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Record W2922339619 · doi:10.1182/blood-2018-99-113938

High Post-Transplant Ferritin Level Associates with Adverse Survival and Higher Mortality Rate following Allogeneic Hematopoietic Stem Cell Transplant, whereas Pre-Transplant Ferritin Level Does Not

2018· article· en· W2922339619 on OpenAlexaffabout
Warren Fingrut, Arjun Law, Wilson Lam, Fotios V. Michelis, Santhosh Thyagu, Auro Viswabandya, Rajat Kumar, Jeffrey H. Lipton, Hans A. Messner, Dennis Dong Hwan Kim

Bibliographic record

VenueBlood · 2018
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineInternal medicineFerritinTransplantationHematopoietic stem cell transplantationHazard ratioCumulative incidenceGraft-versus-host diseaseProportional hazards modelGastroenterologyOncologyImmunologyConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background: Allogeneic hematopoietic stem cell transplantation (HCT) is a curative therapy for many hematologic and non-hematologic diseases; however, it is associated with significant morbidity and mortality. Many studies have shown that elevated pre-transplant ferritin levels are associated with increased risk of morbidity and mortality following HCT. Given ferritin's association with systemic inflammation, it is a plausible potential prognostic marker in the post-HCT period, including in patients with graft-versus-host disease (GVHD). The present study attempts to determine whether post-transplant serum ferritin level is an independent prognostic marker for outcomes post-HCT, especially in the subgroups of patients with acute or chronic GVHD. Methods and patients: A total of 229 patients who had serum ferritin level data available within one-year post HCT were enrolled into this retrospective study. These patients underwent HCT at Princess Margaret Cancer Centre, Toronto, Canada between 2001-2013. Patients' characteristics and transplant procedures were: median age: 51 (range:19-70); refined disease risk index: low (n=70), intermediate (n=108), high (n=51); donor type: HLA-matched related donors (n=94), HLA-matched or partially-matched unrelated donors (n=135); conditioning regimen: myeloablative (n=120), reduced intensity (n=109). Acute and chronic GVHD were respectively classified using the 1994 consensus conference and NIH consensus criteria. Primary outcomes were overall survival (OS), non-relapse mortality (NRM), and relapse. Kaplan-Meier method was used for OS; cumulative incidences were used for NRM and relapse, considering competing risks. Multivariate analysis employed Cox or Fine-Gray proportional hazard regression model. Factors evaluated included recipient age and CMV status, donor-recipient sex, comorbidity score, performance index, GVHD prophylaxis, post-transplant serum ferritin level (ferritinPost-HCT), and serum ferritin level prior to HCT (ferritinPre-HCT). Recursive partitioning was used to stratify patients into low or high risk groups based on ferritinPost-HCT (≤3,169 vs >3,169 ng/mL) and ferritinPre-HCT (≤669 vs >669 ng/mL). Results: Median follow-up duration among survivors was 46 months post-HCT (range:2.8-147.2 months). Median ferritinPost-HCT was 2,178 ng/mL (range: 45-30,647; Figure A), measured at a median of 137 days post-HCT. There was a weak correlation between ferritinPost-HCT and ferritinPre-HCT (Pearson's correlation r=0.242, p=0.002). Compared to the group with low ferritinPost-HCT, the high ferritinPost-HCT group had lower three-year OS (40.0% vs 66.7%, p<0.001; Figure B) and higher NRM (48.6% vs 17.8%, p<0.001 Figure C), but no difference in cumulative incidence of relapse (10.5% vs 19.7%, p=0.079; Figure D). A landmark analysis was performed where the day of ferritinpost-HCT measurement was considered day 0. Results of this landmark analysis showed consistent findings: high ferritinPost-HCT showed lower three-year OS (38.3% vs 65.4%; p<0.001) and higher NRM (50.6% vs 17.7%; p<0.001) compared to low ferritinPost-HCT, but no difference in relapse (10.7% vs 21.3%, p=0.077). In multivariate analysis, ferritinPost-HCT was found to be an independent prognostic factor for OS (p=0.001, HR 2.323) and NRM (p<0.001, HR 3.905). However, pre-transplant ferritin level (ferritinPre-HCT) did not stratify well for OS (57.0% vs 61.8%, p=0.636) or NRM (30.8% vs 22.7%, p=0.247). Out of 229 patients, 171 (74.6%) developed any grade of acute GVHD, and 61 (26.6%) developed chronic GVHD. Median onset of acute and chronic GVHD were, respectively, 33.5 days and 145 days. Level of ferritinPost-HCT was significantly higher in the group that developed acute GVHD than in those without acute GVHD. However, there was no difference in ferritinPost-HCT between patients with or without chronic GVHD. FerritinPost-HCT was found to be an independent prognostic marker in the subgroup with acute GVHD for OS (p=0.001, HR 2.531) and NRM (p<0.001, HR 3.584), and in the subgroup with chronic GVHD for OS (p=0.042, HR 3.226) and NRM (p=0.009, HR 14.130). Conclusions: In our cohort, high ferritinPost-HCT levels were significantly associated with decreased OS and increased NRM, independent of other prognostic factors including ferritinPre-HCT or GVHD. Further prospective study including larger number of patients is warranted. Figure. Figure. Disclosures Lipton: ARIAD: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding. Kim:BMS: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy; Novartis: Consultancy, Honoraria, Research Funding; Paladin: Consultancy.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.258
Teacher spread0.228 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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