1820. Clinical Characteristics of Invasive Extraintestinal Pathogenic Escherichia coli Disease Among Older Adult Patients Treated in Hospitals in the United States
Notice bibliographique
Résumé
Abstract Background The risk of invasive extraintestinal pathogenic Escherichia coli disease (IED) increases with age and can lead to severe outcomes, including sepsis and death. Aim To describe the clinical outcomes of IED in older adults in the United States (US). Methods The Premier Healthcare Database (10/01/2015-03/31/2020) was used to identify IED encounters among patients ≥ 60 years old. The index encounter was defined as the first encounter with a positive E. coli culture in a normally sterile body site (Group 1) or a positive E. coli culture in urine with signs of sepsis (Group 2), in the absence of other pathogens. Outcomes included medical resource utilization, antibiotic use, IED recurrence, and in-hospital death, and were descriptively reported during the index encounter and over the subsequent year. Results Overall, 19,773 patients with IED were included (mean age: 76.8 years; 67.4% female; 82.1% white). Approximately half of index encounters were from Group 1 (51.8%), and the vast majority of patients had community-onset IED (94.3%). Most index encounters led to inpatient hospitalization (96.5%; mean duration: 6.9 days) and 32.4% required transfer to an intensive care unit (mean duration: 3.7 days). During the index encounter, patients received a mean [SD] of 2.9 [1.4] antibiotic agents, and 30.1% received ≥ 4 agents. The 3 most prevalent antibiotics received were ceftriaxone (66.2%), vancomycin (36.3%), and piperacillin (35.0%; Fig 1). The majority of E. coli isolates showed resistance to ≥ 1 antibiotic category (61.7%), and 34.4% were classified as multi-drug resistant (i.e., ≥ 3 categories). Following discharge, 34.8% of patients were transferred to a skilled nursing/intermediate care facility. In-hospital death reached 6.8% during the index encounter and increased to 10.9% 1-year post-index (Fig 2). One-year post-index, 2.4% of patients had an IED recurrence and 36.8% were readmitted to the hospital for any reason (Fig 3). Conclusion Our findings suggest that IED is a severe disease that is associated with substantial burden and far-reaching consequences beyond the initial encounter. These findings emphasize the need for increased awareness and surveillance of IED and its consequences and the potential benefit of preventative measures. Disclosures Elie Saade, MD, Janssen: Grant/Research Support|Pfizer: Board Member|Pfizer: Grant/Research Support|Sanofi Pasteur: Grant/Research Support|Sanofi Pasteur: Speaking/lecture fees, travel reimbursement|Seqirus: Grant/Research Support Jeroen Geurtsen, PhD, Janssen: Employee of Janssen Vaccines & Prevention BV Bryan Baugh, MD, Janssen Research & Development LLC: Employee|Janssen Research & Development LLC: Stocks/Bonds Antoine El Khoury, PhD, Janssen: Employee of Janssen Global Services, LLC. Nnanya Kalu, PhD, Janssen: Employee of Janssen Scientific Affairs, LLC. Marjolaine Gauthier-Loiselle, PhD, Janssen: Advisor/Consultant|Janssen: Advisor/Consultant Rebecca Bungay, MScPH, Janssen: Employees of Analysis Group, Inc. which has received consultancy fees from Janssen Scientific Affairs, LLC for the conduct of this studies. Martin Cloutier, MSc, Janssen: Employees of Analysis Group, Inc. which has received consultancy fees from Janssen Scientific Affairs, LLC for the conduct of this studies. Luis Hernandez Pastor, PharmD, PhD, Janssen: Employee of Janssen Pharmaceutica NV.
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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 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».