| Literature DB >> 28404929 |
Xiao Jun Huang1, Kaiyan Liu1, David Ritchie2, Borje Andersson3, Jin Lu1, Jian Hou4, Adolfo de la Fuente Burguera5, JianXiang Wang6, Allen Yeoh7, Chenhua Yan1, Daobin Zhou8, Daryl Tan9, Dong Wook Kim8, Depei Wu10, Elizabeth Shpall3, Stephen Kornblau3, Sattava Neelapu3, Suradej Hongeng11, Jianyong Li12, Jiong Hu13, Lian Sheng Zhang14, Michael Wang3, Pankaj Malhotra15, Qian Jiang1, Yazhen Qin1, Raymond Wong16, Richard Champlin3, Frederick Hagemeister3, Jason Westin3, Swaminathan Iyer17, Vikram Mathews18, Yu Wang1, Yu Hu19, Zhijian Xiao20, Zonghong Shao21, Robert Z Orlowski3, Chor Sang Chim22, Stephen Mulligan23, Miguel Sanz24, Keiya Ozawa25, Simrit Parmar3, Surapol Issaragrisil26.
Abstract
This report serves as a snapshot of the state-of-knowledge in the Asia Pacific (APAC) Hematology Oncology community, and establishes a baseline for longitudinal investigations to follow changes in best practices over time. The objective of this study was to understand the approach to hematologic diseases, common standards of care and best practices, issues that remain controversial or debated, and educational or resource gaps that warrant attention. We used mobile application to disseminate and distribute questionnaires to delegates during the 6th international hematologic malignancies conference hosted by the APAC Hematology Consortium at Beijing, China. User responses were collected in an anonymous fashion. We report survey results in two ways: the overall responses, and responses as stratified between Chinese physicians and "Other" represented nationalities. Overall geographical concordance in survey responses was positive and strong. Perhaps more interesting than instances of absolute agreement, these data provide a unique opportunity to identify topics in which physician knowledge or opinions diverge. We assigned questions from all modules to broad categories of: patient information; diagnosis; treatment preference; transplantation; and general knowledge/opinion. On average, we observed a geographic difference of 15% for any particular answer choice, and this was fairly constant across survey modules. These results reveal utility and need for widespread and ongoing initiatives to assess knowledge and provide evidence-based education in real time. The data will be made more valuable by longitudinal participation, such that we can monitor changes in the state of the art over time.Entities:
Keywords: Asia; hematology; leukemia; lymphoma; myeloma
Mesh:
Year: 2017 PMID: 28404929 PMCID: PMC5522281 DOI: 10.18632/oncotarget.15655
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
BTG2015 survey module participation
| Survey Module | Number of questions | Average response rate | Number of respondents / question | ||
|---|---|---|---|---|---|
| Overall | China | Other | |||
| Acute Lymphocytic Leukemia | 13 | 87% | 60 - 69 | 46 - 54 | 14 - 15 |
| Acute Myeloid Leukemia | 16 | 83% | 84 - 105 | 68 - 86 | 15 - 19 |
| Aplastic Anemia | 12 | 88% | 52 - 62 | 39 - 49 | 13 |
| Cell Therapy | 5 | 91% | 59 - 62 | 46 - 49 | 12 - 13 |
| Chronic Lymphocytic Leukemia | 2 | 94% | 36 - 37 | 21 - 22 | 15 |
| Chronic Myeloid Leukemia | 11 | 93% | 53 - 58 | 41 - 46 | 12 |
| Donor Selection | 9 | 87% | 60 - 68 | 45 - 53 | 14 - 15 |
| Hodgkin's Lymphoma | 6 | 90% | 68 - 74 | 50 - 55 | 17 - 19 |
| Non-Hodgkin's Lymphoma (part 1) | 7 | 94% | 63 - 67 | 47 - 51 | 16 |
| Non-Hodgkin's Lymphoma (part 2) | 7 | 66% | 29 - 38 | 17 - 23 | 12 - 15 |
| Multiple Myeloma | 25 | 83% | 53 - 76 | 40 - 61 | 13 - 15 |
| Myelo- dysplastic/proliferative Neoplasms | 12 | 86% | 46 - 67 | 33 - 53 | 13 - 14 |
| Thalassemia | 6 | 87% | 56 - 62 | 44 - 49 | 12 - 13 |
Figure 1Meta analyses revealed varying levels of concordance between survey responses, depending on the national origin of the respondent (A), general category of question (B), or disease setting/topic (C)
A. We plotted the percentage of responses by China (x-axis) and “Other” (y-axis) physicians for each answer choice across all survey questions (crosses), and calculated a linear best fit (R2, coefficient of determination, black line) as a measure of agreement between the geographic groups. A theoretical perfect 1:1 fit is represented by the green line. B. Each question was grouped into one of five general categories (x-axis), and the largest overall percent response among the answer choices captured as a proxy for the degree of consensus or unanimity. The average maximum response (or degree of consensus) is shown +/- 1 standard deviation. C. Numerous metrics were tabulated to describe the level of similarity between the stratified China and “Other” groups within and between each survey module (x-axis). The left vertical axis reports the percent of responses. The purple line indicates the percent of answer choices that were ranked in the same order between groups. The blue line represents the mean difference in percent response between China and Other for each answer choice, with 95% confidence intervals shaded light blue, and the absolute range in percent differences shaded grey. The linear best fit (R2) for each module was calculated as in (A), shown in green on the right vertical axis. CLL is an outlier due to only 2 questions, one of which was heavily skewed.
Figure 2Physicians report different treatment preferences for acute lymphocytic leukemia (ALL, A) as well as for chronic myeloid leukemia (CML, B.)
Here we display the results regarding the use of tyrosine kinase inhibitors imatinib, dasatinib and nilotinib. Each question is summarized to the left of the graph, with each answer choice immediately adjacent to the y-axis. As indicated in the legend above the figure, the percent of overall respondents (x-axis) that selected a particular answer choice is represented by a black bar. China and Other sub-groups are shown as squares and circles, respectively. Each question/answer set is separated by gray shading on the chart. Physicians reported treatment preferences for different scenarios regarding Hodgkin lymphoma (HL, C), as well as for non-Hodgkin lymphoma (NHL, D). C. The use of brentuximab vedotin (BV) varied depending on HL conditions among the two groups of respondents. Each question is summarized to the left of the graph, with each answer choice immediately adjacent to the y-axis. As indicated in the legend above the figure, the percent of overall respondents (x-axis) that selected a particular answer choice is represented by a black bar. China and Other sub-groups are shown as squares and circles, respectively. Each question/answer set is separated by gray shading on the chart. D. The China and Other groups’ intent to use ibrutinib varied between mantle cell and large cell lymphoma. Each question is provided to the left of the chart. The percent of physicians responding “Yes” (x-axis) is shown to the left of the vertical axis, while the “No” responses are shown to the right. Responses belonging to the geographic groups are labeled accordingly. E. Physicians from China were more likely to select stem cell transplant (SCT) from the multiple answer choices than were physicians in the Other group. The percent of respondents selecting each SCT answer choice are plotted with China on the y-axis and Other on the X-axis. The dotted line represents a 1:1 correspondence. The solid line represents the linear best-fit of the data, which is shifted upwards about 15 percentage points.