Decreased Infections in Recipients of Unrelated Donor (URD Hematopoietic Cell Transplantation (HCT) from Donors with An Activating KIR B Genotype (B/x)
Bibliographic record
Abstract
Abstract In URD allogeneic HCT, donor, not recipient, KIR B/x genotype improves leukemia free survival. Decreased rates of CMV reactivation occur in renal transplant recipients with KIR B/x genotypes. We hypothesized that recipients of cells from a KIR B/x genotypic URD will have decreased infectious complications due to enhanced NK cell function. The National Marrow Donor Program (NMDP) prospectively collected data oninfectious complications at 2 week intervals beginning with conditioning until day 100 and then monthly until day 180 after URD HCT for malignant and nonmalignant diseases at 27 centers (n = 211). A subset of these donors (n = 116) had samples available through the NMDP Research Repository for KIR genotyping (A/A: n = 44; B/x: n = 72). The two cohorts had similar characteristics including age, gender, Karnofsky score, disease and disease status, degree of HLA matching, conditioning intensity, graft type, and donor/recipient CMV match and sex match. Infections were characterized as clinical infectious syndromes if no organism was identified or bacterial, fungal, or viral based on the causative organism. Bacterial infections were significantly lower for recipients of a B/x genotypic donor compared to the A/A genotype [68% (57 – 78) vs 86% (75 – 95); p = 0.02]. The cumulative incidence of other infection types was similar between the groups. A Poisson regression model estimated the expected number of infections for a patient observed up until day 180 and determined characteristics associated with specific infections. Donor KIR genotype was considered in every model. Other factors analyzed included patient and donor age, donor/recipient CMV and sex match, disease (leukemia/MDS vs other), disease status, HLA match (well matched vs partially matched vs mismatched), GVHD prophylaxis (CSA/FK based vs other), and graft type. The mean estimated number of infections per patient was as follows: bacterial, 1.138; viral, 0.58; fungal, 0.764; clinical infectious syndromes, 0.506; and total infections, 6.306. Based on these models, a B/x donor was associated with a statistically lower mean ratio of total infections [B/x = 1.00 vs A/A = 1.23 (1.02 – 1.49), p = 0.03] and bacterial infections [B/x = 1.00 vs A/A = 1.50 (1.16 – 1.94), p = 0.002] compared to recipients of an A/A donor. The table shows the other factors associated with total infections and bacterial infections. The use of an A/A donor was associated with a lower mean ratio of fungal infections [B/x = 1.00 vs A/A= 0.40 (0.17 – 0.92), p = 0.03] but similar ratios of viral and clinical infectious syndromes. Multivariate analysis found a similar relative risk of aGVHD II – IV, cGVHD, and survival regardless of donor KIR genotype. The role of donor KIR genotype on post HCT infectious complications is intriguing and warrants further study in larger populations of patients with uniform antimicrobial prophylaxis to better assess clinical impact. Variable Total Infections Bacterial Infections Ratio of Mean number of infections (95% CI) p-value Ratio of Mean number of infections (95% CI) p-value KIR Genotype A/A 1.23 (1.02 – 1.49) 0.03 1.50 (1.16 – 1.94) 0.002 B/x 1.00 1.00 Patient Age, yrs <0.0001 0.004 <10 0.44 (0.34 – 0.88) 0.01 0.60 (0.32 – 1.15) 0.13 10 – 19 1.23 (0.9 – 1.69) 0.20 1.36 (0.85 – 2.16) 0.20 20 – 29 0.57 (0.42 – 0.77) 0.0003 0.50 (0.33 – 0.78) 0.002 30 – 39 1.05 (0.81 – 1.37) 0.71 0.91 (0.64 – 1.30) 0.61 40 – 49 0.69 (0.51 – 0.94) 0.02 0.84 (0.55 – 1.28) 0.42 >50 1.00 1.00 D/R sex match 0.0001 0.009 F/F 1.00 1.00 F/M 1.28 (0.95 – 1.72) 0.10 1.22 (0.81 – 1.83) 0.34 M/F 0.84 (0.60 – 1.17) 0.30 0.76 (0.48 – 1.20) 0.24 M/M 1.50 (1.14 – 1.97) 0.004 1.40 (0.97 – 2.04) 0.07 D/R CMV match <0.0001 0.03 N/N 0.79 (0.56 – 1.12) 0.18 0.90 (0.59 – 1.37) 0.62 N/P 1.65 (1.25 – 2.18) 0.0004 1.31 (0.90 – 1.90) 0.16 P/N higher 0.84 (0.61 – 1.16) 0.29 0.76 (0.49 – 1.16) 0.20 P/P 1.00 1.00 Disease status 0.0008 0.0003 Early 1.14 (0.87 – 1.48) 0.34 1.51 (1.04 – 2.18) 0.03 Intermediate 0.68 (0.51 – 0.91) 0.01 0.61 (0.40 – 0.92) 0.02 Advanced 1.23 (0.95 – 1.60) 0.13 1.36 (0.96 – 1.93) 0.09 Other 1.00 1.00 Donor Age, yrs 0.022 - 20 – 29 0.64 (0.46 – 0.88) 0.006 - 30 – 39 0.80 (0.58 – 1.09) 0.16 - 40 – 49 0.89 (0.63 – 1.24) 0.48 - >50 1.00 - HLA Match <0.0001 Well matched - 1.00 Partially matched - 1.54 (1.15 – 2.07) 0.004 Mismatched - 2.87 (2.02 – 4.05) <0.0001 GVHD Prophylaxis - CSA/FK based - 1.84 (1.30 – 2.60) 0.0005 Other - 1.00
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".