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144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia

BACKGROUND: Time to appropriate antimicrobial therapy is the most important predictor of survival among patients with sepsis due to Gram-negative bacteria (GNB). Infections due to multidrug-resistant (MDR) pathogens often result in inappropriate empiric treatment (tx). The GenMark ePlex identifies o...

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Autores principales: Jawanda, Jasanjeet, Clarke, Lloyd, Creager, Hannah M, Shields, Ryan K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678969/
http://dx.doi.org/10.1093/ofid/ofad500.217
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author Jawanda, Jasanjeet
Clarke, Lloyd
Creager, Hannah M
Shields, Ryan K
author_facet Jawanda, Jasanjeet
Clarke, Lloyd
Creager, Hannah M
Shields, Ryan K
author_sort Jawanda, Jasanjeet
collection PubMed
description BACKGROUND: Time to appropriate antimicrobial therapy is the most important predictor of survival among patients with sepsis due to Gram-negative bacteria (GNB). Infections due to multidrug-resistant (MDR) pathogens often result in inappropriate empiric treatment (tx). The GenMark ePlex identifies organisms and resistance markers within 1.5 hours from blood culture positivity compared to 48-72 hours by conventional methods. METHODS: We are conducting a single-center study to assess outcomes before and after ePlex implementation. The pre-intervention period included patients with GN bacteremia from January to June 2021. Patients were excluded if they received comfort-only care within 48 hours, had polymicrobial GN bacteremia, or were infected by an organism not on the ePlex panel. Primary outcome is time to in-vitro active therapy from blood culture collection. RESULTS: 208 patients were identified during the pre-intervention period; 154 met inclusion criteria. Median age was 64 years, 49% were male, and 28% were immunocompromised. Median (IQR) Charlson Comorbidity index and Pitt Bacteremia scores were 6 (4-8) and 2 (1-8), respectively (Table 1). 16% of patients were infected by MDR GNB and 21% received inactive empiric tx. Overall median (IQR) time to in-vitro active tx was 3.9 (1–19) hours, but was 39.5 (13.6-71.9) hours among those who received inactive empiric tx. Median (IQR) time to first antibiotic modification was 2.7 (1.0-3.4) days. 38% of patients were transitioned to oral antibiotics at a median (IQR) of 4.2 (3.1-6.1) days. The median (IQR) duration of tx was 15 (10-17) days; 4.5% of patients were treated with ≤ 7 days. Median (IQR) length of hospitalization was 15.5 (7-36) days. 7.8% were re-admitted within 30 days (Table 2). In-hospital and 30-day mortality rates were numerically higher among patients who received inactive empiric therapy (27% and 21%, respectively) when compared to patients who received active empiric therapy (16.5% and 12%, respectively) (Figure 1). Results Table 1: Patient demographics, underlying conditions and infection and treatment characteristics in the pre-intervention group [Figure: see text] Results Table 2 [Figure: see text] Results Figure 1 [Figure: see text] CONCLUSION: Our pre-intervention data highlight opportunities to improve the management of GN bacteremia. Implementation of ePlex is likely to decrease the proportion of patients treated with inactive therapy, shorten time to optimal tx and reduce lengths of stay. DISCLOSURES: Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support
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spelling pubmed-106789692023-11-27 144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia Jawanda, Jasanjeet Clarke, Lloyd Creager, Hannah M Shields, Ryan K Open Forum Infect Dis Abstract BACKGROUND: Time to appropriate antimicrobial therapy is the most important predictor of survival among patients with sepsis due to Gram-negative bacteria (GNB). Infections due to multidrug-resistant (MDR) pathogens often result in inappropriate empiric treatment (tx). The GenMark ePlex identifies organisms and resistance markers within 1.5 hours from blood culture positivity compared to 48-72 hours by conventional methods. METHODS: We are conducting a single-center study to assess outcomes before and after ePlex implementation. The pre-intervention period included patients with GN bacteremia from January to June 2021. Patients were excluded if they received comfort-only care within 48 hours, had polymicrobial GN bacteremia, or were infected by an organism not on the ePlex panel. Primary outcome is time to in-vitro active therapy from blood culture collection. RESULTS: 208 patients were identified during the pre-intervention period; 154 met inclusion criteria. Median age was 64 years, 49% were male, and 28% were immunocompromised. Median (IQR) Charlson Comorbidity index and Pitt Bacteremia scores were 6 (4-8) and 2 (1-8), respectively (Table 1). 16% of patients were infected by MDR GNB and 21% received inactive empiric tx. Overall median (IQR) time to in-vitro active tx was 3.9 (1–19) hours, but was 39.5 (13.6-71.9) hours among those who received inactive empiric tx. Median (IQR) time to first antibiotic modification was 2.7 (1.0-3.4) days. 38% of patients were transitioned to oral antibiotics at a median (IQR) of 4.2 (3.1-6.1) days. The median (IQR) duration of tx was 15 (10-17) days; 4.5% of patients were treated with ≤ 7 days. Median (IQR) length of hospitalization was 15.5 (7-36) days. 7.8% were re-admitted within 30 days (Table 2). In-hospital and 30-day mortality rates were numerically higher among patients who received inactive empiric therapy (27% and 21%, respectively) when compared to patients who received active empiric therapy (16.5% and 12%, respectively) (Figure 1). Results Table 1: Patient demographics, underlying conditions and infection and treatment characteristics in the pre-intervention group [Figure: see text] Results Table 2 [Figure: see text] Results Figure 1 [Figure: see text] CONCLUSION: Our pre-intervention data highlight opportunities to improve the management of GN bacteremia. Implementation of ePlex is likely to decrease the proportion of patients treated with inactive therapy, shorten time to optimal tx and reduce lengths of stay. DISCLOSURES: Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support Oxford University Press 2023-11-27 /pmc/articles/PMC10678969/ http://dx.doi.org/10.1093/ofid/ofad500.217 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstract
Jawanda, Jasanjeet
Clarke, Lloyd
Creager, Hannah M
Shields, Ryan K
144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia
title 144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia
title_full 144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia
title_fullStr 144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia
title_full_unstemmed 144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia
title_short 144. Impact of Rapid Identification and Resistance Gene Detection using an Algorithm-Based Approach in Gram-Negative Bacteremia
title_sort 144. impact of rapid identification and resistance gene detection using an algorithm-based approach in gram-negative bacteremia
topic Abstract
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10678969/
http://dx.doi.org/10.1093/ofid/ofad500.217
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