Quill, Timothy A.

Abstract Background: Recent approvals and clinical trials have established covalent BTK inhibitors (BTKis), including ibrutinib, acalabrutinib, and zanubrutinib, as effective agents for the treatment of mantle cell lymphoma. Multiple BTKi-based regimens are recommended by experts and clinical guidelines in both the first-line and later-line settings. Additional approvals of the noncovalent BTKi pirtobrutinib and multiple CAR T-cell therapies have also advanced the management of patients who have progressed on covalent BTKi therapy. In light of these developments in treatment strategies for mantle cell lymphoma (MCL), this study compares trends in treatment choice among experts and healthcare professionals (HCPs) in an online treatment decision support tool. Methods: An online decision support tool, developed with 5 US-based experts in MCL, was launched in April 2023 and updated in April 2025. The tool provided treatment recommendations for various clinical scenarios involving newly diagnosed (ND) or relapsed/refractory (R/R) MCL, incorporating factors such as age, fitness, previous therapies, and response duration. HCPs entered specific patient characteristics along with their intended treatment choice. The tool then displayed expert recommendations for that scenario, and HCPs were asked whether these recommendations influenced their treatment decision. HCP-entered data and expert recommendations were analyzed to identify common trends and divergence in current BTKi therapy use. Results: The 2023 online tool was used by 119 HCPs who entered 148 patient scenarios, and the 2025 online tool was used by 69 HCPs who entered 96 patient scenarios. In 2023, for patients with ND MCL who had later-stage disease or were not eligible for radiation therapy (n = 75), 64% of expert-recommended treatments included BTKis, with the ECHO and TRIANGLE regimens representing 12% and 17% of recommendations, respectively. In 2025, 81% of expert recommendations were BTKi-based regimens, with ≥3 experts recommending a BTKi regimen in 66 of 67 unique ND case scenarios. The ECHO and TRIANGLE regimen each represented 28% to 29% of expert recommendations. In addition, 51% of recommendations for patients with 17p deletions or TP53 mutations consisted of the BOVen regimen. The percentage of HCPs who indicated that they were considering a BTKi-based regimen in the setting of ND MCL was 17% (13 of 75 ND cases) in 2023 and 66% (44 of 67 ND cases) in 2025. The use of both the ECHO and TRIANGLE regimens by HCPs increased from 5% and 4%, respectively, in 2023 to 19% for each in 2025. In 2025, 6% of HCPs considered the BOVen regimen for patients with TP53 mutation (n = 17). In 2023 and 2025, approximately 50% of the HCP-entered patient cases for previously treated MCL had not received a BTKi (n = 48 and n = 18, respectively). For these scenarios, 94% and 98% of expert recommendations consisted of covalent BTKis in 2023 and 2025, respectively. HCPs selected covalent BTKis for 68% of cases in 2023 and 89% of cases in 2025. For patients with R/R MCL previously treated with a BTKi, CAR T-cell therapy represented 36% of expert recommendations in 2023 (23 scenarios) and 60% in 2025 (18 scenarios). By contrast, 4% and 11% of HCPs considered CAR T-cell therapy for these patients in 2023 and 2025, respectively. For HCPs reporting on the tool's clinical impact (n = 96), 35% indicated expert recommendations changed their intended therapy, 41% stated their treatment plan matched the recommendations, 10% cited barriers to implementing recommendations, and 14% remained undecided on optimal treatment. Conclusions:This analysis highlights the ongoing adoption of expert-recommended treatments into real-world clinical practice. Our data suggest that expert and HCP use of BTKi therapy in the ND MCL setting has increased in recent years. In R/R MCL, CAR T-cell therapy appears to be considered infrequently by HCPs. Barriers to implementation of expert recommendations warrant further investigation. Enhanced decision support tools and targeted educational initiatives could help increase the use of expert treatment recommendations and potentially improve patient outcomes.

501 Background: Targeted treatment options have significantly improved outcomes for patients with chronic lymphocytic leukemia (CLL), but oncology healthcare professionals (HCPs) who provide care for fewer numbers of patients with CLL are challenged to remain current with continuously changing treatment paradigms. To assess ongoing educational needs, we analyzed the treatment choices made by HCPs in an online survey for select cases of poor-prognosis CLL and compared these with the choices of experts in CLL treatment for the same clinical scenarios. Methods: As part of a CME program, HCPs could access an online algorithm to enter specific characteristics of a CLL case scenario and receive treatment recommendations from 5 US-based CLL experts. Experts made recommendations in July 2024 for 146 possible case patients built into the algorithm. After entering their case characteristics, HCPs were asked to enter their planned treatment prior to viewing the expert recommendations. We compared expert and HCP treatment choices across various case scenarios that were entered into the algorithm, with a focus on previously untreated CLL with del(17p)/mutated TP53 or R/R CLL. Results: Between August 2024 and February 2025, 346 cases were entered into the algorithm by 174 HCPs. Among responding HCPs, 38% were based in the UK/EU, 24% in the US, and 10% in Asia; 62% treated ≤10 patients with CLL per month and 49% sought recommendations for a specific patient in their practice. Among 231 cases of previously untreated CLL entered, 91 (39%) sought recommendations for del(17p)/mutated TP53 disease. For cases with no major comorbidities and previously untreated CLL with del(17p)/mutated TP53 , all experts recommended acalabrutinib or zanubrutinib; in 19 cases submitted by HCPs practicing in locations where second-generation BTK inhibitors (BTKi) are approved for CLL (including the US, EU, and Japan), 58% planned 1 of these treatments for this case scenario, 17% planned ibrutinib-based therapy, and 11% planned venetoclax-based therapy. Among case scenarios requiring third-line therapy after progressing with 2 regimens including a covalent BTKi and venetoclax plus an anti-CD20 Ab (with progression <2 years after completing treatment), all experts would recommend a noncovalent BTKi (eg, pirtobrutinib), with 1 expert indicating they would also consider CAR T-cell therapy. For the same case scenario among 6 US-based HCPs, 1 planned pirtobrutinib and 3 planned CAR T-cell therapy. Overall, after reviewing expert recommendations, 61% of HCP respondents whose planned treatment differed from the experts indicated that they would change their approach, and 20% indicated barriers to implementing the recommendations. Conclusions: Our findings provide insight into potential educational needs among HCPs who manage patients with CLL, suggesting that targeted expert-led education could benefit these HCPs.

Background: The management of multiple myeloma (MM) continues to evolve with the emergence of new classes of therapy, most recently including BCMA-targeted therapies. Considering the expanding MM treatment armamentarium, we sought to identify contemporary practice patterns among healthcare professionals (HCPs) for MM and compare them with those of experts using an online decision support tool. Methods: An online decision support tool was developed with input from 5 experts providing therapy recommendations for 192 unique patient case scenarios based on considerations including disease setting, treatment history, access to therapy, and specific comorbidities. HCP tool users entered specific patient criteria to define a case along with their intended management for that case. The tool then showed the 5 expert recommendations for that same case scenario, and users were asked if the recommendations impacted their intended approach. An analysis of expert recommendations and user-selected therapy was performed. Results: Between April 2023 and July 2023, 207 patient cases were entered by 144 participating HCPs. HCP and expert treatment recommendations were frequently discordant, regardless of treatment setting (Table). The highest concordance between HCP and expert recommendations (47%; P &lt;.0001) was in the induction setting (n = 111). In this setting, experts were more likely to recommend quadruplet regimens compared with HCPs (62% vs 27%, respectively; P &lt;.0001). Treatment selection concordance was lower in the maintenance (n = 17; 24%; P &lt;.0001) and relapsed/refractory settings (n = 79; 20%; P &lt;.0001). The lowest concordance (10%; P &lt;.0001) between HCP and expert treatment recommendations was noted in the relapsed/refractory setting specifically among patients who were refractory to earlier lines of therapy (n = 41; P &lt;.0001). For patients who are refractory to earlier lines of therapy, experts were more likely to recommend an anti-CD38-based combination regimen compared with HCPs (81% vs 32; P &lt;.0001). When recommending treatment for patients with triple- or penta-refractory disease, experts were significantly more likely to recommend BCMA-targeted agents (83% vs 39%; P &lt;.0001). For HCPs whose treatment plan did not match expert recommendations in these scenarios, 53% of respondents indicated that expert recommendations confirmed or changed their intended therapy, but 31% indicated that there were barriers to implementing those recommendations. Conclusions: These data suggest ongoing challenges with incorporating the newest therapies and combinations across the spectrum of MM into patient care, particularly multidrug regimens in the induction and maintenance settings and BCMA-targeted therapies in those with triple- or penta-refractory disease. Continued education and development of resources for HCPs, including online decision support tools, may be increasingly important as the treatment of MM continues to grow in complexity.

Introduction MCL is a rare cancer, and current clinical practice guidelines list multiple reasonable treatment options for MCL. It can be challenging to select treatment (Tx) that optimizes outcomes for patients with a mixture of presenting features. In 2014, we developed an online MCL decision support tool designed to provide expert guidance on optimal treatment for defined patient scenarios. An analysis of the 2014 tool showed that Tx recommendations of community healthcare providers (HCPs) did not consistently align with experts for many patients with MCL (Gopalsamy, et al. Leuk Lymphoma. 2019 Mar 8:1-9). Here we report data from an updated version (2018) of this tool that captures evolving practice patterns among experts and community HCPs and the differences between these 2 groups. Methods In September 2018, a panel of 5 experts provided Tx recommendations for 240 patient scenarios in newly diagnosed or relapsed/refractory MCL. These distinct scenarios were defined by patient and disease characteristics that the experts considered important when making treatment choices, including disease setting, patient age and fitness, cytogenetic information, LDH and Ki67 levels, and information on any previous lines of therapy. To use the tool, HCPs entered specific patient/disease characteristics using expert-defined options along with the Tx planned for each case. The users were then shown the 5 MCL expert recommendations and were asked to indicate the impact of the expert recommendations on their planned Tx approach. Results A total of 387 HCPs entered 654 patient scenarios in the 2018 online tool. For HCPs who answered an optional question on how many patients with MCL they treat per year (n = 145), 81% indicated that they treated 10 or fewer patients with MCL per year, highlighting the rarity of this disease and lack of experience among many HCPs. Furthermore, 74% of these HCPs indicated that they refer &lt; 50% of their patients with MCL for clinical trials or to academic institutions for treatment. Among experts, the primary consensus for initial Tx recommendations for younger, fit patients with MCL were bendamustine/rituximab (BR) and R-HyperCVAD in both 2014 and 2018. In comparison, HCPs chose these 2 regimens for a minority of these patients with newly diagnosed MCL in both editions of the tool. For older or unfit patients, experts predominantly recommended BR (85% in 2014 and 75% in 2018), whereas only 35% to 40% of HCPs would choose this regimen in this setting (Table). Of note, nearly 20% of the HCPs indicated that they would recommend a very aggressive chemotherapy regimen for older or unfit patients. For patients with disease progression after chemoimmunotherapy, the recommendation of ibrutinib-based therapy increased among experts from 2014 to 2018 from 66% to 100%. By contrast, the percentage of HCPs using ibrutinib-based therapy remained low at 29% or 37% in the 2014 and 2018 tools, respectively. Another 14% of HCPs selected acalabrutinib, another approved BTK inhibitor, in 2018. In addition, approximately 20% of HCPs in both 2014 and 2018 were unsure of the optimal use of additional therapy (eg, ASCT or maintenance therapy) for their patients. Among users who answered an optional question on the tool's clinical impact, 47% indicated the expert recommendations changed their planned Tx and 38% indicated that expert recommendations confirmed their approach. Conclusions The analysis of data from this online tool found several areas of expert consensus regarding Tx for patients with MCL, particularly for patients who are progressing after initial chemoimmunotherapy. However, practice patterns of community HCPs did not align with expert recommendations for the majority of cases entered into the tool. The consistent Tx gap between practice patterns of community HCPs and expert recommendations over this time period suggests a continued need for online decision tools like this and other innovative approaches to involve experts in the care of patients with MCL. A detailed comparison of expert and user data from the online tool will be presented. Disclosures Ansell: LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding. Leonard:Nordic Nanovector: Consultancy; Karyopharm Therapeutics: Consultancy; BeiGene: Consultancy; Sutro Biopharma: Consultancy; Gilead: Consultancy; MorphoSys: Consultancy; Merck: Consultancy; Nordic Nanovector: Consultancy; Akcea Therapeutics: Consultancy; MorphoSys: Consultancy; Miltenyi: Consultancy; Epizyme, Inc: Consultancy; AstraZeneca: Consultancy; Bayer Corporation: Consultancy; Celgene: Consultancy; AstraZeneca: Consultancy; ADC Therapeutics: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Gilead: Consultancy; Bayer Corporation: Consultancy; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy; Celgene: Consultancy; Karyopharm Therapeutics: Consultancy; Sutro Biopharma: Consultancy; Epizyme, Inc: Consultancy; Merck: Consultancy; Sandoz: Consultancy; Akcea Therapeutics: Consultancy; Miltenyi: Consultancy; Sandoz: Consultancy; ADC Therapeutics: Consultancy; BeiGene: Consultancy. Vose:Acerta Pharma: Honoraria, Other: Grants, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Celgene Corporation: Research Funding; Incyte Corporation: Research Funding; Kite Pharma: Honoraria, Other: Grants, Research Funding; Novartis: Research Funding; Seattle Genetics: Research Funding; AbbVie: Consultancy, Honoraria; Epizyme: Consultancy, Honoraria; Legend Pharmaceuticals: Honoraria. Wang:BioInvent: Consultancy, Research Funding; Dava Oncology: Honoraria; Aviara: Research Funding; Juno Therapeutics: Research Funding; Celgene: Honoraria, Research Funding; AstraZeneca: Consultancy, Honoraria, Research Funding, Speakers Bureau; Pharmacyclics: Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; VelosBio: Research Funding; Acerta Pharma: Consultancy, Research Funding; Kite Pharma: Consultancy, Research Funding; Guidepoint Global: Consultancy; MoreHealth: Consultancy, Equity Ownership; Loxo Oncology: Research Funding. Flowers:BeiGene: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Denovo Biopharma: Consultancy; Millenium/Takeda: Research Funding; V Foundation: Research Funding; Bayer: Consultancy; Acerta: Research Funding; Genentech, Inc./F. Hoffmann-La Roche Ltd: Consultancy, Research Funding; TG Therapeutics: Research Funding; Burroughs Wellcome Fund: Research Funding; Eastern Cooperative Oncology Group: Research Funding; National Cancer Institute: Research Funding; Optimum Rx: Consultancy; Spectrum: Consultancy; Pharmacyclics/Janssen: Consultancy, Research Funding; AstraZeneca: Consultancy; Gilead: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Karyopharm: Consultancy.

Abstract Background Clinical practice guidelines for MM list many alternative therapeutic options with an equivalent category of evidence but lack recommendations on the best approaches for individual patient cases. To provide clinicians with expert guidance on treatment options for defined patient scenarios, we developed and updated an interactive, online decision aid that allows users to enter specific disease and patient characteristics, enter their planned treatment, and then compare their choice of therapy with those of an MM expert panel for that scenario. Here we report data from the most recent version (2015) of this tool, capturing changes in expert recommendations and treatment trends for MM over time and evaluating the impact of this online tool on clinical care. Methods Each of 3 annual updates to the tool were developed with input from a panel of 5 MM experts. The individual experts provided treatment recommendations for multiple patient scenarios across the settings of induction therapy, maintenance therapy, and therapy for relapsed/refractory MM. A series of pull-down menus allowed users to select individual patient and disease characteristics identified by experts as important to consider when making treatment choices. These characteristics included: eligibility for autologous stem cell transplantation (for induction therapy only), results of chromosome analysis (cytogenetics/FISH), ECOG performance status, risk of renal insufficiency or peripheral neuropathy, previous therapy and depth of response (for maintenance and relapsed/refractory settings), and cardiac or pulmonary dysfunction (for relapsed/refractory setting). Users were then asked to state their intended management approach for that particular patient scenario. Once completed, recommendations for that scenario from each of the experts were displayed, and users were asked to indicate the impact of the expert recommendations on their treatment choice. Results Expert recommendations spanning the continuum of care were compiled in October 2012, November 2013, and March 2015 for the 3 different MM tool updates. As for earlier versions of the MM tool, each of the experts in the 2015 update provided recommendations for 32 case scenarios for induction therapy, for a total of 160 possible induction recommendations. Comparing overall induction therapy recommendations the use of bortezomib/lenalidomide/dexamethasone was approximately 40% in both 2013 and 2015, whereas the use of carfilzomib-containing regimens increased from 5% to 17%. None of the experts chose regimens with melphalan as induction therapy in 2015. In the relapsed/refractory setting, the use of newer therapeutic approaches continued to grow especially in the setting of patients who did not respond to induction therapy or relapsed within 6 months. For example, in those with prior proteasome inhibitor (PI) therapy, the selection of carfilzomib/lenalidomide/dexamethasone by experts rose from 11% to 38% from 2013 to 2015 with pomalidomide and lenalidomide selected for 20% to 25% of cases in the most recent tool. For prior IMiD and PI, the experts selected a new combination of pomalidomide with a PI (either bortezomib or carfilzomib) and dexamethasone in 55% of the case scenarios in 2015. To date, 170 different clinicians sought guidance on more than 259 patient case scenarios using the 2015 decision support tool. In the subset of cases (n = 104) in which users reported how the tool affected their management of patients with MM, 68% indicated the expert recommendations either confirmed or changed their intended treatment (22% changed; 46% confirmed), whereas 17% indicated that there would be barriers to implementing the expert recommendations, and 5% said they disagreed with the experts' recommendations. Conclusions Expert opinions regarding optimal MM therapy continue to evolve with new evidence. Our data show that interactive online therapy decision aids that provide expert recommendations to clinicians for specific case scenarios can aid decision making and capture practice trends in this rapidly evolving environment. Preliminary data suggest that most clinicians using the online decision aid either confirmed or changed their treatment approaches for specific MM patient case scenarios based on expert recommendations. A detailed and updated analysis of expert and user data will be presented. Disclosures Kumar: Janssen: Consultancy, Research Funding; Novartis: Research Funding; Onyx: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Skyline: Consultancy, Honoraria; BMS: Consultancy; Sanofi: Consultancy, Research Funding. Lentzsch:BMS: Consultancy; Novartis: Consultancy; Axiom: Honoraria; Janssen: Consultancy; Celgene: Consultancy. Lonial:Novartis: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Onyx: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Roodman:Amgen: Consultancy; Eli Lilly: Research Funding. Mortimer:AstraZeneca: Other: spouse is an employee of and has equity ownership in.

Abstract Background In 2015, the FDA approved 5 new agents and/or combination regimens for the treatment of patients with relapsed/refractory (R/R) MM. In addition, new clinical data support using new regimens in the first-line setting. This rapid expansion of available treatment options has greatly increased the complexity of treatment decisions for patients in this disease setting. Clinical practice guidelines list multiple preferred treatment options for MM and can be challenging to apply to specific patients with a mixture of presenting features. Since 2013, we have developed and updated an MM online decision support tool designed to provide clinicians with expert guidance on optimal treatment for defined patient scenarios. An analysis of the tool over the years has shown that experts rapidly integrate new data and available agents into practice whereas many tool users do not. Here we report data from the most recent version (2016) of this tool, capturing the impact of the rapid expansion of new therapies on expert treatment recommendations. Methods In June 2016, a panel of 5 experts provided treatment recommendations for 688 case scenarios across 3 settings: induction, maintenance, and R/R disease. These distinct scenarios were defined by key patient and disease characteristics that the experts considered important when making treatment choices, including: eligibility for autologous stem cell transplantation, results of chromosome analysis (cytogenetics/FISH), ECOG performance status, previous therapy and response, and preexisting comorbidities. To use the tool, these patient and disease factors and the intended treatment for that specific patient were entered through drop-down menus to display the expert choices for that case. Users were then asked to indicate the impact of the expert recommendations on planned treatment. Results We compared expert recommendations from the 2 most recent versions of our MM tool, compiled in March 2015 and June 2016, to assess the impact of the new data and FDA approvals on treatment patterns. The experts are generally not recommending these agents as induction or maintenance therapy. Three out of 5 experts recommended carfilzomib/lenalidomide/dexamethasone as induction therapy for preexisting peripheral neuropathy (PN) and 1 expert recommended ixazomib-based regimens as induction and maintenance therapy for patients with high-risk cytogenetics and both renal insufficiency and PN. The new agents did have a substantial impact on expert recommendation in the R/R setting. In the 2016 tool, the type of previous therapy was the primary factor affecting expert selections, whereas the number of previous lines of therapy (1-3 vs >3) had minimal impact in the R/R setting. For example, in patients refractory to previous proteasome inhibitor (PI) therapy, regimens containing either elotuzumab (30%) or daratumumab (25%) were selected frequently by experts with pomalidomide-containing regimens recommended in another 40% of these specific cases. In the 2016 tool, the use of lenalidomide/dexamethasone alone without a newly approved agent was not recommended by any expert in the R/R setting, although it had been recommended by experts in 25% of R/R case scenarios in the 2015 tool. For patient scenarios refractory to previous immunomodulatory drugs and PIs, the experts recommended daratumumab-based regimens in 67.5% of the cases in the 2016 tool whereas the use of carfilzomib/pomalidomide/dexamethasone fell from 45% in 2015 to 10% of the cases in 2016. None of the experts recommended regimens with panobinostat as their preferred choice in any patient scenario in either 2015 or 2016. Conclusions Expert opinions regarding optimal MM therapy continue to evolve with new evidence and FDA approvals. Our data show that newly approved therapies are having a large impact on expert recommendations for specific case scenarios in the relapsed/refractory MM setting. A detailed and updated analysis of expert and user data that captures practice trends in this rapidly evolving environment will be presented. Disclosures Kumar: Kesios: Consultancy; Array BioPharma: Consultancy, Research Funding; AbbVie: Research Funding; Onyx: Consultancy, Research Funding; Sanofi: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Research Funding; BMS: Consultancy; Glycomimetics: Consultancy; Janssen: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding. Lentzsch:Celgene: Consultancy, Honoraria; BMS: Consultancy. Lonial:Merck: Consultancy; Novartis: Consultancy; Millenium: Consultancy; Celgene: Consultancy; Onyx: Consultancy; Janssen: Consultancy; Onyx: Consultancy; BMS: Consultancy; Novartis: Consultancy; Janssen: Consultancy; Celgene: Consultancy; BMS: Consultancy. Roodman:Amgen: Consultancy. Anderson:Oncopep: Other: Scientific Founder; Acetylon: Other: Scientific Founder; Sonofi Aventis: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Onyx: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees.

Abstract Background: The process known as shared decision-making (SDM) helps patients and providers make collaborative health care decisions that consider the available clinical evidence as well as the patient's values and preferences. Engaging the patient's participation in their own medical care is also a pillar of the National Strategy for Quality Improvement in Health Care. However, studies have shown a disconnect between provider perception of their ability to engage patients in their own care and patients' perception that their experiences and preferences are not being considered in the treatment decision-making process. Multiple Myeloma (MM) in particular is a complicated disease to manage with rapidly-evolving data and treatment standards. Treatment guidelines list multiple reasonable treatment options and often do not provide recommendations that are specific for individual patients. Therefore, patient-provider collaboration in treatment decision-making is a particularly important aspect of optimal MM care. This report aims to examine whether continuing medical education (CME) programming can help prepare physicians and other care providers to integrate the latest clinical data as well as the preferences of their patients into the decision-making process. Methods: A CME-certified, video-based educational program was developed and posted on the Clinical Care Options' website on July 16, 2015. The educational program covered SDM concepts along with review of the clinical data and treatment guidelines critical to make optimal treatment decisions for patients with MM. Program participants were asked a series of 3 questions at the start of the program (baseline) and again after program completion (post-education). Data were collected from July 16, 2015 to December 4, 2015 from the 1253 program participants (64% MDs/NPs/PAs, 7% nurses; Figure 1) The impact of the education was calculated and reported as a Cohen's d effect using a matched-pair response comparison (n = 85) from the baseline and post-education questions. Results: At baseline, only 24% of program participants indicated that they would consider patient preferences as part of their decision-making process (Table 1). This significantly increased to 69% (45% improvement; P < .0001) among those that completed the program. The second question exposed program participants to a case vignette that challenged their ability to consider the available data along with patient preferences when making a treatment decision for a patient with MM. At baseline, only 51% of participants chose a treatment course that matched expert recommendations. However, after program completion, 90% chose the optimal answer, representing a 39% improvement (P = .0003). Finally, when asked to explain and manage a treatment-related adverse event, 47% of participants chose the optimal response at baseline and after completing the educational activity, 90% chose the optimal response, representing a 43% improvement (P = .0008). Overall, practical significance testing indicated that the activity had a positive impact on learners, with a Cohen's d effect size calculation of +1.41. In a post activity survey, each participant was asked how many of their patients were likely to benefit from their participation in this activity; the results totaled 7993 patients. Conclusions: These data strongly suggest that CME programs focused on SDM can help prepare providers to consider patient preferences along with the available clinical data to make treatment decisions most likely to result in outcomes that matter most to their patients. Further randomized studies would be warranted to confirm these results. A full review of the study results and recommendations will be presented. This program was supported by independent educational grants from Celgene Corporation, Onyx Pharmaceuticals, and Takeda Pharmaceuticals North America, Inc. Overall Learner Breakdown by Degree and Specialty Overall Learner Breakdown by Degree and Specialty Figure 1 Figure 1. Disclosures Kumar: Skyline: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Research Funding; BMS: Consultancy; Onyx: Consultancy, Research Funding; Kesios: Consultancy; Sanofi: Consultancy, Research Funding; AbbVie: Research Funding; Glycomimetics: Consultancy; Janssen: Consultancy, Research Funding; Millennium: Consultancy, Research Funding; Array BioPharma: Consultancy, Research Funding; Noxxon Pharma: Consultancy, Research Funding.