<i>De Novo</i> DQ Donor-Specific Antibodies Are Associated with Chronic Lung Allograft Dysfunction after Lung Transplantation
Bibliographic record
Abstract
RATIONALE: Despite increasing evidence about the role of donor-specific human leukocyte antigen (HLA) antibodies in transplant outcomes, the incidence and impact of de novo donor-specific antibodies (dnDSA) after lung transplantation remains unclear. OBJECTIVES: To describe the incidence, characteristics, and impact of dnDSA after lung transplantation. METHODS: We investigated a single-center cohort of 340 lung transplant recipients undergoing transplant during 2008 to 2011. All patients underwent HLA-antibody testing quarterly pretransplant and at regular intervals over the first 24 months after transplant. The patients received modified immunosuppression depending on their pretransplant sensitization status. Risk factors for dnDSA development, as well as the associations of dnDSA with patient survival and chronic lung allograft dysfunction (CLAD), were determined using multivariable analysis. MEASUREMENTS AND MAIN RESULTS: The cumulative incidence of dnDSA was 47% at a median of 86 days (range, 44-185 d) after lung transplantation. Seventy-six percent of recipients with dnDSA had DQ-DSA. Male sex and the use of ex vivo lung perfusion were associated with an increased risk of dnDSA, whereas increased HLA-DQB1 matching was protective. DQ-dnDSA preceded or coincided with the diagnosis of CLAD in all cases. Developing dnDSA (vs. no dnDSA) was associated with a twofold increased risk of CLAD (hazard ratio, 2.04; 95% confidence interval, 1.13-3.69). This association appeared to be driven by the development of DQ-dnDSA. CONCLUSIONS: dnDSA are common after lung transplantation, with the majority being DQ DSA. DQ-dnDSA are associated with an increased risk of CLAD. Strategies to prevent or treat DQ-dnDSA may improve outcomes for lung transplant recipients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".