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Changes in health-related quality of life in patients with newly diagnosed acute myeloid leukemia receiving ivosidenib + azacitidine or placebo + azacitidine.

2022· article· en· W4281677430 on OpenAlexaff
Andre C. Schuh, Stéphane de Botton, Christian Récher, Susana Vives Polo, Ewa Zarzycka, Jianxiang Wang, Giambattista Bertani, Michael Heuser, Rodrigo T. Calado, Su‐Peng Yeh, Jianan Hui, Shuchi S. Pandya, Diego A. Gianolio, Christina X. Chamberlain, Hartmut Döhner, Pau Montesinos

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineAzacitidineQuality of life (healthcare)PlaceboClinical endpointInternal medicineMyeloid leukemiaClinical trialOncologyGastroenterology

Abstract

fetched live from OpenAlex

e19024 Background: Ivosidenib (IVO) is a potent, targeted inhibitor of mutant isocitrate dehydrogenase 1 (mIDH1) that is approved for acute myeloid leukemia (AML). IVO plus azacitidine (AZA) demonstrated clinical benefit compared with placebo (PBO) and AZA in the AGILE study (NCT03173248), and here we report the impact of IVO+AZA versus PBO+AZA on health-related quality of life (HRQoL). Methods: In the double-blind, PBO-controlled phase 3 AGILE study, patients (pts) were randomized 1:1 to IVO 500 mg QD + AZA 75 mg/m2 SC or IV for 7 days in 28-day cycles, or PBO+AZA. HRQoL was a secondary endpoint assessed using two validated questionnaires: the European Organisation of Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30) and the EuroQol 5-dimension 5-level questionnaire (EQ-5D-5L). Questionnaires were administered pre-dose on cycle (C) 1 Day (D) 1, on C1D15, C2D1, C2D15, and on D1 of every odd cycle thereafter until the end of treatment. Score change from baseline across visits for all subscales of EORTC QLQ-C30 was analyzed with mixed models. A 10-point threshold in EORTC QLQ-C30 subscale score was used to evaluate clinically meaningful changes from baseline or differences between arms. Two-sided nominal p-values are reported. Results: At baseline, 69 and 68 pts out of 72 receiving IVO+AZA completed the EORTC QLQ-C30 and EQ-5D-5L, respectively, and 66 pts out of 74 receiving PBO+AZA completed both. Mean baseline HRQoL scores were similar between treatment arms. There was an initial decline in HRQoL (EORTC QLQ-C30 global health status [GHS/QoL]) in both arms for ̃4 months, consistent with time to response, and which was generally not clinically meaningful. IVO+AZA was associated with preserved or improved HRQoL compared to baseline for most subscales of the EORTC QLQ-C30 from C5 to C19 (after which no PBO+AZA HRQoL data were available), and at most timepoints for EQ-5D-5L VAS scores and index values. EORTC QLQ-C30 subscales with clinically meaningful improvements from baseline at most timepoints from C5 to C19 in the IVO+AZA arm included GHS/QoL, fatigue, pain and appetite loss. In contrast, there were few clinically meaningful improvements from baseline in PBO+AZA pts. GHS/QoL scores were significantly improved (p≤0.05) for IVO+AZA versus PBO+AZA at C2D1, C2D15, C7 and C9, and differences were clinically meaningful at C2D1 (10.2 point difference), C2D15 (10.1), C7 (12.6), C9 (22.6), C13 (14.9), C15 (15.4) and C19 (19.2). Likewise, improvements in EORTC QLQ-C30 fatigue, appetite loss, nausea and vomiting, diarrhea, cognitive functioning and social functioning favored IVO+AZA over PBO+AZA at multiple timepoints. Conclusions: Data from the AGILE study show that patients with mIDH1 AML receiving treatment with IVO+AZA tended to report maintenance or improved HRQoL from cycle 5 through to cycle 19 compared with PBO+AZA. Clinical trial information: NCT03173248.

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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 head0.109
GPT teacher head0.429
Teacher spread0.320 · 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 designRandomized trial
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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Citations2
Published2022
Admission routes1
Has abstractyes

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