A Primed Neutrophil Subset Predicts the Risk of Bloodstream Infections in Allogenic Bone Marrow Transplant Patients: A Prospective Study
Bibliographic record
Abstract
Background: Blood stream infections (BSIs) are the most common infectious complications in patients receiving allogenic hematopoietic stem cell transplants (allo-HSCT). Timing of polymorphonuclear neutrophil (PMN) recovery following allo-HSCT varies widely among patients. PMN counts are monitored to assess susceptibility of patients to BSIs. Even though allo-HSCT patients undergo a phase of severe neutropenia, some of these patients develop BSIs while others don't. Recent studies have emerged describing the heterogeneity of PMNs and the different functional phenotypes. This raises questions on whether susceptibility to BSIs is related to levels of specific PMN phenotypes rather than total PMN counts. Our previous work has identified a primed PMNs (pPMN) phenotype as a steady-state subset representing (~10%) of PMNs in the circulation. These pPMNs display enhanced transmigration and activation, making them the first cells to respond to bacterial insults. In this study, we explore the potential use of the levels of pPMNs as a biomarker for assessing BSI risk in allo-HSCT patients. Methods: This study was a prospective longitudinal assessment of PMNs in consecutive blood and oral samples collected from 76 patients who underwent an allo-HSCT at Princess Margaret Cancer Hospital between August 2020 and August 2021. Patients received the transplant on day 0 and blood and oral samples were collected from the patients on days (-5, +5, +7, +9, +11, +13, +15, +17, +19 and +21). Blood and oral samples were immediately processed, PMNs were counted, phenotyped with a seven CD marker panel of antibodies to identify pPMNs. Flow cytometry was performed, and data was analyzed by FlowJo software. Results: While all allo-HSCT patients displayed a decline in PMN counts during the early post-transplant phase, two patient subsets were identified by their opposing trends in pPMN frequency. Patients were divided into a high pPMN group (n=36) and a low pPMN group (n=40) based on having above or below the 10% threshold of average blood pPMN percentage on day +5 post-transplant. Patients in the low group had increased susceptibly to BSIs (Hazards ratio= 4.814, 95% CI= 2.086-11.11, P= 0.0013). This was not affected after adjusting for sex, age at transplant, diagnosis, conditioning regimen, mucositis, and the stem cell source and counts and remained statistically significant (P<0.01). We also show that patients in the low pPMN group had delayed oral repopulation of PMNs (19.81 days post-transplant) compared to patients in the high group (15.95 days post-transplant). Oral PMN repopulation helps in maintaining equilibrium between the host immune response in the oral cavity and the colonizing microorganisms which is crucial to preventing infections. Therefore, delays in oral repopulation might be one of the means by which deficiencies in pPMNs contribute to BSIs. Conclusion: In patients receiving an allo-HSCT, having less than 10% pPMNs early in the post-transplant phase can be used as an independent early predictor of BSI in allo-HSCT patients
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".