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Cytomegalovirus (CMV) Serostatus: High Importance of Choosing Seropositive Donors for Seropositive Patients When Using Rabbit Antithymocyte Globulin (ATG)

2015· article· en· W2565750806 on OpenAlexaffabout
Amit Kalra, Andrew Daly, Jan Storek

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSerostatusCumulative incidenceInternal medicineGastroenterologyImmunologyCyclophosphamideMyeloid leukemiaViremiaProportional hazards modelTransplantationChemotherapyViral loadAntibodyVirus

Abstract

fetched live from OpenAlex

Abstract Introduction: In published studies that included mostly hematopoietic cell transplants (HCTs) not using in vivo T cell depletion by ATG, there is a substantial survival difference between D-R- and D+R- patients, and a very small or no survival difference between D-R+ and D+R+ patients. We set out to determine the impact of donor CMV serostatus on survival after HCTs using ATG. Methods: A total of 919 patients underwent myeloablative HCT for hematologic malignancies in Alberta between 1999 and 2014, with a uniform graft versus host disease (GVHD) prophylaxis using ATG (4.5 mg/kg given between day -2 and 0) in addition to methotrexate and cyclosporine. Monitoring for CMV reactivation was performed until at least day 100 after transplant using pp65 antigenemia (before 2008) or CMV DNAemia by quantitative polymerase chain reaction (since 2008). Preemptive therapy typically with ganciclovir was started whenever pp65 antigenemia exceeded 10 to 20 positive cells/200,000 granulocytes or CMV DNAemia exceeded 25,000 IU/mL plasma. Significance of survival difference between patient groups was tested using Cox proportional hazards analysis. Significance of difference in nonrelapse mortality (NRM) or cumulative incidence of relapse (CIR) was tested using Fine-Gray analysis (nonrelapse death was a competing risk of relapse and vice versa, in addition to graft failure and second malignancy). In all analyses, covariates included patient age (>45 vs ≤45), disease stage (acute leukemia in first remission, chronic myeloid leukemia in first chronic phase and myelodysplasia with <5% marrow blasts were considered good risk, all others poor risk), graft type (marrow vs PBSCs) and donor type (human leucocyte antigen [HLA] matched sibling vs other [typically matched unrelated]). Results: D-R- and D+R- patients had similar survival (no significant difference) (blue vs orange curve, figure 1). D-R+ patients had a substantially lower survival than D+R+ patients with an estimated 5 year survival of 41% versus 60% respectively (Hazard Ratio [HR], 1.6; P = 0.001; 95% confidence interval[CI], 1.22-2.09) (red vs green curve, figure 1). This was due to higher NRM (sub hazard ratio [SHR], 1.76; P = 0.003; 95% CI, 1.212-2.58), not CIR. This appeared to be due to higher GVHD-associated mortality. We then compared D-R+ HLA matched sibling transplants with D+R+ HLA matched unrelated donor transplants to assess whether for a CMV seropositive patient with a CMV seronegative HLA matched sibling, search for a CMV seropositive HLA matched unrelated donor could be justified. D-R+ HLA matched sibling transplant recipients had a substantially lower survival than D+R+ 8/8 HLA allele matched unrelated donor transplant recipients with an estimated 5 year survival of 45% versus 66% respectively (HR, 1.7, P = 0.01, 95% CI, 1.10-2.89) (green vs orange curve, figure 2). This suggests that in the scenario of a seropositive patient with a seronegative matched sibling, unrelated donor search is justified. Conclusion: Contrary to recommendations for HCTs not using ATG, choosing a seronegative donor for a seronegative patient is unimportant, whereas choosing a seropositive donor for a seropositive patient is very important (~20% absolute 5 year survival gain). Even if a seronegative HLA matched sibling donor is available for a seropositive patient, it is better to select a seropositive HLA matched unrelated donor. Disclosures No relevant conflicts of interest to declare.

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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)0.004
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.313
Teacher spread0.271 · 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
Published2015
Admission routes2
Has abstractyes

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