Long-Term Organ Function After HCT for SCD: A Report From the Sickle Cell Transplant Advocacy and Research Alliance
Bibliographic record
Abstract
Hematopoietic cell transplantation (HCT) is an established cure for sickle cell disease (SCD) supported by long-term survival, but long-term organ function data are lacking. We sought to describe organ function and assess predictors for dysfunction in a retrospective cohort (n = 247) through the Sickle cell Transplant Advocacy and Research alliance. Patients with <1-year follow-up or graft rejection/second HCT were excluded. Organ function data were collected from last follow-up. Primary measures were organ function, comparing pre- and post-HCT. Bivariable and multivariable analyses were performed for predictors of dysfunction. Median age at HCT was 9.4 years; the majority had HbSS (88.2%) and severe clinical phenotype (65.4%). Most received matched related (76.9%) bone marrow (83.3%) with myeloablative conditioning (MAC; 57.1%). Acute and chronic graft-versus-host disease (GVHD) developed in 24.0% and 24.8%. Thirteen patients (5.3%) died ≥1 year after HCT, primarily from GVHD or infection. More post-HCT patients had low ejection or shortening fractions than pre-HCT (0.6% → 6.0%, P = .007 and 0% → 4.6%, P = .003). The proportion with lung disease remained stable. Eight patients (3.2%) had overt stroke; most had normal (28.3%) or stable (50.3%) brain magnetic resonance imaging. On multivariable analysis, cardiac dysfunction was associated with MAC (odds ratio [OR] = 2.71; 95% confidence interval [CI], 1.09-6.77; P = .033) and severe acute GVHD (OR = 2.41; 95% CI, 1.04-5.62; P = .041). Neurologic events were associated with central nervous system indication (OR = 2.88; 95% CI, 2.00-4.12; P < .001). Overall organ dysfunction was associated with age ≥16 years (OR = 2.26; 95% CI, 1.35-3.78; P = .002) and clinically severe disease (OR = 1.64; 95% CI, 1.02-2.63; P = .043). In conclusion, our results support consideration of HCT at younger age and use of less intense conditioning.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| 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".