Clinical Impact of an Internet-Based Tool to Help Guide Therapeutic Changes While Monitoring Patients with Chronic Myeloid Leukemia Receiving First-Line Tyrosine Kinase Inhibitor Therapy

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4279-4279
Author(s):  
Kevin L. Obholz ◽  
Daniel J. DeAngelo ◽  
Michael J. Mauro ◽  
Neil Shah ◽  
B. Douglas Smith ◽  
...  
Keyword(s):  
Research Funding ◽  
Kinase Inhibitor ◽  
Decision Support Tool ◽  
Treatment Decisions ◽  
Therapeutic Change ◽  
Management Approach ◽  
First Line ◽  
Specific Patient ◽  
Support Tool ◽  
Expert Recommendations

Abstract Abstract 4279 Background European LeukemiaNet (ELN) and National Comprehensive Cancer Network (NCCN) recommendations are important resources to help guide the management of patients with chronic myeloid leukemia (CML) treated with tyrosine kinase inhibitor (TKI) therapy. However, current guidelines are sometimes difficult to apply to all patient scenarios, particularly as they do not provide definite management recommendations for patients who have suboptimal responses to first-line TKI therapy. Furthermore, in a recent survey only 58% (N=132) of community oncologists made treatment decisions in line with expert recommendations for clinical scenarios in which patients had a suboptimal response to first-line therapy with imatinib. Online tools that provide expert clinical guidance have been proposed as one adjunctive approach to help clinicians make more informed treatment decisions. We previously reported that an online tool designed to provide expert guidance on adjuvant treatment of breast cancer may positively impact treatment decisions and thus potentially improve patient care (J Clin Oncol 29: 2011 [suppl; abstr 6063]). Aims/Objectives The goal was to determine whether an interactive online decision support tool providing expert guidance would help community practitioners make more informed therapeutic decisions for patients with CML who were receiving first-line TKI therapy. We sought to evaluate to what extent the expert recommendations changed the community practitioners' clinical approach. Methods An interactive decision support tool, developed with input from 5 CML experts who made treatment recommendations for 42 different patient scenarios, has been posted online at http://www.clinicaloptions.com/TreatingCML. Users of the tool enter specific factors such as patient age and duration of first-line TKI therapy, along with information on hematologic, cytogenetic, and/or molecular responses to first-line TKI therapy at 3, 6, 12 and 18 months. Before expert recommendations for that specific patient scenario are revealed, users are asked to enter their intended management approach. Once that is entered, the tool outputs a table showing the recommendations of the 5 CML experts based on the specific factors entered. Finally, the user is prompted to indicate whether the experts' recommendation confirmed or changed their intended management approach. User response data and intended treatment approaches will be tabulated and compared with the experts' recommendations. Results This resource was posted online July 2012 and had 161 unique users with 219 uses of the tool in the first 40 days. Among the users to date, 15% indicated that the experts' recommendations changed their intended management approach. A separate analysis of the in-tool recommendations showed that the experts considered not only guidelines, but also emerging data and their own clinical experience in making recommendations for specific patient scenarios. For example, ahead of similar recommendations included in the most recent update to the NCCN treatment guidelines for CML, the experts added a consideration in the tool suggesting a BCR-ABL/ABL ratio of 10% by QPCR as the threshold for guiding a therapeutic change at 3 months. Moreover, most of the experts (4 of 5) recommended a therapeutic change for patients in CCyR without MMR at 12 months if there was a concomitant increase in BCR-ABL ratio of 1 log or more and (3 of 5) also recommended a therapeutic change for patients without a MMR at 18 months regardless of whether their BCR-ABL/ABL ratio was increasing. Detailed comparisons of expert and user responses will be presented. Conclusions Preliminary data suggest that an online tool designed to provide customized, patient-specific expert advice may increase the number of clinicians who make optimal treatment decisions for patients with CML based on their response to first-line TKI therapy, and therefore, may be an important adjunct to the ELN and NCCN guidelines. Disclosures: Shah: ARIAD: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy, Research Funding; Novartis: Consultancy. Smith:Bristol-Myers Squibb: Consultancy; Novartis: Consultancy. Radich:Ariad: Consultancy; Bristol-Myers Squibb: Consultancy; Pfizer: Consultancy; Novartis: Consultancy, Research Funding.

scholarly journals Clinical Impact of Internet-Based Tools to Help Guide Therapeutic Decisions for Mantle Cell Lymphoma (MCL)

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4527-4527
Author(s):  
Kristen M Rosenthal ◽  
Christopher R. Flowers ◽  
Andre Goy ◽  
John P. Leonard ◽  
Julie M. Vose ◽  
...  
Keyword(s):  
Decision Support ◽  
Board Of Directors ◽  
Research Funding ◽  
Decision Support Tool ◽  
Treatment Decisions ◽  
Clinical Impact ◽  
Advisory Committees ◽  
Online Tool ◽  
Support Tool ◽  
Expert Recommendations

Abstract Introduction. Multiple regimens listed in current guidelines are reasonable options for treatment of MCL; however, guidance for selecting patient-specific regimens are needed. We sought to determine if expert recommendations, delivered as an online decision support tool, would change or confirm treatment decisions of community practitioners. Methods. An online decision support tool was developed with input from 5 experts to provide therapy recommendations for patient scenarios in newly diagnosed or relapsed/refractory MCL. The online tool included expert insight on 120 scenarios based on criteria such as age, fitness, histology, LDH, and Ki-67 as well as previous therapy and duration of response. Tool users were asked to enter specific patient criteria and their intended management for each case. The tool then showed 5 MCL expert recommendations for the user-entered patient case, and the users were asked to indicate if the expert recommendations changed their intended approach. An analysis of expert recommendations and user-selected therapy was performed. Results. At interim analysis, this online tool was used by over 160 individuals, entering more than 235 patient scenarios. For users reporting on the tool's clinical impact, 73% indicated expert recommendations confirmed or changed their intended therapy and 21% indicated that there were barriers to implementing those recommendations. Data from the online tool showed areas of consensus and controversy in treating patients with MCL (Table 1). In addition, nearly 20% of users were unsure of the optimal use of additional therapy (eg, transplant or maintenance therapy) for their patients. Conclusions. An online tool providing expert advice on specific MCL patient scenarios confirmed or changed the clinical approach for a majority of community practitioners. Online decision support tools may increase the number of clinicians making optimal treatment decisions for patients with MCL, especially when new data, agent indications, and guideline updates must be considered. Detailed comparisons of expert and user responses from the online tool will be presented. Table. Summary of Treatment Choices by Experts and Tool Users for Select Patient Scenarios ND MCL: ≤ 75 yrs, fit Expert, % Users, % Bendamustine-R 44 17 R-HyperCVAD or HyperCVAD 44 28 R-CHOP 0 27 Other aggressive chemotherapy 0 21 Other less aggressive chemotherapy 11 1 Unsure 0 3 ND MCL: > 75 yrs or less fit Expert, % Users, % Bendamustine-R 85 56 R-HyperCVAD or HyperCVAD 0 6 R-CHOP 0 25 Other less aggressive chemotherapy 10 6 Observation 5 0 Unsure 0 6 R/R MCL: < 60 yrs, fit Expert, % Users, % Ibrutinib 34 22 Bendamustine-R 19 19 Lenalidomide-R 25 11 R-HyperCVAD or HyperCVAD 0 11 R-CHOP 0 4 Other aggressive chemotherapy 0 7 Other less aggressive chemotherapy 15 4 Unsure 0 19 R/R MCL: < 60 yrs, unfit or > 60 yrs regardless of fitness Expert, % Users, % Ibrutinib 72 34 Bendamustine-R 13 17 Lenalidomide-R 0 6 R-HyperCVAD or HyperCVAD 0 8 R-CHOP 4 4 Other less aggressive chemotherapy 8 2 Other chemotherapy-free regimens 4 17 Unsure 0 4 *Treatments selected by <3% of either experts or users were not included in summary table. Disclosures Flowers: Genentech: Other: unpaid consultant, Research Funding; Celgene: Other: unpaid consultant, Research Funding; Abbott: Research Funding; Millennium/Takeda: Research Funding; Algeta: Consultancy; Optum Rx: Consultancy; Biogen Idec: Other: unpaid consultant; Roche: Other: unpaid consultant; Spectrum: Research Funding; Gilead: Research Funding. Goy:Acerta: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pharmacyclics/JNJ: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Leonard:Weill Cornell Medical College: Employment; Genentech: Consultancy; Medimmune: Consultancy; AstraZeneca: Consultancy; Spectrum: Consultancy; Boehringer Ingelheim: Consultancy; Vertex: Consultancy; ProNAI: Consultancy; Biotest: Consultancy; Seattle Genetics: Consultancy; Pfizer: Consultancy; Mirati Therapeutics: Consultancy; Gilead: Consultancy; Novartis: Consultancy. Vose:GlaxoSmithKline: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Janssen Biotech: Research Funding; Incyte Corp: Research Funding; Allos Therapeutics/Spectrum: Research Funding; US Biotest, Inc: Research Funding; Acerta Pharma: Research Funding; Bristol-Myers Squibb: Research Funding. Mortimer:AstraZeneca: Other: spouse is an employee of and has equity ownership in . Armitage:Roche: Consultancy; Spectrum: Consultancy; Celgene: Consultancy; GlaxoSmithKline: Consultancy, Membership on an entity's Board of Directors or advisory committees; Tesaro Bio, Inc: Membership on an entity's Board of Directors or advisory committees; Conatus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Ziopharm: Consultancy.

Clinical Impact of an Internet-Based Tool to Provide Expert Guidance for Multiple Myeloma (MM) from 2012 to 2014

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 1298-1298
Author(s):  
Davecia R Cameron ◽  
Sagar Lonial ◽  
Amitabha Mazumder ◽  
Jim Mortimer ◽  
Andrew D Bowser ◽  
...  
Keyword(s):  
Decision Support ◽  
Induction Therapy ◽  
Research Funding ◽  
Decision Support Tool ◽  
Clinical Impact ◽  
Management Approach ◽  
Refractory Disease ◽  
Support Tool ◽  
Previous Therapy ◽  
Expert Recommendations

Abstract Clinical practice guidelines in MM list many therapeutic choices, with similar levels of evidence, but frequently lack specific recommendations for individual patient cases. We sought to determine whether expert recommendations on MM treatment, based on specific disease and patient characteristics and delivered via an interactive, online decision support tool, would affect the planned treatment decisions of community practitioners. Two tools were developed over successive years. We conducted an analysis to determine changes in practice patterns and expert recommendations over time by comparing data from a tool developed in 2014 with those from a similar tool developed in 2013. Both tools were developed based on recommendation surveys from a panel of 5 MM experts. The surveys were completed in October 2012 (2013 tool) and in November 2013 (2014 tool). Each expert provided treatment recommendations for all patient scenarios in 3 settings: induction, maintenance, and relapsed/refractory disease. The expert survey and online tool included 315 patient scenarios based on variations of the following criteria: eligibility for autologous stem cell transplantation, results of chromosome analysis, ECOG performance status, risk of renal insufficiency or peripheral neuropathy, as well as previous therapy and depth of response to previous therapy. The tool functioned through a series of pull-down menus that allowed users to enter a specific patient scenario. Users were then prompted to state their intended management approach for that scenario. Once completed, recommendations of the 5 experts for that scenario were displayed and the users were prompted to indicate how or if the experts’ recommendations changed their intended management approach. In both the 2013 and 2014 surveys, experts responded to 32 case variations for induction therapy, for a total of 160 possible recommendations. The data revealed that the recommendation of bortezomib/lenalidomide/dexamethasone increased from 23% in 2013 to 41% in 2014, whereas recommended regimens with melphalan dropped from 32% to 10%. In addition, use of carfilzomib as induction therapy increased from 0% to 5% of cases from 2013 to 2014. In the relapsed/refractory setting, there was expansion in the number of different regimens recommended by the experts in 2014, most notably for patients who did not respond to induction therapy or relapsed within 6 months. In patients with relapsed/refractory disease treated previously with an immunomodulatory agent (IMiD), carfilzomib regimens were recommended for 23% of patient scenarios in 2013 vs 39% in 2014, the recommendation of pomalidomide increased from 0% of patient scenarios in 2013 to 13% in 2014, and bortezomib/cyclophosphamide/dexamethasone decreased from 43% to 16% of patient scenarios. For patients with relapsed/refractory disease treated with prior IMiDs and proteasome inhibitors (PIs), pomalidomide recommendations rose from 0% to 40%. Nearly 100% of expert recommended lenalidomide-containing regimens for patients previously treated with PIs in 2013, decreasing to only 37% in 2014, whereas the use of both carfilzomib and pomalidomide regimens increased to 49% in these scenarios. The analysis above does not segregate for comorbidities, which was an important aspect of the tool and expert recommendations. To date, the 2014 decision support tool has been used by 154 individuals who sought guidance on 335 patient case scenarios and 115 reported on the tool’s clinical impact. The majority (71%) indicated the expert recommendations confirmed or changed their intended treatment, 20% indicated that there would be barriers to implementing the recommendations, and 9% disagreed with the recommendations. Preliminary data suggest that the majority of practitioners using the online decision support tool either confirmed or changed their treatment approaches on specific MM patient cases. These data suggest that interactive online tools that offer expert recommendations can clarify selection of therapy in induction, maintenance, and relapsed/refractory settings for patients with MM. Detailed comparisons of evolving expert recommendations and an analysis of participant use and responses, including comorbidity segregation, from the 2014 tool will be presented. Disclosures Off Label Use: carfilzomib use in the frontline setting for patients with myeloma. Lonial:Onyx: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Millennium: Consultancy, Research Funding. Mazumder:Celgene: Speakers Bureau; Millennium: Speakers Bureau; Onyx: Speakers Bureau. Anderson:Celgene: Consultancy; Onyx: Consultancy; Millennium: Consultancy; Gilead: Consultancy; Sanofi: Consultancy; Acetylon: Scientific Founder Other; Oncopep: Scientific Founder Other.

Areas of Consensus and Differences Among a Panel of Experts on the Optimal Use of Newly Approved Agents to Treat Multiple Myeloma (MM): Results from an Annually Updated Online Decision Support Tool

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 2379-2379
Author(s):  
Timothy A Quill ◽  
Kristen M Rosenthal ◽  
Shaji Kumar ◽  
Suzanne Lentzsch ◽  
Sagar Lonial ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Research Funding ◽  
Treatment Options ◽  
Decision Support Tool ◽  
Rapid Expansion ◽  
Advisory Committees ◽  
New Agents ◽  
Support Tool ◽  
The Impact ◽  
Expert Recommendations

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.

Analysis of an Online Decision Support Tool for Chronic Lymphocytic Leukemia: Disparities in Treatment Selection Between Experts and Community Practitioners

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 5958-5958
Author(s):  
Kristen M Rosenthal ◽  
Farrukh T. Awan ◽  
Jacqueline C. Barrientos ◽  
Steven E. Coutre ◽  
Kevin L Obholz ◽  
...  
Keyword(s):  
Research Funding ◽  
Treatment Plan ◽  
Decision Support Tool ◽  
Treatment Recommendations ◽  
Treatment Selection ◽  
First Line ◽  
Support Tool ◽  
First Line Therapy ◽  
Line Therapy ◽  
The Impact

Abstract Background. Rapid advances in clinical discovery and availability of new treatment options have increased the complexity of treatment decisions for patients with CLL. Guidelines list multiple agents and combinations as recommended therapeutic options for CLL but often do not provide specific treatment recommendations for individual patients. We developed an online treatment decision tool that provides treatment recommendations from CLL experts for specific patient cases. We hypothesized that these individualized recommendations from recognized experts would affect treatment plans. Here we report on an analysis of data entered into this CLL decision support tool, including variance between intended treatment of tool users and the recommendations made by the experts and the impact of the tool on subsequent therapy decisions. Methods. In December 2015, 5 experts provided treatment recommendations for 1380 case variations based on key factors that guide treatment choice. Expert-selected factors for newly diagnosed CLL included age, fitness (based on ECOG PS, CIRS, and renal function), and cytogenetic abnormalities (del[17p], del[11q], or other). Additional variables for patients with relapsed/refractory (R/R) disease after first-line treatment included previous treatment, response duration, and burden of comorbidities. To use the tool, drop-down menus allowed users to select from choices for each variable and their intended treatment for that patient. The corresponding treatment selection from 5 experts was then displayed and users were asked about the tool's impact on their planned treatment. Results. An analysis of 883 patient scenarios (67% treatment naive and 33% with R/R CLL) entered into the tool from February 2016 through July 2016 found substantial variation between the intended therapy choice among tool users and the recommendations from the experts.For example, in every patient case with del(17p), all 5 of the experts recommended ibrutinib as first-line therapy whereas only 49% of tool users planned to use ibrutinib for these patients. Of those users whose intended first-line therapy for del(17p) CLL did not match the experts' recommendation, 54% indicated that this tool would change their original treatment plan and 17% indicated a barrier to implementing this treatment. For either elderly or unfit patients without del(17p), 4 of 5 experts recommended obinutuzumab plus chlorambucil, but only 41% of tool users planned to use this regimen with 50% citing barriers to this treatment approach. For patients with del(17p) CLL and disease relapse or recurrence after chemoimmunotherapy, all 5 experts recommended ibrutinib for these cases with the exception of patients with a history of atrial fibrillation, anticoagulation, or difficult-to-control hypertension where 4 of 5 experts recommended idelalisib/rituximab. Again, the intended treatment plan of approximately 50% of tool users failed to match the experts' recommendation for these cases, and half of these users indicated that this tool would change their original treatment plan. At the time of tool development, all experts recommended either idelalisib/rituximab or clinical trial for patients with R/R CLL and del(17p) who previously received ibrutinib, but 61% of users indicated that they were unsure of the next appropriate treatment. All users who answered the impact question indicated that they now intended to use the expert-recommended treatment for these patients. For patients without del(17p) cytogenetics, treatment selection was more variable among experts and users and changed based on age, fitness, and previous therapy. For patients with del(11q) or other cytogenetics, approximately 20% of tool users were unsure of the appropriate treatment after progression on first-line therapy but 71% of those who answered the impact questions indicated that they remained unsure of their treatment approach despite viewing expert recommendations. Conclusions. Our analysis demonstrates that this interactive online therapy decision tool providing expert recommendations for specific case scenarios in CLL can support optimal decision making and change intended treatment for a majority of cases in which the planned therapy differed from the experts. Detailed comparisons of expert and user responses from the online tool will be presented. Disclosures Awan: Innate Pharma: Research Funding; Pharmacyclics: Consultancy; Novartis Oncology: Consultancy. Barrientos:AbbVie: Consultancy, Research Funding; Janssen: Consultancy; Gilead: Consultancy, Research Funding. Coutre:AbbVie: Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Research Funding. Zelenetz:Gilead Sciences: Research Funding.

scholarly journals Evolving Treatment Patterns in Chronic Lymphocytic Leukemia Among Experts and Community Practitioners: Analysis of an Online Decision Support Tool

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 41-42
Author(s):  
Ryan P Topping ◽  
Kristen Rosenthal ◽  
Farrukh T Awan ◽  
Jennifer R Brown ◽  
Nicole Lamanna ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Research Funding ◽  
Treatment Decision ◽  
Decision Support Tool ◽  
Treatment Recommendations ◽  
Institutional Research ◽  
Advisory Committees ◽  
Research Grants ◽  
Support Tool ◽  
Expert Recommendations

Introduction The recent approvals of several effective targeted therapies have shifted treatment paradigms for chronic lymphocytic leukemia (CLL). The rapid pace of approvals and expanded indications may challenge oncology healthcare providers (HCPs) to make optimal treatment decisions. We developed an online treatment decision support tool designed to provide HCPs with case-specific treatment recommendations from five CLL experts; this tool has been regularly updated since 2017. In this analysis, we examined the CLL treatment patterns of HCPs and experts from three tool iterations-spanning early 2017 to August 2020-and evaluated comparative trends over time. Methods For each version of the CLL tool, five experts provided treatment recommendations for hundreds of different case scenarios in the newly diagnosed and relapsed/refractory CLL settings. These unique case scenarios were defined by patient and disease factors that the experts considered critical to making treatment decisions, including patient age and fitness, cytogenetic abnormalities, and previous treatment. To use the tool, HCPs entered patient information and their intended treatment plan; expert recommendations for that specific patient scenario were then provided, followed by a survey to determine whether the recommendations impacted the HCP's intended treatment. This analysis compared the intended treatment plans for cases entered into the tool by HCPs with expert recommendations in three versions of the tool (March 2017, October 2018, and October 2019). HCP responses to the post-recommendation survey also were assessed. Results HCPs sought recommendations for 543 cases from March to July 2017 for the 2017 CLL tool, for 656 cases from October 2018 to July 2019 for the 2018 tool, and for 1015 cases from October 2019 to August 2020 for the 2019 tool. HCPs were generally less experienced with CLL; for example, in the 2019 tool, 48% of HCPs who sought treatment recommendations treated ≤ 10 patients with CLL per year. Clear shifts in expert treatment patterns were observed over time (Table). For example, in assessing first-line therapy for patients with CLL with del(17p) or TP53 mutations, the expert panel recommended ibrutinib as first-line therapy regardless of any other characteristic in both the 2017 and 2018 tool iterations; however, in the 2019 tool, experts recommended acalabrutinib plus obinutuzumab, venetoclax plus obinutuzumab, and ibrutinib with similar frequency, dependent upon specific patient characteristics. Similarly, in younger (&lt; 65 years of age), fitter patients with treatment-naive CLL and no del(17p) or TP53 mutations, IGHV mutation status was important for expert treatment recommendations in the 2017 and 2018 tools, as experts recommended fludarabine/cyclophosphamide/rituximab (FCR) for the majority of cases with IGHV mutations and ibrutinib for those without these mutations in both tool iterations. In the 2019 tool, however, experts had shifted toward the use of venetoclax plus obinutuzumab for 50% of cases with IGHV mutations, with FCR still recommended for 40%; for CLL with unmutated IGHV, experts also shifted to venetoclax plus obinutuzumab (70% of cases). Substantial variance was observed between expert recommendations and the planned treatment of HCPs for a variety of case scenarios across tool iterations. For example, in the 2019 tool, experts selected venetoclax plus obinutuzumab for 50% of cases of younger patients with treatment-naive CLL with no del(17p) or TP53 mutations but mutated IGHV; HCPs selected this regimen for 6% of these cases. Overall, after reviewing expert recommendations for their cases, 56% of HCPs whose planned treatment differed from the experts indicated that they would change their treatment based on panel recommendations. Conclusions Analysis of data from progressive iterations of an online treatment decision support tool suggest evolution in best practices in CLL treatment and differences in how experts and community providers manage patients with CLL. Expert recommendations in the tool changed the intended treatment plan of many HCPs, suggesting that online treatment decision tools providing patient-specific expert guidance may increase implementation of optimal therapeutic decisions for advanced CLL. A full analysis of cases entered into the 2019 tool and comparison with previous tools will be presented. Disclosures Awan: Dava Oncology: Consultancy; Kite Pharma: Consultancy; Sunesis: Consultancy; Gilead Sciences: Consultancy; MEI Pharma: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy; Abbvie: Consultancy; Astrazeneca: Consultancy; Genentech: Consultancy; Karyopharm: Consultancy; Celgene: Consultancy; Blueprint medicines: Consultancy. Brown:Gilead, Loxo, Sun, Verastem: Research Funding; Janssen, Teva: Speakers Bureau; Abbvie, Acerta, AstraZeneca, Beigene, Invectys, Juno/Celgene, Kite, Morphosys, Novartis, Octapharma, Pharmacyclics, Sunesis, TG Therapeutics, Verastem: Consultancy. Lamanna:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Oncternal, Verastem, TG Therapeutics: Other: Institutional research grants, Research Funding; Astra Zeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding; MingSight: Other: Institutional research grants, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Juno: Other: Institutional research grants, Research Funding; Octapharma: Research Funding; Loxo: Research Funding; Columbia University Medical Center: Current Employment; Abbvie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding; Bei-Gene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Institutional research grants, Research Funding. Sharman:Roche: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; TG Therapeutics: Consultancy, Research Funding; Acerta: Consultancy, Research Funding; BeiGene: Research Funding; Bristol Meyers Squibb: Consultancy, Research Funding. Zelenetz:MEI Pharma: Research Funding; MorphoSys: Research Funding; Sandoz: Research Funding; BeiGene: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnology: Consultancy; Novartis: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Gilead: Consultancy; Celgene: Research Funding; Roche: Research Funding; Gilead: Research Funding; Genentech/Roche: Consultancy.

Dasatinib (Sprycel®) and Low Intensity Chemotherapy for First-Line Treatment in Patients with De Novo Philadelphia Positive ALL Aged 55 and Over: Final Results of the EWALL-Ph-01 Study

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 666-666 ◽  
Author(s):  
Philippe Rousselot ◽  
Marie Magdeleine Coudé ◽  
Françoise Huguet ◽  
Marina Lafage ◽  
Thibaut Leguay ◽  
...  
Keyword(s):  
Research Funding ◽  
Kinase Inhibitor ◽  
De Novo ◽  
Src Family Kinases ◽  
Line Treatment ◽  
European Level ◽  
First Line ◽  
Factors Associated ◽  
Low Intensity ◽  
First Line Treatment

Abstract Abstract 666 Background. Dasatinib (Sprycel®, Bristol-Myers Squibb) is a potent multi-targeted kinase inhibitor (TKI) of BCR-ABL and SRC family kinases. The EWALL group for adult ALL decided to run a study at the European level evaluating the combination of dasatinib and chemotherapy for Philadelphia positive (Ph+) ALL patients (pts) aged 55 and over. Aim. To analyse efficacy of Dasatinib combined to low intensity chemotherapy and to test factors associated with outcome. (EudraCT 2006–005694-21). Methods. After prephase, dasatinib was administered at 140 mg QD (100 mg over 70y) during the induction period in combination with weekly vincristine (VCR) 1 mg IV and dexamethasone (DEX) 40 mg for 2 days (20 mg over 70y) for 4 weeks. Consolidation Disclosures: Rousselot: BMS, Novartis: Research Funding. Gambacorti-Passerini:BMS, Novartis: Research Funding.

scholarly journals Changes in Expert Recommendations and Global Practice Patterns from 2012-2015: Results from an Annually Updated Online Decision Aid for Multiple Myeloma (MM)

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 2105-2105 ◽  
Author(s):  
Timothy A Quill ◽  
Shaji K Kumar ◽  
Suzanne Lentzsch ◽  
Sagar Lonial ◽  
G. David Roodman ◽  
...  
Keyword(s):  
Induction Therapy ◽  
Research Funding ◽  
Decision Aid ◽  
Clinical Care ◽  
Performance Status ◽  
New Combination ◽  
Management Approach ◽  
Patient Case ◽  
The Impact ◽  
Expert Recommendations

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.

scholarly journals 754. A Two-step Testing Algorithm for Hospital-onset Clostridioides difficile Infection (CDI) Reduces Prescribing of C. difficile (CD) Therapy but Its Ability to Guide Treatment Decisions Remains Unclear

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S474-S474
Author(s):  
Mackenzie Dolan ◽  
Heather Cox ◽  
Cirle A Warren ◽  
Costi Sifri ◽  
Melinda Poulter ◽  
...  
Keyword(s):  
Decision Support Tool ◽  
Treatment Decisions ◽  
Adverse Outcomes ◽  
Nucleic Acid Amplification ◽  
Test Results ◽  
Patient Death ◽  
Support Tool ◽  
Clostridioides Difficile ◽  
Days Of Therapy ◽  
Testing Algorithm

Abstract Background Determining true CDI versus CD colonization through CD testing is a continuing challenge. A previously introduced decision support tool at UVA Health significantly reduced inappropriate testing without adverse outcomes. More recently, our methodology changed from nucleic acid amplification test (NAAT) alone to an initial NAAT followed by ELISA for toxin to improve specificity. The purpose of this analysis was to assess provider interpretation of test results, using targeted CD therapy as a surrogate. Methods This single-center, retrospective study evaluated all patients with a positive NAAT (Cepheid Xpert® C. difficile) on day 4 or later of hospitalization following 2-step algorithm implementation from Feb 2020 through Feb 2021. Toxin negative (TOX-) test results (C. DIFF QUIK CHEK COMPLETE®) were accompanied by a comment that discordance may represent colonization or CDI and to consider ID consult. The proportion of toxin positive (TOX+) versus TOX- patients receiving ≥ 1 dose of CD therapy served as the primary outcome with partial courses considered &lt; 10 days. Clinical outcomes were also compared. Results Ninety patients with NAAT+ results were included, of whom 58 (64%) were TOX-. Thirty-two (100%) TOX+ (median days of therapy [IQR] = 14 [11-17]) versus 51 (88%) TOX- patients (median days of therapy [IQR] = 11 [7-14]) received CD therapy (p=0.04). Treatment decisions were guided by ID physicians for 32 (63%) TOX- patients; ID recommendations to discontinue CD therapy were followed in 2 out of 9 (22%) cases. TOX- patients received partial therapy due to patient death (n=5), presumptive colonization (n=3), and provider error (n=1). Of TOX- patients receiving partial or no treatment, there were no CDI-related adverse outcomes during the admission. CDI-related colectomy occurred in 2 (6%) and 1 (2%) TOX+ and TOX- patients, respectively. Five in-hospital deaths with CDI as a contributing factor occurred in the TOX+ group. Conclusion Adoption of a 2-step NAAT plus toxin testing algorithm for hospital-onset CDI reduced the frequency with which TOX- patients received CD therapy but the vast majority were still treated. Most providers considered a positive NAAT indicative of CDI, regardless of TOX status. Disclosures All Authors: No reported disclosures

scholarly journals Factors Affecting Health Care Engagement of Patients With End-Stage Heart Failure: An Exploratory Survey Study

2019 ◽  
Vol 4 (2) ◽  
pp. 238146831986551
Author(s):  
Lisa Carey Lohmueller ◽  
Aakanksha Naik ◽  
Luke Breitfeller ◽  
Colleen K. McIlvennan ◽  
Manreet Kanwar ◽  
...  
Keyword(s):  
Heart Failure ◽  
Health Care ◽  
Decision Making ◽  
Decision Support ◽  
Information Seeking ◽  
Decision Support Tool ◽  
Treatment Decisions ◽  
Support Tool ◽  
End Stage Heart Failure ◽  
End Stage

Background. The decision to receive a permanent left ventricular assist device (LVAD) to treat end-stage heart failure (HF) involves understanding and weighing the risks and benefits of a highly invasive treatment. The goal of this study was to characterize end-stage HF patients across parameters that may affect their decision making and to inform the development of an LVAD decision support tool. Methods. A survey of 35 end-stage HF patients at an LVAD implant hospital was performed to characterize their information-seeking habits, interaction with physicians, technology use, numeracy, and concerns about their health. Survey responses were analyzed using descriptive statistics, grounded theory method, and Bayesian network learning. Results. Most patients indicated an interest in using some type of decision support tool (roadmap of health progression: 46%, n = 16; personal prognosis: 51%, n = 18; short videos of patients telling stories of their experiences with an LVAD: 57%, n = 20). Information patients desired in a hypothetical decision support tool fell into the following topics: prognoses for health outcomes, technical information seeking, expressing emotions, and treatment decisions. Desire for understanding their condition was closely related to whether they had difficult interpreting their electronic medical record in the past. Conclusions. Most patients reported interest in engaging in their health care decision making and seeing their prognosis and electronic health record information. Patients who were less interested in their own treatment decisions were characterized by having less success understanding their health information. Design of a decision support tool for potential LVAD patients should consider a spectrum of health literacy and include information beyond the technical specifications of LVAD support.

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