Prognostic Power of Chronic Gvhd Risk Score Model by Ibmtr Can Be Improved with Addition of Absolute Lymphocyte Counts and Eosinophil Counts At the Onset of Chronic Gvhd
Bibliographic record
Abstract
Abstract Abstract 4184 Background: Chronic graft-versus-host disease (cGVHD) risk score by Arora et al (Blood 2011) was successfully demonstrated to stratify patients with cGVHD according to their transplantation outcomes. In this risk score model, the following variables were included: recipient age, donor type, sex mismatch, GVHD prophylaxis, prior acute GVHD, time from HSCT to cGVHD, disease status before HSCT, platelet count at cGVHD diagnosis, serum bilirubin at cGVHD onset and performance status at diagnosis of cGVHD. However, this risk score system needs to be validated in an independent cohort. Methods: A total of 425 consecutive patients who survived beyond 100 days following allogeneic hematopoietic stem cell transplantation (HSCT) at the Princess Margaret Hospital from Jan 1996 to Oct 2007, were reviewed retrospectively and evaluated for the cGVHD risk score[J1]. Results: Out of 425 patients, 317 patients developed cGVHD according to the NIH consensus criteria of GVHD. Ten patients were removed from the analysis due to missing variables. Long-term transplant outcomes of the remaining 307 patients were evaluated using the cGVHD risk score system. First, overall survival (OS) according to the risk group (RG) were 82.5±11.3 (RG1), 76.4±3.4 (RG2), 69.4±6.5 (RG3), and 27.3±13.4 (RG4) (p<0.001), while non-relapse mortality (NRM) were 10.0±9.5 (RG1), 10.4±2.4 (RG2), 21.4±5.8 (RG3), and 69.3±14.7 (RG4) (p<0.001). Significantly lower OS and higher NRM was noted in RG4. However, there is no statistical difference of the transplant outcomes among 3 groups of RG1, RG2 and RG3 for OS and NRM. Next, in order to improve a stratification power of cGVHD risk score model, two other clinical variables were incorporated into the model, absolute lymphocyte count (ALC) and eosinophil count (EOS) at the onset of cGVHD. The patients with lower (ALC, <1.0·109/L) and lower eosinophil counts (EOS, <0.5·109/L) at the onset of cGVHD were associated with inferior OS and higher risk of NRM. In a multivariate analysis, lower ALC was confirmed to be an independent variable predicting OS (HR 1.94, 95% CI 1.14–3.28, p=0.014) and NRM (HR 2.87, 95% CI 1.35–6.08, p=0.006). Similarly, EOS was also confirmed to be an independent variable predicting OS (HR 3.27, 95% CI 1.28–8.38, p=0.014). Accordingly, these two variables (i.e. ALC and EOS) were tested in a revised cGVHD risk score model and patients were stratified into revised 4 groups: rRG1 (score 0–3), rRG2 (score 4–6), rRG3 (score 7–9), rRG4 (score °Ã10). Overall survival (OS) according to the revised risk group (rRG) were 93.3±6.4 (rRG1), 81.4±4.1 (rRG2), 68.2±4.6 (rRG3), and 32.0±1.7 (rRG4) (p<0.001). Non-relapse mortality (NRM) were 0.0 (rRG1), 7.9±2.8 (rRG2), 17.2±3.8 (rRG3), and 62.2±11.8 (rRG4) (p<0.001). The revised cGVHD-risk score appears to better discriminatepatients according to their transplant outcomes (particularly OS and NRM). Conclusion: The original cGVHD risk score model has been successfully validated to stratify transplant patients according to their risk of cGVHD[J2]. In addition, revised cGVHD risk score model including ALC and EOS at cGVHD onset improved prognostic stratification of the patients according to their transplantation outcomes. Disclosures: No relevant conflicts of interest to declare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".