Outcomes of haploidentical hematopoietic stem cell transplantation in low- and middle-income countries: A systematic review and meta-analysis.
Notice bibliographique
Résumé
Abstract Introduction: Haploidentical hematopoietic cell transplantation (haplo-HCT) has emerged as a critical alternative for patients without matched donors, particularly in the management of hematologic malignancies and select non-malignant disorders. Advances in transplant techniques and supportive care have expanded its applicability. Given these developments, evaluating real-world outcomes is essential. This systematic review summarizes current evidence on indications, transplant strategies, and survival outcomes for haplo-HCT in low- and middle-income countries (LMICs). Methods: A comprehensive literature search was conducted on PubMed, ClinicalTrials.gov, Scopus, and Web of Science for studies published up to June 2025 using keywords and MeSH terms related to ‘haploidentical stem cell transplant’, ‘LMICs’, and ‘outcomes’. Eligible studies included those reporting post-haplo-HCT outcomes from LMICs reporting haplo-HCT in the adult population. Data on baseline demographics, clinical and transplant characteristics, survival rates, graft-versus-host disease (GVHD), complications, engraftment kinetics, immune recovery, and disease relapse were extracted. The risk of bias was assessed using the modified Newcastle-Ottawa scale and the ROB2 tool. Meta-analyses were conducted in RStudio (v5.4.1) using a random-effects model. Results This review included 24 studies comprising a total of 2,248 patients who underwent haplo-HCT. The ages of patients ranged from 0-79 years and follow yup duration range from 1-83 months. Of the 24 included studies, (n = 11, 45.8%) were conducted in North America, (n = 10, 41.7%) in Asia (including India, Iran, Thailand, Lebanon, and Nepal), (n = 2, 8.3%) in South America (Brazil), and (n = 1, 4.2%) in Africa (Egypt). A total of 1,214 (63.3%) male recipients were reported across 22 studies, comprising 1,915 patients in total. ECOG performance status was reported across four studies, with the majority of patients (n = 300/341, 88.0%) having ECOG 0–2 and the remaining (n = 41/341, 12.0%) having ECOG >2. Across the 15 studies that reported disease-specific data, acute leukemia was the most common indication (n = 825/1139, 72.4%), followed by aplastic anemia (n = 59/1139, 5.2%), lymphoma (n = 51/1139, 4.5%), and myelodysplastic syndromes (n = 26/1139, 2.3%). Non-malignant conditions such as immune deficiencies, inherited anemias, and metabolic diseases each accounted for <2% of cases. Peripheral blood stem cells (PBSC) were the predominant graft source, used in (n = 21, 88%) of studies, with most employing PBSC exclusively. Myeloablative conditioning (MAC) was used in 42% while reduced intensity/non-myeloablative (RIC/NMA) in 58% of, with RIC/NMA favoring Fludarabine-based regimens and MAC using full-dose Busulfan or high-dose TBI. Post-transplant cyclophosphamide was used in 99% of patients (n = 1018/1028) across 13 studies. Pooled overall survival (OS) at 1 and 2 years was 0.52 (95% CI: 0.41–0.63; I² = 87.5%, p < 0.0001) and 0.43 (95% CI: 0.36–0.53; I² = 83.1%, p < 0.0001), respectively. The use of post-transplant cyclophosphamide (PTCy) was not associated with differences in OS at either time point compared to non-PTCy regimens (p = 0.4694). Pooled disease-free survival (DFS) was 0.63 at 1 year (95% CI: 0.50–0.75; I² = 76.6%, p = 0.0007) and 0.41 at 3 years (95% CI: 0.35–0.48; I² = 66.1%, p = 0.0070). Relapse-free survival (RFS) was 0.49 at 1 year (95% CI: 0.32–0.67; I² = 88.7%, p < 0.0001) and 0.41 at 3 years (95% CI: 0.31–0.53; I² = 0.0%, p = 0.4641). Acute GVHD incidence across 16 studies within 90–100 days was 0.37 (95% CI: 0.32–0.42; I² = 70.7%, p< 0.0001). Relapse occurred in 32.9% of patients (n = 343/1041) across 16 studies. Conclusion: Haploidentical hematopoietic cell transplantation is widely utilized in low- and middle-income countries, primarily for the treatment of acute leukemia, using peripheral blood stem cell grafts and a preference for reduced-intensity or non-myeloablative conditioning regimens over myeloablative ones. Survival outcomes remain modest and heterogeneous, emphasizing the need for standardized protocols and further research to optimize results in resource-constrained settings.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,014 | 0,027 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».