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Effectiveness of Posttransplant Prophylaxis With Anti-Hepatitis B Virus Immunoglobulin in Recipients of Heart Transplant From Hepatitis B Virus Core Antibody Positive Donors

2007· letter· en· W1969485389 on OpenAlexaffabout
Maria Krassilnikova, Marc Deschênes, Jean Tchevenkov, Nadia Giannetti, Renzo Cecere, Marcelo Cantarovich

Bibliographic record

VenueTransplantation · 2007
Typeletter
Languageen
FieldMedicine
TopicHepatitis B Virus Studies
Canadian institutionsRoyal Victoria Regional Health CentreMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineHBsAgLamivudineHepatitis B virusImmunosuppressionHepatitis BTransplantationImmunologyLiver transplantationInternal medicineGastroenterologyEntecavirSeroconversionAntibodyVirus

Abstract

fetched live from OpenAlex

Organ shortage remains a major problem in heart transplantation (HTx). In the past, donors who tested positive for hepatitis B virus (HBV) were removed from the pool because of the risk of HBV transmission to naïve recipients. Because hepatitis B remains widespread with 2 billion people infected worldwide, the use of expanded criteria donors has been reexamined. In one study, the use of organs from HBsAg−/ HBcAb+ donors resulted in 43% transmission of HBV in naïve liver recipients, while no transmission was observed in kidney or heart recipients (1). The course of de novo HBV infection was studied in 69 patients after HTx. The majority of the patients developed chronic hepatitis resulting in liver cirrhosis in 55% and death in 45.9% of the patients, respectively (2). In liver transplantation, it was shown that despite ongoing HB immunoglobulin (HBIg) administration, 15% to 50% of patients developed recurrent HBV likely due to escape mutations (3). Therefore, a combination of HBIg with nucleoside analogs has been evaluated to decrease the risk of HBV transmission from HBsAg−/HBcAb+ donors (4–6). Of 33 HTx recipients of HBsAg−/HBcAb+ donors, none of five patients receiving HBV prophylaxis with Lamivudine experienced seroconversion but one patient not receiving prophylaxis developed HBV infection (7). Between January 1998 and August 2001, 3 (HBV naïve) of 58 adult HTx received hearts from HBsAg−/HBcAb+ donors. Immunosuppression consisted of induction therapy with anti-thymocyte globulin in two patients and daclizumab in one patient, mycophenolate mofetil, cyclosporine microemulsion, and prednisone. All patients received HBIg (human hepatitis B immunoglobulin; BayHep B, Bayer Corporation) prophylaxis (intramuscular injections of 10 mL daily during the first 2 weeks postHTx, weekly for 1 month, and then every 6–12 weeks). We aimed for an HBsAb titer >250 IU/L. The use of HBIg was well tolerated without evidence of side effects. Liver function tests remained normal throughout a follow-up period of 7 years in two patients and 5 years in one. HBV serologies and HBV DNA polymerase chain reaction remained negative, and their HBsAb titers remained >250 IU/L. The use of hepatitis B vaccination prior to HTx increases the utilization of HBcAb+ donors (8). However, the rate of response to HBV vaccination is poor in sicker patients (8), it may not be feasible in case of emergency HTx and/or lack of completion of the vaccination schedule. Hence, there is a cluster of HTx recipients who are serologically naïve and who require other strategies for HBV prophylaxis. Our HBIg prophylaxis protocol was based in our liver transplant experience using HbcAb+ donors, at the time that the use of Lamivudine was not widely accepted. In HTx patients who are HBsAb−, preoperative and (if necessary) postoperative vaccination should be considered. However, one study, reported a seroconversion rate of 12.9% and it has been suggested to administer HBIg until an HBsAb titer >100 IU/L is achieved (9). Our report has the limitations of a small study involving only three subjects. However, our results suggest that HBIg may be effective to prevent HBV infection in naïve HTx recipients from HBcAb+ donors. Maria Krassilnikova Department of Medicine Royal Victoria Hospital McGill University Health Center McGill University Montréal, Québec, Canada Marc Deschenes Department of Medicine Royal Victoria Hospital McGill University Health Center McGill University Montréal, Québec, Canada Jean Tchevenkov Department of Surgery Royal Victoria Hospital McGill University Health Center McGill University Montréal, Québec, Canada Nadia Giannetti Department of Medicine Royal Victoria Hospital McGill University Health Center McGill University Montréal, Québec, Canada Renzo Cecere Department of Cardiovascular and Thoracic Surgery Royal Victoria Hospital McGill University Health Center McGill University Montréal, Québec, Canada Marcelo Cantarovich Department of Medicine Royal Victoria Hospital McGill University Health Center McGill University Montréal, Québec, Canada

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)0.003
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
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 head0.015
GPT teacher head0.279
Teacher spread0.264 · 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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Citations7
Published2007
Admission routes2
Has abstractyes

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