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222.8: Effect of recurrent CMV viremia on graft and patient survival in renal transplantation.

2024· article· en· W4402800774 on OpenAlexaffabout
Sabina Dobrer, Karen Sherwood, Kimberly Davis, James H. Lan, John S. Gill, Anat Fisher, Paul Keown

Bibliographic record

VenueTransplantation · 2024
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsViremiaMedicineTransplantationCytomegalovirusKidney transplantationVirologyInternal medicineHuman immunodeficiency virus (HIV)Viral diseaseHerpesviridae

Abstract

fetched live from OpenAlex

Background: The Genome Canada Precision Medicine Program has shown that cytomegalovirus (CMV) viral load kinetics determine the risk of failure in a large renal transplant cohort with uniform clinical management followed for up to 13 yrs. Here we show that recurrence of CMV viremia, related to CMV risk status, treatment and other factors, has a further profound effect on graft and patient survival. Methods: 2,507 sequential patients who received a renal transplant at UBC from 01/01/08-31/12/08 were followed until 31/12/19 (>6 million patient days of follow-up). 1,441 (57%) received a deceased donor (DD) and 1066 (42%) a live donor (LD) transplant, 2321 (93%) received 1 graft and 186 (7%) >1 graft. All were managed using uniform provincial guidelines for immune suppression and anti-viral therapy. Patients were stratified by donor source, graft number, donor/recipient (D/R) CMV status and CMV viral episode for analysis of graft and patient outcomes. Results: 2,464 patients (98%) had complete data for analysis; 59% were Caucasian, 62% were male, the mean age was 52 ± 15 yrs and CMV strata were D+/R+ (883, 35%), D+/R- (454, 18%), D-/R+ (685, 27%) and D-/R- (442, 18%). Overall, 434 patients developed primary CMV viremia (viral titre: ≥830 IU/ml; range: 1-7 log 10 IU/ml) of whom 67 had recurrent viremia. Recurrent viremia was more common vs no viremia in non-Caucasian (51% vs 39%, p=0.0014), older (mean: 58 vs 51 yr. p<0.0001), D+/R- (45% vs 15%, p<0.0001), and DD graft (78% vs 55%, p<0.0001) recipients, and those receiving antiviral prophylaxis (72% vs 41%, p<0.0001). Multinomial adjusted regression confirmed that increased age, non-Caucasian race, diabetes, D+/R-status and delayed graft function (DGF) were significant predictors of recurrent CMV viremia (p<0.005). Mean peak time from transplant to recurrent CMV infection was 250 days, being shortest in D+/R+ and longest in D-/R- recipients (Figure 1). Mean cumulative event frequency (total viremic episodes by time) reached an asymptote of 21% at 500 days, with rates of 43% in D+/R-, 26% in D+/R+, 14% in D-/R+, and 1% in D-/R- risk groups. Kaplan–Meier analysis found that the probability of the composite endpoint of patient survival with a functioning graft was significantly reduced in patients with recurrent CMV viremia (no CMV viremia 85%, primary CMV viremia 83%, recurrent CMV viremia 63%, p<0.0001; Figure 2). Conclusion: This study confirms that patients with a recurrence of CMV viremia post-transplant are at exceptionally high risk of transplant failure as measured by graft loss or death. The risk of recurrent CMV is related to race, age, donor source and D/R CMV status. Conventional prophylaxis appears to be inadequate to protect these patients from recurrent infection and its serious consequences, indicating that alternative treatment strategies, with continuous long-term monitoring and rapid, effective therapy are of vital importance in these susceptible patients to maximize transplant success.Funding provided by Takeda Development Center Americas, Inc., Genome BC (Genome Canada 273AMR) and the Canadian Institutes of Health Research (CIHR GP1-155871).

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)0.002
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.315
Teacher spread0.299 · 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
Published2024
Admission routes2
Has abstractyes

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