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Enregistrement W4317234251 · doi:10.2106/jbjs.22.01220

Know Your Microbial Enemy

2023· letter· en· W4317234251 sur OpenAlexaff
Michelle Ghert

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2023
Typeletter
Langueen
DomaineMedicine
ThématiqueOrthopedic Infections and Treatments
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésPeriprostheticStaphylococcus aureusStaphylococcus epidermidisMedicineGram stainingAntibioticsJoint arthroplastyMicrobiologyBiologyArthroplastySurgeryBacteriaGenetics

Résumé

récupéré en direct d'OpenAlex

Commentary “Know your enemy.” This far-seeing counsel by Sun Tzu in The Art of War1 remains relevant even 2,000 years after the words were written. Although periprosthetic joint infections (PJIs) are arguably the most devastating complications of arthroplasty, a PJI cannot occur without a causative organism, the enemy. The war against PJI pathogens cannot be won without first knowing them; we need to know what they are and how they behave. Tarabichi et al. report the culture results and the time to positivity of intraoperative samples obtained in 536 PJIs. Although the most common organisms were methicillin-sensitive Staphylococcus aureus and Staphylococcus epidermidis, many other pathogens were identified, including gram-negative rods and Candida species. As such, it is difficult to predict in each individual case “who” will be the enemy. Nevertheless, the study did provide important information on when the pathogens are likely to be identified in culture, with methicillin-resistant S. aureus, gram-negative rods, and methicillin-sensitive S. aureus having the shortest times to positivity (median, <2 days) and Cutibacterium acnes having the longest time to positivity (median, 7 days). These data represent a small but meaningful step forward to understanding the enemy and how it behaves. The findings of the study are helpful in several ways. First, the data highlight the wide range of pathogens that cause PJIs. Others have also reported that causative organisms in PJI are not limited to the Staphylococcus species2, and, therefore, wider-spectrum prophylactic antibiotics may be indicated in patients at risk for more biologically complex infections. The challenge remains to identify these patients prior to the surgical procedure. Second, if the clinical presentation indicates that a highly virulent pathogen is likely, cultures can be assessed early, at 24 to 28 hours, and antibiotics can be tailored to the specific organisms isolated. Third, the findings highlight the importance of maintaining cultures to 14 days, as some pathogens can be identified as late as this date. Fourth, extrapolation from the sepsis and urosepsis literature, as comprehensively referenced by Tarabichi et al., suggests that patients with rapidly identified PJI pathogens may be at increased risk for poor clinical outcomes and therefore should be treated aggressively. Finally, although only 2 clinical sites participated in the study, the data are international; given that and the large sample size, the results of this study can be considered somewhat generalizable. There is still much more that we need to know to defeat the enemy in joint reconstruction. Many PJIs are the result of bacterial colonization of biofilm that is formed on the implant. These sessile organisms are less likely to be detected in intraoperative samples as they are not planktonic and therefore sonication of the implant would be required to release them into fluid or tissue culture specimens. Sonication was not performed in the current study and, therefore, cultures may have missed biofilm-associated bacteria. In addition, Tarabichi et al. did not correlate culture results with clinical outcome. These outcomes were confounded by nonstandardized treatment protocols, and, therefore, knowing the behavior of the bacteria in vitro may not give clinicians enough information, at least at this point, to direct the treatment approach and clarify the prognosis. Overuse of antibiotics in limb reconstruction has been shown to increase the risk of serious antibiotic complications and may lead to resistant organisms3. As such, subsequent research could aim to clarify the microbiologic criteria for PJI prognostication and, therefore, provide guidance for de-escalation of antimicrobial treatment when appropriate. Overall, Tarabichi et al. provide useful information as a starting point to understand the in vitro behavior of PJI pathogens, the enemy. The more we understand these pathogens, the closer we will be to defeating them. In the meantime, clinicians now have a greater understanding of culture dynamics and the timeline for assessing and acting upon culture results. This knowledge can curb overtreatment and eventually lead to more patient-specific antibiotic prophylaxis and PJI management. Furthermore, future research that provides direct clinical extrapolation will take us closer to knowing the enemy and understanding the art of the war against it.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,036
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,083

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,036
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0030,004
Communication savante0,0030,004
Science ouverte0,0040,002
Intégrité de la recherche0,0220,028
Charge utile insuffisante (le modèle a refusé de juger)0,0250,015

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.

Tête enseignante Opus0,045
Tête enseignante GPT0,267
Écart entre enseignants0,222 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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