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The Effects of Azacitidine On Quality of Life: A Prospective Longitudinal Assessment

2012· article· en· W2980132597 on OpenAlexaff
Eric Tseng, Richard A. Wells, Shabbir M.H. Alibhai, Adam Lam, Alex Mamedov, Lisa Chodirker, Liying Zhang, Dina Khalaf, Anca Prica, Martha Lenis, Rena Buckstein

Bibliographic record

VenueBlood · 2012
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsUniversity Health NetworkHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Repeated measures designProspective cohort studyPhysical therapyRandomized controlled trialAzacitidineAnalysis of varianceRandomizationLongitudinal studyCovariateInternal medicineStatistics

Abstract

fetched live from OpenAlex

Abstract Abstract 4938 The effect of azacitidine (AZA) on health related quality of life (QOL) compared with best supportive care in MDS patients has been evaluated by Kornblith A. et al (JCO 2002) in a prospective randomized CALGB trial. The study demonstrated improved fatigue, dyspnea, physical functioning and psychological state in patients receiving AZA. One limitation is the relatively short follow up (last QOL measured at 8. 6 months) and the lack of validation in a non-trial setting. We have been conducting prospective assessments of QOL in all patients registered at our MDS clinic using the instruments EORTC QLQ-C30, FACT-Fatigue, EQ-5D and a global fatigue scale. We present longitudinal data on 56 registered patients registered in our program who were treated with AZA, 50 with serial QOLs. Methods: We examined and compared QOL scores at AZA start (baseline) and over time in all patients. We considered the following co-variates' potential impact on QOL scores: age, sex, IPSS, time from diagnosis, being a responder, # cycles and transfusion dependence. We used univariate linear regression analysis for continuous variables and analysis of variance (ANOVA) for categorical variables to determine their relationship with QOL scores at baseline and over time. For time-dependent covariates, linear mixed model was performed with random intercept and unstructured covariance matrix. Clinically significant (CS) score differences were considered 10 points for the QLQ-C30, 4 for the FACT-Fatigue and 0. 08 for the EQ-5D (7 for the visual analog score). The impact of baseline covariates on the QOL scores for global health, fatigue and dyspnea were determined by backward selection procedure of regression analysis using the Bonferroni adjusted P value of <0. 01 for multiple comparisons. Patients provided informed consent for this REB approved study. Results: 56 MDS patients consented to our registry have been treated with AZA between Oct 2008 and July 2012. The median age was 72, 60% were male and 77% had int-2/high risk IPSS MDS. 64 % were transfusion dependent (TD) at baseline. With a median time to death or last follow up of 16 months (range 1. 5–45) a median of 11 cycles of AZA were administered with 34% remaining on drug for a median of 25 cycles (IQR 8–32). The overall response rate (ORR) was 62%: 25% CR; 5% MCR; 4% PR; 28% HI. Stable disease (SD) was seen in 26% and not considered a response. 53% became transfusion independent (TI). 59% have died and 61% developed leukemia or progressed to > 30% blasts at a median time of 13 months. Overall survival was 18 months (95% CI 14. 5–26). 50 were evaluable for HrQOL with a median time between each serial QOL of 13 weeks (IQR 10–18). QOL was assessed at baseline (within 12 weeks pre-AZA start) in 50 patients, 2x in 44, 3x in 32, 4x in 22 and 5x in 14 patients (exceeding 52 weeks follow up). Looking at all 50 patients, overall, function and symptom domains remained stable over time in all instruments. At baseline, there were no statistically significant differences in QOL scores between responders and non responders. Nevertheless, clinically important differences were seen in physical, role, cognitive and social functioning, global health status (all higher in responders). Assessing QOL changes over time and considering baseline and time-dependent predictive factors in multivariate analysis, responders had significantly superior global health status (p=. 001) and EQ-5D scores (p=. 0002) and lower levels of fatigue (p<. 0001). If transfusion dependence status at time of QOL was included in the model, this often supplanted response as predictive of higher scores over time, likely representing the strong relationship between response and transfusion dependence. Conclusions: In addition to validating the clinical outcomes of AZA-001 study, we validate the importance of clinical response on QOL in MDS patients treated with AZA. Unlike the CALGB study, we observed relative stability in global health status and fatigue scores in responding patients and overall declines in non-responding patients. The higher scores at baseline in the patients destined for response raises the intriguing possibility that QOL at AZA start may be independently predictive of response perhaps because of improved tolerability and ability to remain on drug for the requisite number of cycles to achieve response. Disclosures: Wells: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Buckstein:Celgene: Honoraria, Research Funding.

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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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.319
Teacher spread0.299 · 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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Citations1
Published2012
Admission routes1
Has abstractyes

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