Patient Related Factors Have an Indepedent Impact on Overall Survival in Myelodysplastic Syndrome Patients: A Report of the MDS-Can Registry
Bibliographic record
Abstract
Abstract Introduction: MDS is a disease of the elderly; yet, the impact of clinical frailty (an age-related vulnerability state created by a multidimensional loss of reserves) and patient-reported outcomes on overall survival (OS) are unknown. Rockwood et al. have developed a simple 9-point clinical frailty scale (CFS) that correlated highly with the risk of death, institutionalization, worsening health and hospital use (Rockwood K., CMAJ 2005). In a prospective, national MDS registry, participants have undergone annual evaluations with the following: (a) 3 geriatric physical performance tests, (b) Charlson (CCI) and Della Porta comorbidity index (DP-CCI) scores, (c) graded frailty using the Rockwood CFS, (d) disability assessments with the Lawton Brody SIADL, and (e) QOL using the EORTC QLQ C-30 and the EQ-5D. The results of these frailty assessments and the effects of these patient-related factors and reported outcomes on OS, in addition to the IPSS/revised IPSS, will be presented. Methods: Overall survival was measured from time to enrollment. Results from physical performance tests were divided into quintiles with higher scores indicating better performance. We used univariate and multivariable Cox proportional hazard model to determine significant predictive factors of overall survival (OS). The variables considered included age, IPSS, R-IPSS, ferritin, LDH, transfusion dependence, hemoglobin (hgb), ECOG, frailty, CCI and DP-CCI, grip strength, 4 M walk test, stand-sit test, modified short physical performance battery (SPPB), Lawton Brody SIADL, time from diagnosis and selected QOL domains including the EQ-5D summary score, EORTC physical functioning, dyspnea and fatigue scores. Results: 453 MDS patients (pts) have been consented and enrolled locally since January 2008 (n=231) and nationally since January 2012 (n=222). Median time from diagnosis was 5.8 mos (IQR 1.4-21). Median age was 73 y (range, 26-95 y), 65% were male and the R-IPSS scores were very low (14%), low (46%), intermediate (24%), high (10%) and very high (6%). Thirty-three % of pts were transfusion dependent at enrollment. Median CCI and DP-CCI scores were 1 (0-12) and 0 (0-6) respectively with 18% and 24% falling into the highest category scores. Median frailty scale score (n=346) was 3 (1-9) with 25% having scores indicating moderate (4-5) or severe (6-9) frailty. The CCI and DP-CCI strongly correlated (r=0.6; p< .0001) with each other, while frailty significantly but modestly correlated with them (r=0.3-0.35, p<.0001). With a median follow up (from enrollment) of 15 mos (95% CI: 13-16), 159 (35%) pts have died and 28 pts lost to follow up. Actuarial survival was 41.0 mos (range, 33.6 - 48.5 mos). When considering patient related factors - age, frailty, comorbidity (both indices), sex, ECOG, the 10 x stand sit test, the SPPB, Lawton Brody SIADL, and all QOL domains considered above were significantly predictive of OS. The multivariable model with the highest R2 included R-IPSS (p=.0004), frailty (1-3 vs 4-9, p= .004), CCI (0-1 vs >2, p=.03) and EORTC fatigue (p=.01) as summarized in Table 1 below. A frailty score > 3 predicted for worse survival (figure 1: 2 year OS 68.5% vs. 83.8%) and further refined survival within the R-IPSS categories (Figure 2). Frailty was also the single most predictive factor for OS from the start of azacitidine therapy (not shown). Conclusions: Patient-related factors such as frailty and comorbidity (that evaluate physiologic reserve and global fitness) should be considered in addition to traditional MDS prognostic indices. Abstract 165. Table. Independent Covariate Predictive factors at baseline Coefficient SE p -value HR 95% CI of HR R2 (%) Time from diagnosis (months) * 0.0335 0.1076 0.7557 1.034 0.837 1.277 17.29% R-IPSS (5 categories) <.0001 Very high vs. very low 2.5701 0.7289 0.0004 13.066 3.131 54.524 High vs. very low 2.1156 0.6437 0.0010 8.294 2.349 29.285 Intermediate vs. very low 1.0466 0.6430 0.1036 2.848 0.808 10.043 Low vs. very low 0.6346 0.6181 0.3045 1.886 0.562 6.334 Frailty (1-3 vs. 4-9) -0.8323 0.2905 0.0042 0.435 0.246 0.769 Comorbidity Charlson (0-1 vs. ³2) -0.5915 0.2749 0.0314 0.553 0.323 0.949 EORTC fatigue * 0.3671 0.1546 0.0176 1.443 1.066 1.954 natural log-transformation was applied for normalizing distribution Figure 1 Overall survival by Frailty (n=346) Figure 1. Overall survival by Frailty (n=346) Figure 2 Overall Survival by Frailty and R-IPSS Figure 2. Overall Survival by Frailty and R-IPSS Disclosures Buckstein: Celgene Canada: Research Funding. Wells:Celgene: Honoraria, Other, Research Funding; Novartis: Honoraria, Research Funding; Alexion: Honoraria, Research Funding. Leitch:Alexion: Honoraria, Research Funding; Novartis: Honoraria, Research Funding, Speakers Bureau; Celgene: Educational Grant Other, Honoraria, Research Funding. Shamy:Celgene: Honoraria, Other.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".