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Health-related quality of life (HRQoL) with enasidenib versus conventional care regimens in older patients with late-stage mutant-<i>IDH2</i> relapsed or refractory acute myeloid leukemia (R/R AML).

2022· article· en· W4286297779 on OpenAlexaff
Courtney D. DiNardo, Pau Montesinos, Andre C. Schuh, Cristina Papayannidis, Paresh Vyas, Andrew H. Wei, Amer M. Zeidan, Clara Chen, Jennifer Lord‐Bessen, Peiwen Yu, Ling Shi, Shien Guo, Iryna Bluemmert, Xin Yu, Maroof Hasan, Patricia Martin Regueira, Stéphane de Botton

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineClinical endpointInternal medicineQuality of life (healthcare)Refractory (planetary science)IDH2Randomized controlled trialClinical trialOncologyGastroenterologyIDH1Mutant

Abstract

fetched live from OpenAlex

7032 Background: Enasidenib (ENA) is an oral inhibitor of mutant-IDH2 (mIDH2) proteins. In the phase 3 IDHENTIFY trial, ENA improved event-free survival (EFS), overall response, and complete remission rate vs conventional care regimens (CCR) ( P < 0.01 for all) in patients (pts) ≥ 60 years of age with m IDH2 R/R AML with 2 or 3 prior treatments (Tx) (DiNardo 2021). Pt-reported HRQoL was a secondary trial endpoint. Methods: IDHENTIFY is an open-label, randomized trial (NCT02577406). Pts were preselected to a CCR (SC azacitidine, intermediate- or low-dose Ara-C, or supportive care) and then randomized 1:1 to ENA 100 mg/d or CCR in 28-d cycles. Key HRQoL endpoints were mean changes from baseline (CFB) overall and by clinical response in the Global Health Status/QoL, Physical Functioning, Role Functioning, Fatigue, and Dyspnea domains of the EORTC QLQ-C30 questionnaire, and in EQ-5D-5L utility index (UI) and visual analogue scale scores. The QLQ-C30 and EQ-5D-5L were assessed on D1 of each Tx cycle (C) and at end of Tx. Minimally important differences (MIDs) in CFB scores within or between Tx arms were based on accepted thresholds. Sensitivity analysis using imputed data on CFB was conducted using pattern mixture modeling. Results: HRQoL-evaluable cohorts included 118/158 (74.7%) pts in the ENA arm and 80/161 (49.7%) in the CCR arm; 40 ENA pts and 81 CCR pts were not evaluable due to missing data at baseline (BL; 22 ENA and 51 CCR) and/or at ≥1 post-BL visit (26 ENA and 69 CCR). Pts ineligible for HRQoL analyses had lower response rates and worse EFS and overall survival than HRQoL-evaluable pts. Overall QLQ-C30 completion rates in the ENA and CCR arms were 79% and 65%, respectively ( P < 0.001). While there was no meaningful improvement or worsening from BL (ie, exceeding MID) within either Tx arm in the key QLQ-C30 domains, scores worsened during initial Tx cycles and then improved with continued Tx. Mean EQ-5D-5L scores also worsened during early cycles in both Tx arms, with meaningful UI deterioration in the ENA arm from C2 through C7. However, between-group comparisons showed no consistent differences between ENA and CCR in mean CFB. Sensitivity analysis with imputation of missing CFB data showed worsened HRQoL compared with non-imputed data in the CCR arm but not with ENA. In the ENA arm, clinical responders reported relatively stable mean HRQoL scores over time, and non-responders showed no meaningful differences in CFB vs the CCR arm. Conclusions: HRQoL measures tended to worsen during early cycles in both Tx arms and gradually improved with continued Tx. Data should be interpreted with caution as only approximately one-half of pts in the CCR arm were HRQoL-evaluable. ENA improved clinical efficacy measures vs CCR without compromising HRQoL in older pts with R/R AML. Clinical trial information: NCT02577406.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.433
Teacher spread0.339 · 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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Citations4
Published2022
Admission routes1
Has abstractyes

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