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Enregistrement W4389030010 · doi:10.1093/ofid/ofad500.2083

2465. O-serotype Distribution of <i>Escherichia coli</i> Causing Invasive Disease in Tertiary Care Hospital Patients

2023· article· en· W4389030010 sur OpenAlexaff
Jeroen Geurtsen, Joachim Doua, Luis Martı́nez-Martı́nez, Patricia Palacios, Jeff Powis, Matthew Sims, Peter W. M. Hermans, Oliver Barraud, Philippe Lanotte, Joshua T. Thaden, Oscar Go, Bart Spiessens, Darren Abbanat, Florian Wagenlehner, Tetsuya Matsumoto, Marc J. M. Bonten, Michal Sarnecki, Jan Poolman

Notice bibliographique

RevueOpen Forum Infectious Diseases · 2023
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueEscherichia coli research studies
Établissements canadiensUniversity of Toronto
Organismes subventionnairesUniversitair Medisch Centrum UtrechtUniversity of Connecticut Health CenterUniversity of ConnecticutYale UniversityOPEC Fund for International Development
Mots-clésMedicineSerotypeGenotypingSeptic shockSepsisBacteremiaEscherichia coliInternal medicineGenotypeMicrobiologyVirologyAntibioticsBiology

Résumé

récupéré en direct d'OpenAlex

Abstract Background Escherichia coli is a common Gram-negative bacterium that can infect normally sterile body sites and cause invasive E. coli disease (IED) including bacteremia, sepsis and septic shock. E. coli surface O-antigens are important virulence factors that contribute to pathogenicity, making them promising targets for the development of multivalent conjugate vaccines to protect against IED. Here, we describe the prevalence of O-serotypes and O-genotypes of clinical E. coli isolates across a multinational cohort of patients with IED. Methods This was a retrospective, multicenter, noninterventional study across 17 tertiary care hospitals in Europe, North America and Asia. Patients with an IED diagnosis in the 12 months prior to data collection were included. IED was defined as E. coli presence in cultures from any normally sterile body site or urine in patients exhibiting clinical criteria of invasive disease (i.e., systemic inflammatory response syndrome [SIRS], sepsis, or septic shock) and no other identifiable site of infection. O-serotyping (agglutination) and O-genotyping (whole genome sequencing [WGS]) were conducted. Subgroup analyses were performed in isolates from patients with bacteremic vs nonbacteremic IED and in patients ≥60 years old. Results 902 patients with IED were identified (median age at initial IED diagnosis, 71.0 years; 51.6% male). The most common O-serotypes (prevalence ≥5%) based on O-genotyping were O25 (17.3% [95% CI, 14.82–20.06%]), O2 (11.7% [95% CI, 9.61–14.08%]), O6 (9.3% [95% CI, 7.44–11.49%]), O1 (6.3% [95% CI, 4.78–8.20%]), O15 (5.3% [95% CI, 3.85– 6.99%]) and O75 (5.0% [95% CI, 3.64–6.72%]) (Table 1). Collectively, these 6 most prevalent serotypes accounted for 55.0% of total isolates. A similar pattern of O-serotypes was observed in the subgroup of patients ≥60 years old (Table 2), with serotypes O25, O2 and O6 most common in both bacteremic and nonbacteremic IED isolates. Conclusion The most predominant O-serotype among IED isolates from hospitalized patients with IED was O25, followed by O2, O6, O1, O15 and O75. Such epidemiological data could inform the development of an effective prophylactic vaccine against IED. Disclosures Jeroen Geurtsen, PhD, Janssen: Employee|Janssen: Stocks/Bonds Joachim Doua, MD, MPH, Janssen: Employee|Janssen: Stocks/Bonds Patricia Ibarra de Palacios, MD, Janssen: Employee at the time of analysis Matthew Sims, MD, PhD, Astra-Zeneca: Investigator for company-sponsored studies|ContraFect: Investigator for company-sponsored studies|Crestone: Investigator for company-sponsored studies|Finch: Investigator for company-sponsored studies|Janssen: Investigator for company-sponsored studies|Leonard-Meron: Investigator for company-sponsored studies|Merck and Co: Investigator for company-sponsored studies|OpGen Inc: Advisor/Consultant|OpGen Inc: Investigator for company-sponsored studies|Pfizer: Investigator for company-sponsored studies|Prenosis: Advisor/Consultant|Prenosis: Investigator for company-sponsored studies|QIAGEN Sciences LLC: Investigator for company-sponsored studies|Roche: Investigator for company-sponsored studies|Seres Therapeutics: Investigator for company-sponsored studies Peter Hermans, PhD, Janssen: Employee at the time of analysis Joshua T. Thaden, MD, PhD, Resonantia Diagnostics, Inc: Advisor/Consultant Oscar Go, PhD, Janssen: Employee|Janssen: Stocks/Bonds Bart Spiessens, PhD, Janssen: Employee|Janssen: Stocks/Bonds Darren Abbanat, PhD, Janssen: Employee at the time of analysis Florian Wagenlehner, MD, Achaogen: Advisory Board member, study participation|Astellas: Honoraria|AstraZeneca: Honoraria|AstraZeneca: Advisory Board member|Biomedical Advanced Research and Development Authority (BARDA): Grant/Research Support|Bionorica: Honoraria|Bionorica: Meeting/travel support, study participation|Deutsches Zentrum für Infektionsforschung (DZIF): Study participation|Enteris BioPharma: Study participation|Everest Medicines: Grant/Research Support|German S3 guideline Urinary tract infections: Board Member|Glaxo Smith Kline: Advisor/Consultant|Glaxo Smith Kline: Honoraria|Glaxo Smith Kline: Consulting fees, meeting/travel support, advisory board member, principal investigator in a GSK-sponsored study|Global Antibiotic Research and Development Partnership (GARDP Foundation): Grant/Research Support|Guidelines European Association of Urology: Infections in Urology: Board Member|Helperby Therapeutics: Study participation|Janssen: Honoraria|Janssen: Advisory Board member|Klosterfrau: Honoraria|LeoPharma: Advisory Board member|MerLion: Advisory Board member|MIP Pharma: Honoraria|MSD: Advisory Board member|OM Pharma/Vifor Pharma: Advisory Board member, study participation|OM-Pharma: Honoraria|Pfizer: Honoraria|Pfizer: Advisory Board member|RosenPharma: Advisory Board member|Shionogi: Advisory Board member, study participation|Speaker research group German research foundation (DFG) Bacterial Renal Infections and Defense (FOR 5427): Study participation|Spero Therapeutics: Advisor/Consultant|Spero Therapeutics: Consulting fees|University Hospital Giessen and Marburg GmbH, and Justus Liebig University, Germany: Employee|Venatorx Pharmaceuticals, Inc.: Advisor/Consultant|Venatorx Pharmaceuticals, Inc.: Grant/Research Support|Venatorx Pharmaceuticals, Inc.: Consulting fees, Advisory Board member Tetsuya Matsumoto, MD; PhD, member of the international study steering committee for the E.mbrace study and reports payment: Board Member Marc Bonten, MD, PhD, chair of the international study steering committee for the E.mbrace study (Janssen Vaccines), with payments made to UMC Utrecht: Board Member Michal Sarnecki, MD, Janssen: Employee|Janssen: Stocks/Bonds Jan Poolman, PhD, Janssen: Employee|Janssen: Stocks/Bonds

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,006
Tête enseignante GPT0,262
Écart entre enseignants0,255 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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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