MétaCan
Menu
Back to cohort

PS1026 ENASIDENIB ASSOCIATED WITH IMPROVED SURVIVAL COMPARED WITH STANDARD OF CARE FOR R/R AML PATIENTS WITH AN IDH2 MUTATION BASED ON DATA FROM STUDY AG221‐C‐001 AND A REVIEW OF PATIENT CHARTS IN FRANCE

2019· review· en· W2952177114 on OpenAlexaff
Stéphane de Botton, Joseph Brandwein, Andrew H. Wei, Salem Abi Nehme, Milo Frattini, Alessandra Tosolini, Roland Marion‐Gallois, J.J. Wang, Chris Cameron, Mustaqeem Siddiqui, B. Hutton, Gary Milkovich, E.M. Stein

Bibliographic record

VenueHemaSphere · 2019
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOttawa HospitalUniversity of Alberta
Fundersnot available
KeywordsMedicineHazard ratioInternal medicineOncologyPropensity score matchingIDH2Hematopoietic stem cell transplantationTransplantationConfidence intervalMutationIDH1Biology

Abstract

fetched live from OpenAlex

Background: Enasidenib is approved for the treatment of adult patients with relapsed or refractory (R/R) acute myeloid leukemia (AML) with an isocitrate dehydrogenase‐2 (IDH2) mutation. Supporting data for the use of enasidenib in patients with R/R AML and an IDH2 mutation are available from the phase 1/2 AG221‐C‐001 single‐arm study (NCT0915498); direct comparative efficacy data for enasidenib are not available. Aims: To compare the overall survival (OS) associated with enasidenib 100 mg daily and standard of care (SoC) in patients with R/R AML and an IDH2 mutation not eligible for hematopoietic stem cell transplantation (HSCT). Methods: Propensity score matching (PSM) analysis was conducted using individual patient data (IPD) for patients treated with 100 mg of enasidenib daily from the phase 1/2 AG221‐C‐001 study, and IPD for real‐world patients treated with SoC from a review of patient charts at 9 French sites. Covariates selected for calculating propensity scores (PS) included history of HSCT before baseline, age, number of prior lines of AML therapy at baseline, cytogenetic risk at baseline, and history of myelodysplastic syndromes. For the primary analysis, PSM was performed using optimal 1:1 matching and excluded patients undergoing HSCT after baseline. Hazard ratios (HRs) were estimated from Cox proportional hazards models that adjusted for PS covariates in matched populations. Robustness of results was assessed by a range of sensitivity analyses including different matching algorithms and other prognostic factors. Results: Prior to performing PSM, considerable differences between patients treated with enasidenib (N = 195) and patients treated with SoC (N = 80) were observed with respect to number of prior lines of therapy, prior HSCT, and cytogenetic risk profile, as determined by higher standard mean differences (SMDs) in the pre‐matched population compared with the post‐matched population. Before PSM, patients treated with enasidenib had improved OS compared with SoC (HR 0.82; 95% confidence internal [CI] 0.61–1.11). After matching (enasidenib n = 78; SoC n = 78) and adjusting for covariates, mortality risk was significantly lower for enasidenib compared with SoC and the 95% CI excluded the value 1 (HR 0.67, 95% CI 0.47–0.97). The median survival time for enasidenib was 9.3 months (95% CI 7.7–13.2) compared with 4.8 months (95% CI 3.8–8.2) for SoC (Figure). All sensitivity analyses had mortality HR point estimates that were in favor of enasidenib and excluded 1 from their respective 95% CI in most cases. The results from the sensitivity analyses were consistent with the primary analysis. Summary/Conclusion: Patients with R/R IDH2‐mutated AML not eligible for HSCT who were treated with enasidenib on study had improved survival compared with patients receiving SoC in a real‐world setting. Future studies are needed to validate these findings using other data sources, to compare enasidenib with specific treatments and to assess the comparative efficacy of enasidenib for other outcomes. image

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.352
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueHemaSphereSame topicAcute Myeloid Leukemia ResearchFrench-language works237,207