefer Adherence. 2022;16:3291–3302. 10.2147/PPA.S384333. eCollection 2022.
Kawaguchi, et al. Development and validation of the Japanese version of the Decisional Conflict Scale to investigate the value of pharmacists’ information: a before and after study. BMC Med Inf Decis Mak. 2013;13:50.
Ishiyama I, Nagai A, Muro K, Tamakoshi A, Kondo M, Mimura K, Tanzawa T, Yamagata Z. Relationship Between Public Attitudes Toward Genomic Studies Related to Medicine and Their Level of Genomic Literacy in Japan. Am J Med Genet Part A. 2008;146A:1696–706.
Lisa MH, Eve MS, Kate DJ. The Expanding Role of the Oncology Pharmacist. Pharm (Basal). 2020;8(3):130. 10.3390/pharmacy8030130.
Terada T. Roles of Pharmacists in Cancer Genomic Medicine. Yakugaku Zasshi. 2020;140:663–6.
Aimono E, Iguchi A, Mochida K, Imai M, Hayashi H, Nishihara H. Learning from My Experience: Outpatient Care for Cancer Multigene Genomic Testing. Yakugaku Zasshi. 2020;140:667–8.

Tables 1-4 are available in the Supplementary Files section.

No competing interests reported.

Download PDF

Reviewers agreed at journal
05 Oct, 2024
Reviewers invited by journal
23 Sep, 2024
Editor invited by journal
22 Aug, 2024
Editor assigned by journal
22 Aug, 2024
Submission checks completed at journal
22 Aug, 2024
First submitted to journal
14 Aug, 2024

You are reading this latest preprint version

Role of pharmacists in providing information on cancer genome medicine and patient decision making：open-label randomized controlled study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study design

Eligibility Criteria

Sample size

Randomization

Interventions

Data collection

Study outcomes

Statistical analysis

Registration

Results

Discussion

Conclusions

Abbreviations

Declarations

Competing interests

Funding

Author Contribution

Acknowledgement

Data Availability

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1