Quality of Life Scores Improve with Increasing Hemoglobin but Optimal Thresholds Vary According to Transfusion Dependence and Clinical Risk Scores: A Canadian Cross Sectional Study of 689 Patients with 2969 Measurements
Bibliographic record
Abstract
Abstract Background : We previously presented that selected quality of life (QOL) domains in MDS patients are impaired compared with age-matched controls and most impacted by hemoglobin (Hgb) level, transfusion dependence, frailty and comorbidity in an initial cohort of 236 patients from a Canadian MDS registry (Buckstein R. et al, Abstract 699, ASH 2012 and Abstract 2500, ASH 2009). The optimal Hgb threshold associated with improved QOL may vary according to health states that may fluctuate for any given patient. With longer follow up and greater sample size, we now examine the impact of Hgb levels on QOL in transfusion dependent (TD) versus independent (TI) patients and according to IPSS-R risk scores. Methods:Since 2008, we have prospectively assessed QOL in all patients registered in the Canadian national MDS registry using the instruments EORTC QLQ-C30, FACT-F, global fatigue scale (GFS) and EQ-5D, at enrollment and every 4-6 months. These QOL data are paired with disease specific and laboratory information at the same time intervals. Each patient could provide multiple QOL measurements at different time points. Clinically significant score differences were considered 10 points for the EORTC, 0.08 for the EQ-5D and 4 for the FACT F. General linear regression analysis was applied to search for a significant relationship between physical and social functioning, dyspnea, fatigue and QOL with Hgb, according to transfusion dependence, IPSS and IPSS-R measured categorically. To account for multiple comparisons among 5 Hgb categories, Bonferroni adjusted p-value < .01 was considered statistically significant. Results: 689 patients from 15 Canadian sites completed their first QOL assessment at a median time of 7.8 (IQR 2.7-23) months from MDS diagnosis. The median time from MDS diagnosis to death or last follow-up was 2.5 years (IQR 1.2-4.9). The median Hgb at enrollment was 100 g/L (IQR 86-113) and the distribution of risk scores included: very low (13%); low (35%); intermediate (28%); high (15%); and very high (10%). 27% of patients were TD at enrollment and 54% were TD at any time. The median number of QOL assessments per patient completed was 3 (IQR 2-6) with 547 patients completing at least 2, 424 at least 3 and 335 at least 4 serial QOL measurements at a median time interval of 17 weeks (IQR 13-25). When examined by Hgb thresholds, mean physical functioning, dyspnea, fatigue (QLQ-C30 and GFS) and global QOL improved with increasing Hgb. QOL symptom and function scores were clinically and statistically significantly superior in TI versus TD patients (table 1). The optimal discriminating Hgb threshold for improved symptom and function scores was 100 g/L for patients that were TI or with IPSS-R very low, low and intermediate risk MDS; and 90 g/L for high and very high risk disease (table 2). No discriminating threshold was found in TD patients. Conclusions: In the largest reported serial cross sectional population based assessment of QOL in MDS patients, we confirm that higher Hgb and transfusion independence have significant impact on QOL, symptoms and self-reported function and should be considered important surrogate endpoints for clinical improvement. Disclosures Buckstein: Novartis: Honoraria; Celgene: Honoraria, Research Funding. Wells:Janssen: Honoraria, Other: Advisory Board; Celgene: Honoraria, Other: advisory board; Novartis: Honoraria, Other: advisory board; Alexion: Honoraria, Other: Advisory board. Zhu:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Geddes:Celgene: Other: Advisory Board, Research Funding. Sabloff:Gilead: Research Funding; Novartis Canada: Membership on an entity's Board of Directors or advisory committees; Alexion: Honoraria; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Lundbeck: Research Funding. Leber:BMS Canada: Honoraria, Research Funding, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Keating:Bayer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Storring:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Yee:Novartis Canada: Membership on an entity's Board of Directors or advisory committees, Research Funding. Leitch:Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. St-Hilaire:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis Canada: Membership on an entity's Board of Directors or advisory committees. Nevill:Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Shamy:Celgene: Honoraria, Other: Advisory board; Amgen: 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. Kumar:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees. Delage:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".