584. Microbiological Outcomes of Culture-Negative Blood Specimens Using 16s rRNA Broad-Range PCR Sequencing: a Retrospective Study in a Canadian Province from 2018 to 2022
Notice bibliographique
Résumé
Abstract Background Broad-range bacterial PCR sequencing (BRBPS) has emerged as a novel tool to detect fastidious organisms. While its utility has been characterized in different specimen types, its role in culture-negative blood specimens remains poorly understood. Methods We reviewed all clinical specimens sent for culture-negative blood BRBPS (blood, serum and blood culture bottles) to the Laboratoire de santé publique du Québec, the reference laboratory for a large Canadian province, from May 2018 to November 2022. Sanger sequencing of the amplified 16s rRNA gene was performed in all PCR-positive specimens. Data were extracted from the laboratory information system, and the analysis was restricted to the first specimen per patient. Microbiological outcomes were categorized as interpretable sequence, uninterpretable sequence, or negative PCR result. Interpretable sequences were identified and then classified based on the National Healthcare Safety Network database (NHSN; Table 1) and microbiological characteristics.Table 1.Definitions of National Health Safety Network (NHSN) Classification and Examples Results A total of 1199 blood specimens were analyzed using BRBPS from 852 unique patients. Of these, there was no PCR amplification in 152, amplification with uninterpretable sequences in 445 and an interpretable sequence in 255 specimens (Figure 1). Blood specimens received at room temperature, in blood culture bottles, or with positive gram stain were more likely to yield interpretable sequences (Table 2). We identified 174 patients with BRBPS results suggestive of organisms associated with mucosal barrier injury (n=89) or possible pathogens (n=85), summarized in Figure 2. In contrast, 75 patients had results suggestive of contamination from common commensal organisms (n=44) or taxa not in the NSHN database (n=31).Figure 1.Flowchart of the analysis from primary specimens to those specimens with successful amplification of 16s rRNA Classification of interpretable sequences using the National Healthcare Safety Network database classification of microorganisms.Table 2.Characteristics of BRBPS of the 16s rRNA on Culture-Negative Blood SpecimensFigure 2.Tree Map of Mucosal Barrier Injury Organisms and Possible Pathogens by Genera Mucosal barrier injury (A) and possible pathogens (B) are categorized based on the National Healthcare Safety Network database. Microorganisms are colour classified based on microbiological characteristics. Conclusion Our findings demonstrate the potential utility of BRBPS in blood specimens from culture-negative patients, particularly infectious syndromes caused by fastidious gram-negative bacteria associated with animal or arthropod exposures or anaerobic bacteria. However, the frequent recovery of commensal and environmental organisms argues for careful and judicious use. Additional technical optimization is likely required to improve diagnostic yield, particularly with mixed sequences. Disclosures Matthew Cheng, MD, Amplyx Pharmaceuticals: Grant/Research Support|AstraZeneca: Advisor/Consultant|AstraZeneca: Honoraria|Cidara Therapeutics: Grant/Research Support|GEn1E lifesciences: Advisor/Consultant|GEn1E lifesciences: Stocks/Bonds|Kanvas Biosciences, Inc.: Board Member|Kanvas Biosciences, Inc.: Pending patents|Kanvas Biosciences, Inc.: Ownership Interest|Merck: Honoraria|nomic bio: Advisor/Consultant|nomic bio: Stocks/Bonds|Pfizer: Honoraria|Scynexis Inc.: Grant/Research Support|Takeda: Advisor/Consultant|Takeda: Honoraria
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».