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Record W3197836939 · doi:10.1016/j.jtct.2021.08.018

Is Haploidentical Hematopoietic Cell Transplantation Using Post-Transplantation Cyclophosphamide Feasible in Sub-Saharan Africa?

2021· article· en· W3197836939 on OpenAlexaff
Justin Rudolph du Toit, Andrew McDonald, David Brittain, Michael Cass, J. G. Thomson, Jenna Oosthuizen, Cecile du Toit, Matthew D. Seftel, Vernon Louw, Estelle Verburgh

Bibliographic record

VenueTransplantation and Cellular Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity of British Columbia
FundersFogarty International CenterNational Institutes of Health
KeywordsMedicineTransplantationInternal medicineHematopoietic stem cell transplantationCyclophosphamideCumulative incidenceIncidence (geometry)SerostatusSurgeryImmunologyChemotherapy

Abstract

fetched live from OpenAlex

Identifying a suitable volunteer unrelated donor (UD) in South Africa is challenging due to the highly diverse ethnic groups and mixed-race populations in this region. Haploidentical hematopoietic cell transplantation (haploHCT) is thus an attractive procedure for patients with high-risk hematologic malignancies. This study was conducted to assess the safety and feasibility of haploHCT in South Africa. We retrospectively analyzed the outcome of 134 patients with hematologic malignancies who received unmanipulated haploHCT with post-transplantation cyclophosphamide at 2 high-volume HCT centers between 2014 and 2019. We assessed overall survival (OS), disease-free survival (DFS), nonrelapse mortality (NRM), relapse incidence (RI), and incidence of acute GVHD. The median recipient age was 44 years (range, 15 to 73 years) and the median donor age was 36 years (range, 9 to 68 years). Acute myelogenous leukemia or myelodysplastic syndrome (AML/MDS) and acute lymphoblastic leukemia (ALL) were the most common indications for haploHCT (61.2%). The European Society for Blood and Marrow Transplantation risk score was ≥5 in 44 patients (32.8%). Seventy-seven patients (57.4%) received a myeloablative conditioning regimen. The majority of patients received a sex-matched transplant (57.4%) and had peripheral blood stem cells (PBSCs) as the stem cell source (70.9%). Sixteen patients (11.9%) had an incongruent cytomegalovirus serostatus at transplantation. The median duration of follow-up was 10.8 months (range, 0.36 to 70.8 months). OS was 56% (95% confidence interval [CI], 47% to 64%) at 1 year and 37% (95% CI, 28% to 47%) at 3 years. DFS was 47% (95% CI, 38% to 55%) at 1 year and 32% (95% CI, 24% to 41%) at 3 years. The 100-day and 3-year cumulative incidence of NRM was 18% (95% CI, 11% to 25%) and 41% (95% CI, 32% to 50%), respectively, and the 1- and 3-year cumulative RI was 16% (95% CI, 11% to 24%) and 21% (95% CI, 14% to 29%), respectively. The 1-year OS was 55% (95% CI, 40% to 67%) for the patients with AML/MDS versus 41% (95% CI, 21% to 60%) for those with ALL. Forty-five patients (41.7%) developed acute GVHD by day +100; of these, 80% had grade I-II disease. Fifty patients (37.5%) developed cytomegalovirus infection that required therapy. On multivariable analysis, older donor age was an independent risk factor for lower DFS. RI was higher for diagnoses other than acute leukemia/MDS (relative risk [RR], 2.62; 95% CI, 1.12 to 6.15; P = .027), decreased for PBSC versus bone marrow (RR, 0.43; 95% CI, 0.19 to 0.95; P = .038) and decreased for offspring donors (RR, 0.25; 95% CI, 0.09 to 0.67; P = .006). These data support the feasibility of haploHCT and suggest that unmanipulated haploHCT using a younger parent or offspring donor is a viable option for adults in sub-Saharan Africa with acute leukemia and MDS who lack a suitable related or unrelated donor.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.028
GPT teacher head0.268
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractno

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