A Primed Neutrophil Subset Predicts the Risk of Bloodstream Infections in Allogenic Bone Marrow Transplant Patients: A Prospective Study
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».