Integrating touchscreen-based geriatric assessment and frailty screening for adults with acute myelogenous leukemia to drive personalized treatment decisions.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e24030-e24030
Author(s):  
Debra Wujcik ◽  
Nikolaos Papadantonakis ◽  
Sarah Allison Wall ◽  
Margaret T. Kasner ◽  
OMER HASSAN JAMY ◽  
...  
Keyword(s):  
Decision Making ◽  
Frailty Index ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Self Report ◽  
Myelogenous Leukemia ◽  
Valuable Insight ◽  
Nccn Guidelines ◽  
Treatment Decision Making ◽  
Patient Reported

e24030 Background: AML is a disease of older adults, with median age of 68 years at presentation. NCCN guidelines suggest comprehensive geriatric assessments (GA) be included in clinical practice to guide treatment decisions. Utility of GA in older AML patients in a real-world environment is not yet established. We tested the feasibility of using a modified GA (mGA), administered by patient self-report on a touchscreen computer, real-time use and utility by clinicians and the correlation of mGA results on treatment decision-making. Methods: Sixty-two patients were recruited from three sites to complete a tablet-based mGA screening at a treatment decision-making time point. The mGA consists of the Frailty Index (FI) that includes four domains: age, activities of daily living, instrumental ADLs, and comorbidities. Falls within the past 6 months and patient reported health interference with function are also assessed. Results are displayed for the clinician to inform the treatment discussion. Results: Participants were mean age 73 years (range 61-88), 63% male, and 90% white. Frailty Index result was 32% fit, 40% intermediate, and 28% frail. Providers were asked the fit/frailty status prior to seeing the results of the mGA. Of 53 provider responses, there was 57% (n=30) provider concordance with the mGA result; 9% (n=5) said fit when mGA said intermediate and 17% (n=9) said intermediate when mGA said frail. When asked their goals of care, nearly all (n=60, 97%) patients agreed with the statement “my cancer is curable”, yet 30% (n=19) disagreed the treatment goal was to get rid of all the cancer. Nearly half (n=30) indicated they want to make treatment decisions together with the provider rather than provider or patient making decision alone. 73% (45/62) of patients were satisfied with the ease of using the survey and took an average 16.3 minutes to complete. Patient self-reported presence/severity of eight symptoms at baseline (see Table). Conclusions: A simple electronic tool may provide valuable insight into patient understanding of disease to better tailor patient-provider discussion and treatment decision-making. Providers overestimated fitness 26% of the time. Final results will be presented to include the outcome at 3 months by Frailty Index. [Table: see text]

scholarly journals Integrating Touchscreen-Based Geriatric Assessment and Frailty Screening for Adults with Acute Myelogenous Leukemia to Drive Personalized Treatment Decisions

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4050-4050
Author(s):  
Omer Hassan Jamy ◽  
Stacey Ingram ◽  
D'Ambra Dent ◽  
William Dudley ◽  
Matthew Dudley ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Real Time ◽  
Frailty Index ◽  
Treatment Decision ◽  
Symptom Assessment ◽  
Treatment Decisions ◽  
Frailty Status ◽  
Treatment Decision Making ◽  
Survey Results

Abstract Background: Acute Myeloid Leukemia (AML) is a disease of older adults, with a median age of 68 years at diagnosis. The NCCN guidelines recommend comprehensive geriatric assessments (GA) be included in clinical practice to guide treatment decisions. Utility of GA in older AML patients in a real-world environment is not yet established. We tested the feasibility of using a modified GA (mGA), administered by patient self-report on a touchscreen computer, real-time use and utility by clinicians and the correlation of mGA results on treatment decision-making. Methods: 77 newly diagnosed patients were recruited from three sites to complete a tablet-based mGA screening at a treatment decision visit. The mGA includes four domains: age, activities of daily living (ADLs), instrumental ADLs, and comorbidities. Survey results along with history of falls was used to create the Frailty Index (FI). Providers were asked what they thought the fit/frailty status of the patient was before viewing the results on a dashboard. After viewing the survey results, the clinician discussed the treatment plan with the patient. Patients received intensive or non-intensive therapy. Additional information was captured on clinical trial enrollment. Baseline and 3 month surveys recorded presence and severity of 8 symptoms using the Edmonton Symptom Assessment Scale (ESAS) with 0=no symptom and 10=worst possible symptom and quality of life using the Functional Assessment of Cancer Treatment: Leukemia (FACT-LEU). Results: Participants had a median age of 71 years ( range:61-88y); 50% were female, and 87% white. Frailty Index results for 76 patients were 28 (36.4%) fit, 25 (32.5%) intermediate, and 23 (29.9%) frail. (One patient did not complete the mGA). 52 of 77 (69%) enrolled patients were alive at 3 months; 21(27%) died and 4 (5%) were lost to follow-up. Providers were asked the fit/frailty status prior to seeing the results of the mGA. Of 75 provider responses, results were 27 (36.0%) fit, 29 (38.7%) intermediate, and 19 (25.3%) frail. There was 63% (n=47) provider concordance with the mGA result. There was more agreement with fit (n=22, 81.5%) and frail status (n=11, 57.9%) and less with intermediate (n=14, 48.3%). Of the 25 of 75 (33.%) provider reports that indicated that the mGA result influenced the treatment decision, 6 patients (5 fit, 1 intermediate) received intensive treatment, 15 received non intensive treatment (1 fit, 6 intermediate, 8 frail) and 4 enrolled in a clinical trial (1 fit, 2 intermediate, and 1 frail). Significant symptom improvement at 3 months was seen for drowsiness, lack of appetite, shortness of breath, and anxiety. FACT Leu results did not change over 3 months. Providers reported an average of 4.45 minutes to review the dashboard. Patients were able to complete the surveys unassisted in an average time of 16.24 minutes. Discussion: There was nearly 40% discordance between the provider and mGA, with the most discordance on the intermediate fit status. However, results of the mGA influenced treatment decision making in one third of provider/patient interactions. Further analysis of the mGA domains is warranted to see if additional insights can be gained. With time, some symptoms improve and others don't. This points to the opportunity to direct resources towards symptom assessment during treatment. Feasibility was demonstrated in this study as providers received the aggregated results in real time and reviewed them in less than 5 minutes. In addition, patients were able to complete the survey unassisted without disturbing clinic operations. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Decision Making for Treatment After ACL Injury From an Orthopaedic Surgeon and Patient Perspective: Results From the NACOX Study

2021 ◽  
Vol 9 (4) ◽  
pp. 232596712110050
Author(s):  
Hanna Tigerstrand Grevnerts ◽  
Sofi Sonesson ◽  
Håkan Gauffin ◽  
Clare L. Ardern ◽  
Anders Stålman ◽  
...  
Keyword(s):  
Decision Making ◽  
High Activity ◽  
Treatment Decision ◽  
Acl Injury ◽  
Treatment Decisions ◽  
Activity Level ◽  
Knee Instability ◽  
Acl Injuries ◽  
Patient Reported ◽  
And Function

Background: In the treatment of anterior cruciate ligament (ACL) injuries, there is little evidence of when and why a decision for ACL reconstruction (ACLR) or nonoperative treatment (non-ACLR) is made. Purpose: To (1) describe the key characteristics of ACL injury treatment decisions and (2) compare patient-reported knee instability, function, and preinjury activity level between patients with non-ACLR and ACLR treatment decisions. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 216 patients with acute ACL injury were evaluated during the first year after injury. The treatment decision was non-ACLR in 73 patients and ACLR in 143. Reasons guiding treatment decision were obtained from medical charts and questionnaires to patients and orthopaedic surgeons. Patient-reported instability and function were obtained via questionnaires and compared between patients with non-ACLR and ACLR treatment decisions. The ACLR treatment group was classified retrospectively by decision phase: acute phase (decision made between injury day and 31 days after injury), subacute phase (decision made between 32 days and up to 5 months after injury), and late phase (decision made 5-12 months after injury). Data were evaluated using descriptive statistics, and group comparisons were made using parametric or nonparametric tests as appropriate. Results: The main reasons for a non-ACLR treatment decision were no knee instability and no problems with knee function. The main reasons for an ACLR treatment decision were high activity demands and knee instability. Patients in the non-ACLR group were significantly older ( P = .031) and had a lower preinjury activity level than did those in the acute-phase ( P < .01) and subacute-phase ( P = .006) ACLR decision groups. There were no differences in patient-reported instability and function between treatment decision groups at baseline, 4 weeks after injury, or 3 months after injury. Conclusion: Activity demands, not patient-reported knee instability, may be the most important factor in the decision-making process for treatment after ACL injury. We suggest a decision-making algorithm for patients with ACL injuries and no high activity demands; waiting for >3 months can help distinguish those who need surgical intervention from those who can undergo nonoperative management. Registration: NCT02931084 ( ClinicalTrials.gov identifier).

The Significance of Patient-Reported Outcomes for Metastatic Breast Cancer Patients

2022 ◽  
Author(s):  
Larissa Elisabeth Hillebrand ◽  
Ulrike Söling ◽  
Norbert Marschner
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Metastatic Breast Cancer ◽  
Cancer Treatment ◽  
Patient Reported Outcomes ◽  
Metastatic Breast ◽  
Treatment Decision ◽  
Breast Cancer Patients ◽  
Treatment Decision Making ◽  
Patient Reported

Background: Breast cancer is still the most common malignancy in women worldwide. Once metastasized, breast cancer treatment primarily aims at reducing symptom burden, thereby trying to maintain and improve a patient´s quality of life (QoL), delaying disease progression, and prolonging survival. Curing the disease is not possible in the palliative setting. To better understand metastatic breast cancer patients, their symptoms and wishes, which are important for treatment-decision making and outcome, patient-reported outcomes (PROs) are of great importance, giving an impression of what really matters to and concerns a patient. Summary: Many advances have been made to implicate PROs in clinical trials, non-interventional studies, registries, and clinical routine care of metastatic breast cancer. For example, large phase III trials like PALOMA-3 (NCT01942135), MONALEESA-7 (NCT02278120), HER2CLIMB (NCT02614794), and KEYNOTE-119 (NCT02555657) trials implemented PROs in their trial design to assess the QoL of their trial patients. Also, non-interventional studies on metastatic breast cancer, like e.g., the NABUCCO study (IOM-02240), and prospective non-interventional, multicenter registries e.g., the tumor registry breast cancer (NCT01351584) or the breast cancer registry platform OPAL (NCT03417115), have implemented PROs to assess QoL during the anti-cancer treatment periods of the patients. Key Message: Using PROs in metastatic breast cancer can support shared treatment-decision making and management of symptoms, eventually leading to an improvement in QoL. Progressively, regulatory authorities take PROs into consideration for the approval of new drugs. Hence, the implication of PROs in cancer treatment, and especially in MBC, is of significant value.

Physicians' views on the influence of patient participation on treatment decisions - an explorative study

2007 ◽  
Vol 20 (3) ◽  
pp. 174-182 ◽  
Author(s):  
Elina Jaakkola
Keyword(s):  
Decision Making ◽  
Patient Participation ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Role Expectations ◽  
Customer Participation ◽  
Health Care Managers ◽  
Treatment Decision Making ◽  
Explorative Study ◽  
Significant Difference

While patient participation in treatment decisions is increasingly advocated in medical literature, patient demand has been considered to cause unnecessary prescribing. Using the concept of customer participation as discussed in services marketing and management literature as a theoretical base, the paper analyses the influence of patient participation on the medical service process and treatment decision-making. A qualitative, explorative study was conducted to investigate American and British physicians' views on patient participation in the treatment of osteoporosis and schizophrenia. It became evident that in the cases of both osteoporosis and schizophrenia, patients influence prescribing decisions despite the significant difference in their willingness and ability to participate. The manifestations of patient participation were divided into three groups: (1) resources, such as the patient's condition and information about it, and his/her preconceived notions and preferences, (2) actions, such as preparing for the service, negotiating decisions and implementing the treatment, and (3) the patient's role expectations and inclination to participate. The influence of such manifestations on prescribing decision-making is discussed in detail, and differences between the studied illnesses are explained. Implications to health-care managers and practitioners are discussed.

HSR19-089: Misperceptions Regarding Palliative and Hospice Care Among Cancer Patients: What Can We Learn from Patient-Reported Treatment Decision Making

2019 ◽  
Vol 17 (3.5) ◽  
pp. HSR19-089
Author(s):  
Sara Hayes ◽  
Brian M. Green ◽  
Shayna Yeates ◽  
Amrita Bhowmick ◽  
Kaitlyn McNamara ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Making ◽  
Palliative Care ◽  
Cancer Care ◽  
Hospice Care ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Treatment Decision Making ◽  
Diagnosis Of Cancer

Background: Despite NCCN Guidelines and clear definition of palliative care, patients often carry misperceptions about palliative care and how it can be beneficially integrated into a patient’s care plan. In order to better understand the misinformation about palliative and hospice care, this study aims to assess patient-healthcare provider (HCP) communication regarding treatment decisions. Methods: An online survey was conducted with individuals who have had a diagnosis of cancer (n=1,517) to better understand their healthcare experiences as well as the impact their cancer diagnosis had on their quality of life. Measures included agreement scale questions assessing patient information needs surrounding treatment decision making. Open-ended questions where respondents were prompted to provide a written response allowed researchers to further assess patients’ understanding of palliative and hospice care. Responses to agreement-scale questions were evaluated using descriptive statistics. Openended question responses were analyzed using Dedoose qualitative data analysis software. Results: Among patients with a diagnosis of cancer, there were a broad range of patient misperceptions regarding palliative care, hospice care, and how they are used in cancer care. The majority of respondents (81%) stated that their HCP played a role when deciding on their treatment plan. Despite this, only 46% were confident they knew about the treatment’s impact on their daily life, 56% were confident they knew about the potential side effects of treatment, and 57% felt they had all of the information they needed. Themes identified through qualitative analysis include: patient conflation of palliative and hospice care, belief that palliative and hospice care are only relevant to end-of-life decision-making, and uncertainty about whether quality of life can actually be improved. Conclusions: Institutions and HCPs are recommended to integrate palliative care into cancer care. However, as this research shows, oncology patients are often misinformed about the benefits of palliative care. This follows a parallel concern of patients making treatment decisions without optimal information. A potential factor behind this unmet need may be lack of effective communication between patient and HCP. Palliative care may be mentioned by the HCP, but not discussed with enough empathy or depth, leading to patient misunderstanding and lack of inclusion in treatment plans.

Size and influence of the decision support networks of women newly diagnosed with breast cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18012-e18012
Author(s):  
Lauren P. Wallner ◽  
Yun Li ◽  
Chandler McLeod ◽  
Ann S Hamilton ◽  
Kevin C. Ward ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Decision Support ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Breast Cancer Treatment ◽  
Network Size ◽  
Support Networks ◽  
Newly Diagnosed ◽  
Treatment Decision Making

e18012 Background: Little is known about the size and characteristics of informal decision support networks of women diagnosed with breast cancer and whether involvement of informal decision supporters (DSP) influences breast cancer treatment decisions. Methods: A population-based sample of newly diagnosed breast cancer patients reported to the Georgia and Los Angeles SEER registries in 2014-15 were surveyed approximately 6 months after diagnosis (N = 2,502, 68% response rate). Network size was estimated by asking women to list up to 3 of the most important DSPs who helped them with locoregional therapy decisions. For each individual DSP listed, respondents reported how important each DSP’s opinion was in treatment decision making, and how satisfied they were with their involvement (5 pt. scales, “not at all” to “very”). Decision deliberation was measured using 5-items assessing degree patients thought through the decision, with higher scores reflecting more deliberative treatment decisions. We compared network size (0-3 or more) across patient-level characteristics and estimated the association between network size and deliberation using multivariable linear regression. Results: Of the 2,502 women in this analysis, 51% reported having 3 or more DSPs, 20% reported 2, 18% reported 1, and 11% reported not having any DSPs. Married/partnered women, those younger than 45 years old, and black women were all more likely to report larger networks (all p < 0.001). Partnered women most often reported their partner as their main DSP (37.9%), whereas not partnered/unmarried women most often reported children (38.4%). The majority of women were highly satisfied with their DSP being involved in their decisions (76.5%) and 68.6% felt their DSP was very important in their decision making. Larger support networks were associated with more deliberative surgical treatment decision-making (p < 0.001). Conclusions: Most women engaged multiple DSPs in their treatment decision making, including spouses, children, and friends. Involving more DSPs was associated with more deliberative treatment decisions. Future initiatives to improve breast cancer treatment decision making should acknowledge and engage informal DSPs.

“It’s important to me”: A qualitative analysis of shared decision-making and patient preferences in early-stage breast cancer patients.

2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 227-227
Author(s):  
Valerie Lawhon ◽  
Rebecca England ◽  
Audrey S. Wallace ◽  
Courtney Williams ◽  
Stacey A. Ingram ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Shared Decision Making ◽  
Patient Preferences ◽  
Early Stage ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Early Stage Breast Cancer ◽  
Shared Decision ◽  
Treatment Decision Making

227 Background: Shared decision-making (SDM) occurs when both patient and provider are involved in the treatment decision-making process. SDM allows patients to understand the pros and cons of different treatments while also helping them select the one that aligns with their care goals when multiple options are available. This qualitative study sought to understand different factors that influence early-stage breast cancer (EBC) patients’ approach in selecting treatment. Methods: This cross-sectional study included women with stage I-III EBC receiving treatment at the University of Alabama at Birmingham from 2017-2018. To understand SDM preferences, patients completed the Control Preferences Scale and a short demographic questionnaire. To understand patient’s values when choosing treatment, semi-structured interviews were conducted to capture patient preferences for making treatment decisions, including surgery, radiation, or systemic treatments. Interviews were audio-recorded, transcribed, and analyzed using NVivo. Two coders analyzed transcripts using a constant comparative method to identify major themes related to decision-making preferences. Results: Amongst the 33 women, the majority of patients (52%) desired shared responsibility in treatment decisions. 52% of patients were age 75+ and 48% of patients were age 65-74, with an average age of 74 (4.2 SD). 21% of patients were African American and 79% were Caucasian. Interviews revealed 19 recurrent treatment decision-making themes, including effectiveness, disease prognosis, physician and others’ opinions, side effects, logistics, personal responsibilites, ability to accomplish daily activities or larger goals, and spirituality. EBC patient preferences varied widely in regards to treatment decision-making. Conclusions: The variety of themes identified in the analysis indicate that there is a large amount of variability to what preferences are most crucial to patients. Providers should consider individual patient needs and desires rather than using a “one size fits all” approach when making treatment decisions. Findings from this study could aid in future SDM implementations.

scholarly journals Qualitative Study of Factors That Influence Treatment Decision-Making Among Community Oncologists and Older Patients with Acute Myeloid Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2246-2246 ◽  
Author(s):  
Kah Poh Loh ◽  
Sindhuja Kadambi ◽  
Supriya G. Mohile ◽  
Jason H. Mendler ◽  
Jane L. Liesveld ◽  
...  
Keyword(s):  
Decision Making ◽  
Qualitative Study ◽  
Older Patients ◽  
Clinical Decision Making ◽  
Real Life ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Cell Transplant ◽  
Hematopoietic Stem ◽  
Treatment Decision Making

Abstract Introduction: Despite data supporting the safety and efficacy of treatment for many older adults with AML, <40% of adults aged ≥65 receive any leukemia-directed therapy. The reasons for why the majority of older patients with AML do not receive therapy are unclear. The use of objective fitness measures (e.g. physical function and cognition) has been shown to predict outcomes and may assist with treatment decision-making, but is underutilized. As most patients are initially evaluated in community practices, exploring clinical decision-making and the barriers to performing objective fitness assessments in the community oncology setting is critical to understanding current patterns of care. We conducted a qualitative study: 1) to identify factors that influence treatment decision making from the perspectives of the community oncologists and older patients with AML, and 2) to understand the barriers to performing objective fitness assessments among oncologists. The findings will help to inform the design of a larger study to assess real-life treatment decision-making among community oncologists and patients. Methods: We conducted semi-structured interviews with 13 community oncologists (9 states) and 9 patients aged ≥60 with AML at any stage of treatment to elicit potential factors that influence treatment decisions. Patients were recruited from the outpatient clinics in a single institution and oncologists were recruited via email using purposive samples (patients: based on treatment received and stage of treatment; oncologists: based on practice location). Interviews were audio-recorded and transcribed. We utilized directed content analysis and adapted the decision-making model introduced by Zafar et al. to serve as a framework for categorizing the factors at various levels. A codebook was provisionally developed. Using Atlas.ti, two investigators independently coded the initial transcripts and resolved any discrepancies through an iterative process. The coding scheme was subsequently applied to the rest of the transcripts by one coder. Results: Median age of the oncologists was 37 years (range 34-64); 62% were females, 92% were white, 38% had practiced more than 15 years, and 92% reported seeing <10 older patients with AML annually. Median age of the patients was 70 years (64-80), 33% were females and all were Caucasian. In terms of treatment, 66% received intensive induction therapy, 22% received low-intensity treatment, and 11% received both. Three patients also received allogeneic hematopoietic stem cell transplant. Eighty-nine percent were initially evaluated and 56% were initially treated by a community oncologist. Factors that influenced treatment decision-making are shown in Figure 1. When making treatment decisions, both patients and oncologists considered factors such as patient's overall health, chronological age, comorbidities, insurance coverage, treatment efficacy and tolerability, and distance to treatment center. Nonetheless, there were distinct factors considered by patients (e.g. quality of care and facility, trust in their oncologist/team) and by oncologists (e.g. local practice patterns, availability of transplant/clinical trials, their own clinical expertise and beliefs) when making treatment decisions. The majority of oncologists do not perform an objective assessment of fitness. Most common reasons provided included: 1) Do not add much to routine assessments (N=8), 2) Lack of time, resources, and expertise (N=7), 3) Lack of awareness of the tools or the evidence to support its use (N=4), 4) Specifics are not important (e.g. impairments are clinically apparent and further nuance is not necessarily helpful; N=5), 5) Impairments are usually performed by other team members (N=2), and 6) Do not want to rely on scores (N=2). Conclusions: Treatment decision-making for older patients with AML is complex and influenced by many factors at the patient, disease/treatment, physician, and organizational levels. Despite studies supporting the utility of objective fitness assessments, these were not commonly performed in the community due to several barriers. Our framework will be useful to guide a larger study to assess real-life treatment decision-making in the community settings. We also identified several barriers raised by community oncologists that could be targeted to allow incorporation of objective fitness assessments. Figure 1. Figure 1. Disclosures Liesveld: Onconova: Other: DSMB; Abbvie: Honoraria. Stock:Jazz Pharmaceuticals: Consultancy. Majhail:Anthem, Inc.: Consultancy; Atara: Honoraria; Incyte: Honoraria. Wildes:Janssen: Research Funding. Klepin:Genentech Inc: Consultancy.

scholarly journals Treatment Decision Making and Treatment Satisfaction Among Individuals Living with Chronic Myeloid Leukemia

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4787-4787
Author(s):  
Julie Olson ◽  
Shauna McManus ◽  
Melissa F. Miller ◽  
Thomas W. LeBlanc ◽  
Eva Yuen ◽  
...  
Keyword(s):  
Decision Making ◽  
Treatment Outcomes ◽  
Board Of Directors ◽  
Research Funding ◽  
Treatment Options ◽  
Treatment Decision ◽  
Treatment Decisions ◽  
Advisory Committees ◽  
Treatment Decision Making ◽  
Satisfaction With Treatment

Abstract Background: Over the past decade, an increase in treatment options for chronic myeloid leukemia (CML) has dramatically changed the therapeutic landscape and has improved clinical outcomes. This abundance of treatment options may make it difficult for CML patients to feel knowledgeable about what options are available to them, may hinder patients' preparedness for having conversations about treatment, and, similarly, may contribute to patients feeling less involved in treatment decision making (TDM). In light of this changing landscape, we explored whether the TDM experience was linked to satisfaction with treatment outcomes in a national sample of CML patients. Methods: Using data from the Cancer Support Community's Cancer Experience Registry®, our analytic sample included 310 participants who reported CML as their primary diagnosis. The dependent variable in all analyses was a dichotomous, patient-reported indicator of satisfaction with treatment outcomes (satisfied or not satisfied). Our independent variables include three measures that capture the TDM experience: feeling involved in the TDM process; feeling knowledgeable about treatment options prior to making treatment decisions; and, feeling prepared to discuss treatment options with one's doctor. Respondents ranked TDM knowledge, preparedness, and involvement from 0 = "not at all" to 4 = "very much." Responses were dichotomized such that 1 = "quite a bit" or "very much" and 0 = "not at all," "a little bit" or "somewhat." Analytically, we compared patients who reported high satisfaction with treatment outcomes to those who reported low satisfaction, using Student's t-test. Then, we estimated multivariate logistic regression models predicting odds of being satisfied with treatment outcome by TDM knowledge, preparedness, and involvement. Regression models controlled for demographic characteristics including age, gender, and race; clinical factors such as time since diagnosis and symptom burden; treatment-related measures including financial impact of treatment; and the degree to which individuals felt their health care teams prepared them to manage treatment side effects. Results: Descriptively, our sample was 65% female and 87% non-Hispanic White, with an average age of 56.6 years (SD = 12) and mean time since diagnosis of 6 years (SD = 5). Most (74%) reported being "quite a bit" to "very much satisfied" with their treatment outcomes. Experiences with TDM, however, were variable. When making treatment decisions, 52% reported feeling involved, 41% reported feeling knowledgeable, and 21% felt prepared. Importantly, t-test results suggested that individuals with greater involvement, more knowledge, and higher preparedness were significantly more likely to report satisfaction with treatment outcomes. Results of the multivariate models demonstrated a greater likelihood of treatment satisfaction among individuals who felt prepared to discuss treatment options with their health care team, even after controlling for demographic, clinical, and treatment-related characteristics. In fact, prepared individuals were nearly 6 times as likely to be satisfied with their treatment outcomes, as compared to individuals who did not feel prepared to discuss treatment options (p < .05). Conclusion: Most of our patients with CML did not feel prepared to make treatment decisions. However, those who feel more prepared to discuss treatment options with their doctors are also more likely to report satisfaction with treatment outcomes. As new CML treatment options become available, our results highlight the need for an increased focus on shared decision making in clinical practice. This may necessitate providing patients with more resources to help prepare them for treatment-related conversations. Disclosures Birhiray: Takeda: Research Funding, Speakers Bureau; Genomic Health: Patents & Royalties; Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Alexion: Consultancy; Puma: Research Funding, Speakers Bureau; Pharmacyclics: Speakers Bureau; Janssen: Consultancy, Speakers Bureau; Bristol Myers Squibb: Speakers Bureau; Norvatis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bayer: Membership on an entity's Board of Directors or advisory committees; Eli-Lilly: Speakers Bureau; Excelis: Speakers Bureau; Clovis Oncology: Speakers Bureau; Sanofi Oncology: Speakers Bureau; Incyte: Speakers Bureau; AstraZeneca: Speakers Bureau; Tessaro: Speakers Bureau; Pfizer: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees; Helsinn: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees.

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