Harmonized immune recovery monitoring after HCT: evidence and practical guidance from the Westhafen Intercontinental Group
Bibliographic record
Abstract
ABSTRACT: Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative option for patients with high-risk malignancies and nonmalignant disorders. Long-term survival depends on robust immune reconstitution (IR), which governs overall immune homeostasis and risks of infection, graft-versus-host disease, and relapse. However, despite its centrality to posttransplant outcomes, IR is not consistently monitored across transplant centers, limiting ability to generate meaningful, comparable, and translatable data. This review synthesizes current knowledge on numerical and functional IR milestones after allo-HCT, with a primary focus on flow cytometry-based monitoring of key immune cell subsets. Importantly, early CD4+ T-cell recovery (achieving >50 cells per μL by day 100 after transplant), is supported by strong clinical evidence and correlates with improved outcomes. Although emerging data suggest that additional subsets (CD8+ T cells, natural killer cells, B cells, naïve and recent thymic emigrant T cells, and γδ T cells) may also influence clinical trajectories, further harmonized, multicenter studies are needed to validate prognostic relevance across transplant settings. We propose practical, evidence-based guidelines for IR monitoring, including recommended time points, preferred assays, and flow cytometry panel components. Additionally, we highlight modifiable factors (eg, immunosuppressive drug exposures, graft manipulation) offering interventional opportunities for influencing IR. Harmonized monitoring strategies will support robust correlation between IR and clinical outcomes, guide real-time risk stratification, and facilitate the development of targeted, individualized transplant approaches. Standardization efforts led by consortia and registries are essential for advancing knowledge and optimizing care. We provide a roadmap for implementing uniform IR monitoring to improve outcomes and quality of life for allo-HCT recipients.
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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 | 0.045 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".