Impact of Age on Hospitalization and Readmission on Post Allogeneic Stem Cell Transplantation Outcome, Single Center Experience
Bibliographic record
Abstract
Abstract Background: Recent advances and improvement of supportive care allowed allogeneic stem cell transplantation (HCT) to be offered to selected older patients. However, data regarding outcome and factors affecting the outcomes are limited. Method: We retrospective analyzed the outcome in 332 patients, median age 65 years (60-76), who underwent HLA-matched related (n=85), matched unrelated (n=205) and haploidentical donor (n=42) HCT, between January 2014 to December 2019. Of these 60% were male. Diagnosis was leukemia: 193, MDS: 76, MF: 46 and others: 17. Graft source was PBSC in 98%. Reduce-intensity conditioning regimen was used in 95%, and in vivo T-cell depleted in 89% of patients. We categorized them to 3 age-groups (G): G1 60-65y, (n=175), G2 >65-70y (n=127), and G3 >70y (n=30).Cox models were used to compare the rates of overall survival (OS), non-relapse mortality( NRM), event free-survival (EFS), length of hospitalization for HCT, GVHD and reasons of re-hospitalization during the first year post HCT. Results: The median follow up was 14 months (range: 1-123 months). Median days of hospitalization during HCT period were 30-days (range: 20-132 days), with trend towards significance when stratified by age group (p=0.049). HCT-CI scores were 0-1 (n=143), 2-3 (n=107) and >3 (n=70). The cumulative incidences of grade II-IV acute-GVHD was 38.3% and 16.3% for grades III-IV. Moderate-severe chronic-GVHD was 23.7%. Increasing age was not associated with increases in acute GVHD (p=0.86) or chronic-GVHD (p= 0.6). Overall, 188 (56%) patients were re-hospitalized within the first 6-month of HCT, and 61 (18%) in the second 6-month period. The 2-year OS rate (Fig 1) were 56% in G1, 53% in G2 and 34% in G3 (p=0.05). The 2-year EFS rate (Fig 2) were 54% for G1, 49% for G2, and 31% for G3 (P=0.04). Cumulative incidence of NRM at 2-year (Fig 3) were 25% in G1, 36% in G2 and 52% in G3 (p=0.008). Further results are illustrated in Table 1. Risk factors such as age, KPS, HCT-CI, donor-type, readmission and GVHD were analyzed for their associations with outcomes using univariate analyses, those with significant results entered in multivariate-analysis Table 2. Patients aged 60-≤65 had significantly better EFS (p=0.04) and associated with a border line significant trend for lower NRM (p=0.05) than those aged >70. Re-admission in the first 6-month post HCT had a significant impact on the OS, EFS and NRM. HCT-CI >3 had significant impact on NRM. Conclusion: Age had a significant impact on hospitalization period during HCT. Age >70 had significant impact on EFS and trend toward higher NRM. HCT-CI, acute and chronic-GVHD and readmission in first 6-month post-HCT were significant risk factors. Readmission in the first 6 months correlated with lower OS, EFS and higher NRM. Acute GVHD III-IV or moderate-severe chronic GVHD associated with poor outcomes. Selecting patients based on HCT-CI, and good management of GVHD and post-HCT complication may improve the clinical outcome. Figure 1 Figure 1. Disclosures Law: Novartis: Consultancy; Actinium Pharmaceuticals: Research Funding. Kim: Bristol-Meier Squibb: Research Funding; Pfizer: Honoraria; Paladin: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Lipton: Bristol Myers Squibb, Ariad, Pfizer, Novartis: Consultancy, 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".