Transfusion Dependence and Low Hemoglobin Have the Greatest Impact On Quality of Life (QOL) in MDS Patients - a Tertiary Care Cross Sectional and Longitudinal Study.
Bibliographic record
Abstract
Abstract Abstract 2500 Poster Board II-477 Background: There are few prospective longitudinal data evaluating QOL in MDS, a disease characterized by chronic anemia and transfusion dependence in many patients. Furthermore, the effects of various drug therapies on QOL are little known but essential for cost-effectiveness studies. We have been conducting prospective assessments of QOL in all consenting patients registered in our MDS clinic using the instruments EORTC QLQ-C30, FACT-An/Fatigue, EQ5D and a global fatigue scale. We present cross-sectional results in the first 93 patients evaluated prospectively over 14.7 months. Methods: We analyzed QOL according to age, hemoglobin, IPSS risk group, transfusion dependence (Y/N), drug therapy and ferritin. Changes in QOL in the 64 (67%) patients with repeat assessments at a median time of 3 months were also examined. We examined the correlation between QOL scores using Spearman's correlation coefficient. We compared raw scores for clinically significant differences between MDS patients and normative data. Clinically significant (CS) score differences were considered 10 points for the EORTC, 7 for the FACT-An, and 4 for the FACT-Fatigue. Statistical significance (SS) using p<0.05 was determined using logistic and linear regression analysis to compare QOL scores adjusting for up to 3 additional confounding variables and non-parametric tests were used to compare risk groups (e.g., ferritin > 1000 vs. =< 1000 ug/L) on QOL scores without adjustment for confounders. Results: The median age was 71 y, with 59% males. Of the 89 patients with measurable IPSS scores, 84% fell into low/low intermediate risk categories and 7% had del 5q abnormality. 40% were transfusion dependent, 20% were receiving lenalidomide, 21% iron chelation and 21% growth factors. 50% had a Hgb of <100 g/L at time of study and the median ferritin was 837 ug/L. Comparing MDS QOL scores with normative scores from the general population, we observed SS and CS differences in the following 9 scales: worse physical, role, emotional, cognitive and social functioning; worse global QOL; increased fatigue, nausea, vomiting and pain on the QLQ-C30 function and symptoms scales; and increased fatigue on the FACT-Fatigue subscale. By linear regression analysis of QOL scores, transfusion dependence and Hgb level < 100 were the most powerful and independent predictors for impaired global health status (p=.03 and .01) but transfusion dependence was the most significant predictor of global fatigue (p=.012), impaired physical functioning (p=.002), impaired social functioning (p=.001), global health status (p=.03), and financial problems (p=.001). Increasing age was independently predictive of impaired physical functioning and appetite loss. QOL scores did not differ significantly between patients on or off lenalidomide, growth factors or iron chelation however, patients on lenalidomide who were transfusion independent had CS improved physical, emotional and social functioning and global health status /QOL compared with those who remained transfusion dependent. Eleven of 63 patients who had repeated QOL assessments increased their Hgb to >100 g/L and demonstrated increased global health status QOL (p=.03). Finally, the visual analogue scores (derived) from the single-item global fatigue and the EQ-5D health state scales correlated very strongly with virtually every symptom and functional domain of the QLQ-C30 as did the FACT-An and EQ-5D. The UK converted EQ-5D summary state utilities /self-reported scores and EORTC QLQ-C30 global health scores are in the table below. Conclusions: Most domains of QOL are impaired and symptoms of fatigue and dyspnea are increased in MDS and most dependent on transfusion dependence and Hgb, particularly if the Hgb is <100 g/L. Simple numerical rating scales for global health and fatigue may be convenient screening tools to employ in the clinic for QOL assessment. Utility scores derived from longitudinal QOL data in MDS patients treated with different agents are both feasible and essential to calculate QALYs in this era of cost constraints and pharmaco-economic modeling. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".