Optimizing Patient Selection for Allogeneic Stem Cell Transplantation in Chronic Myeloid Leukemia.
Bibliographic record
Abstract
Abstract Abstract 3392 Poster Board III-280 Although imatinib has replaced allogeneic stem cell transplantation (SCT) as the first treatment option for CML in first chronic phase (CP1), approximately 35% of patients do not obtain long-term benefit. As only 40-50% of these patients can be salvaged with second-generation tyrosine kinase inhibitors there is considerable debate about use of SCT as second line therapy. It is therefore important to identify groups of patients who would have a good outcome after SCT so that it may be offered after imatinib failure. In a previous analysis we identified the pre-conditioning level of C-reactive protein (CRP) as a prognostic factor for the outcome of SCT in CML. In this study, we investigated the prognostic value of comorbidities together with CRP at the time of myeloablative SCT, for patients with CML in CP1. Clinical data on 312 consecutive patients who underwent SCT between January 1991 and July 2008 was reviewed; 41 patients with incomplete data were omitted from the analysis. The median age of 271 analyzed patients was 34.3 years (range 9.7 – 59.6). 256 (94.5%) patients received bone marrow (BM) and 15 (5.5%) patients received peripheral blood stem cells (PBSC). Conditioning consisted of cyclophosphamide and TBI for 130 (48%) recipients of sibling stem cells. In addition in vivo T cell depletion with anti CD52 antibody (Campath 1H) was used for 141 (52%) unrelated donor transplants. Comorbidities were defined and assigned different weights (1-3) by the hematopoietic cell transplantation comorbidity index (HCT-CI; Sorror, ML et al, Blood 2005). HCT-CI scores were calculated for each patient stratifying them into low risk (LR, no comorbidities, HCT-CI =0), intermediate risk (IR, HCT-CI 1 or 2) and high risk (HR, HCT-CI ≥3) groups and were evaluated for their effects on transplant related mortality (TRM) and overall survival (OS). In multivariate analysis the HCT-CI score failed to predict OS or TRM at 1 year. The only significant difference was in TRM at 100 days between the patients with HCT-CI =0 (LR group) and patients with HCT-CI ≥1 (IR and HR groups; relative risk (RR) 4.0; 95% CI: 1.4 – 11.6). The absolute number of comorbidities (CMn) was a better prognostic indicator for day 100 TRM then the weighted HCT-CI (CMn=1, RR: 3.1, CI 1.3-7.3; CMn ≥1, RR: 4.9, CI: 1.7-14.1). Pre-conditioning CRP was predictive for both TRM and survival and was independent of CMn. We thus combined these two parameters into a new risk assessment tool (CRP/CMn), with 3 prognostic groups: LR (no comorbidities and CRP <2 mg/L), HR (1 comorbidity and CRP >10 mg/L or >1 comorbidity) and IR (remaining patients). These groups yielded probabilities of OS at 10 years of 81% (N=61, LR), 63% (N=159, IR) and 38% (N=25, HR; p=0.0001, Figure 1). When adjusted for patient age, duration of disease pre-SCT, donor type and patient/donor gender mismatch in a multivariate analysis, the relative risk of treatment failure (death) was 5.3 (95% CI: 2.5 – 11.5) for the HR group, and 2.3 (95% CI: 1.2 – 4.4) for the IR group when compared to the LR group. Fig 1 Probability of survival by CRP/CMn group Fig 1. Probability of survival by CRP/CMn group In our single center cohort of patients with CML in CP1 the HCT-CI was a poor indicator of prognosis post SCT. However, by removing the weighted scores and combining absolute number of co-morbidities with pre-conditioning CRP levels, we identified a new risk assessment tool that helps to select patients with CML in CP1 who could benefit from SCT as second line therapy. 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".