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Record W4405046497 · doi:10.1182/blood-2024-206153

Multicenter Survey on the Practice Patterns of Post-Transplant Maintenance Treatment in FLT3-ITD Acute Myeloid Leukemia and Philadelphia Positive Acute Lymphoblastic Leukemia: What Is the Common Institutional Policy for Duration of Therapy?

2024· article· en· W4405046497 on OpenAlexaffabout
Alejandro Garcia‐Horton, Imran Ahmad, Kylie Lepic, Christopher J. Lemieux, David Sanford, Mohamed Elemary, Kareem Jamani, Mohsen Al Zahrani, Muhned Alhumaid, Ahmad S. Alotaibi, Ayman Saad, Ahmad Alhuraiji, Murtadha Al‐Khabori, Ali Bazarbachi, Robert Zeiser, Varun Mehra, Jae-Sook Ahn, Hyeoung‐Joon Kim, Joon Ho Moon, Sang Kyun Sohn, Yu Cai, Xianmin Song, Xiao Jun Huang, Yishan Ye, He Huang, Hee‐Je Kim, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of CalgarySaskatchewan Cancer AgencyVancouver General HospitalUniversity of British ColumbiaUniversity of SaskatchewanUniversité LavalJuravinski HospitalMcMaster UniversityHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineLymphoblastic LeukemiaMyeloid leukemiaInternal medicineLeukemiaChemotherapy regimenOncologyOverall survival

Abstract

fetched live from OpenAlex

Introduction: Since leukemia relapse remains a significant cause of mortality after allogeneic stem cell transplant (HCT) for acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), interest in post-transplant maintenance (PTM) strategies continues to increase. Specifically, evidence of outcome improvement with the FLT3 inhibitors (FLT3i) sorafenib and gilteritinib in FLT3-ITD AML continues to emerge. Similarly, in Philadelphia positive (Ph+) ALL, tyrosine kinase inhibitor (TKI) maintenance prophylactic or pre-emptive approaches are widely recommended to prevent disease relapse. However, worldwide clinical practices of PTM strategies are not standardized and significantly influenced by drug availability, funding, and healthcare systems of individual countries. Thus, we conducted a worldwide survey amongst HCT centers (n=21) from 9 countries to understand international PTM strategy usage. Methods: We implemented a worldwide survey amongst 21 HCT centers to understand international patterns of clinical practice toward PTM in patients who received allogeneic HCT for FLT3-ITD AML or Ph+ ALL. Thirty-eight centers received the survey, and of these, 21 (55%) replied. Respondent centers were located in Canada (n=7), Saudi Arabia (n=3), China (n=3), South Korea (n=3), Germany (n=1), Lebanon (n=1), Oman (n=1), Kuwait (n=1), and the United Kingdom (n=1). Results: For patients with FLT3-ITD AML, 81% (n=17/21) of centers used a FLT3i (either midostaurin, sorafenib, or gilteritinib) as PTM strategy, with 4 centers not having access to medication. The choice of FLT3i varied depending on access and funding of drugs. Of the centers with capacity to provide a FLT3i, 75-100% of candidates would go on to receive a FLT3i, while only 2 centres had a compliance of 5-50%. The duration of FLT3i PTM practices were mostly standard with all but 3 centers aiming to use the medication for 2 years. Two centers used FLT3i PTM for up to 1 year and another center continued it until disease progression. The most common FLT3 inhibitors used were sorafenib and gilteritinib, with midostaurin only used in 2 centers. FLT3-ITD mutation status was determined by NGS or PCR at diagnosis, with heterogeneous access to allele frequency/ratio, with this not being a contributing factor in the decision to use or not maintenance FLT3i. For patients with Ph+ ALL, 71% (n=15/21) of centers used a TKI as PTM for prophylaxis. Four centers did not have access to medication and 2 centres would only use TKI if measurable residual disease was detected by PCR (pre-emptive strategy). Of the centres that used TKI routinely as a PTM, prescribing compliance was reported to range from 75-100%. Duration of TKI PTM was heterogenous, with 8 centers keeping the approach for 2 years post transplant, 3 centers for 5 years, 1 center for 3 years, 2 centers for 1 year, and one until disease progression. Monitoring practices by quantitative PCR were also variable. The most common approach (12 centers) was PCR monitoring every 3 months for 2 years. Four centers monitored monthly initially, with de-escalation after 1 year or negative PCR to every 3 months. One center monitored every 6 months. Conclusion: While there is no standardized recommendation in PTM using FLT3i or TKI for FLT3-ITD AML or Ph ALL, the present study revealed that there is some common practice for PTM approach. Although the duration of maintenance still remains variable and an area of ongoing research, most of HCT centers around the world are currently employing a 2-year duration both in FLT3-ITD AML and Ph ALL. However, worldwide PTM practices still remain heterogeneous, seemingly driven by medication access rather than physician-driven decision. It would highlight urgent need for the development of international standardized treatment guidelines for PTM which can support approval of medication reimbursement or financial support for this approach. About 80% of the centers that participated in this survey consistently adopted post-transplant maintenance using FLT3i or TKI for FLT3-ITD AML or Ph+ ALL, respectively. For monitoring strategies, especially in Ph+ ALL, qPCR is employed at slightly different schedule with some variation for frequency. In FLT3-ITD, most centres have access to mutational data through PCR or NGS from initial diagnosis, but not employed for follow up or MRD assessment, which is a matter of future research development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.302
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes2
Has abstractyes

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