Frontiers in medicine · 2026
Analysis of microorganisms isolated from tracheal aspirate cultures and their antibiotic susceptibility profiles: a retrospective study from 2018 to 2022.
СтатьяНазвание и аннотация: Перевод в очереди
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Sanmak Erkan, Davarci İsmail, Gökdemir Gül Şahika, Güler Gökhan, Canbaz Hayri, Ayaydin Zeynep, Kabak Mehmet, Çil Barış
Аннотация
BACKGROUND: To determine the distribution of microorganisms isolated from tracheal aspirate (TA) cultures and their antimicrobial susceptibility patterns, and to assess resistance differences between intensive care unit (ICU) - and ward-derived isolates as well as temporal trends across years.
METHODS: Tracheal aspirate specimens obtained at a tertiary-care center between 2018 and 2022 were retrospectively reviewed. Only growth meeting laboratory acceptance criteria for causative pathogens was analyzed (semi-quantitative culture thresholds with cytologic quality control). Bacterial identification was performed using automated systems, and antimicrobial susceptibility testing was interpreted according to EUCAST standards. In addition to descriptive analyses, annual resistance trends and a joinpoint regression analysis (annual percent change) were conducted.
RESULTS: Of all causative isolates, 83.8% were Gram-negative. The most frequent pathogens were Klebsiella spp., Acinetobacter spp., and Pseudomonas spp. For Klebsiella spp., resistance to cephalosporins and fluoroquinolones was generally >90%, meropenem >80%, whereas imipenem showed comparatively higher susceptibility. In Acinetobacter spp., resistance was very high to most agents, with amikacin showing the lowest resistance. In Pseudomonas spp., resistance rates ranged from 40% to 55%, and amikacin emerged as the most active agent. Resistance was systematically higher in ICU-derived isolates than in ward isolates. Joinpoint analysis identified a single breakpoint around 2020; resistance trajectories during 2018-2020 were heterogeneous, with increases observed for some organism-antimicrobial combinations, followed by divergent patterns thereafter.
CONCLUSION: The predominance of Gram-negative pathogens and the high resistance burden in our center support locally tailored Gram-negative coverage for empiric therapy alongside early de-escalation. Temporal patterns underscore the need to update empiric policies using annual local surveillance data and to reinforce infection control and antimicrobial stewardship, particularly in ICUs.
METHODS: Tracheal aspirate specimens obtained at a tertiary-care center between 2018 and 2022 were retrospectively reviewed. Only growth meeting laboratory acceptance criteria for causative pathogens was analyzed (semi-quantitative culture thresholds with cytologic quality control). Bacterial identification was performed using automated systems, and antimicrobial susceptibility testing was interpreted according to EUCAST standards. In addition to descriptive analyses, annual resistance trends and a joinpoint regression analysis (annual percent change) were conducted.
RESULTS: Of all causative isolates, 83.8% were Gram-negative. The most frequent pathogens were Klebsiella spp., Acinetobacter spp., and Pseudomonas spp. For Klebsiella spp., resistance to cephalosporins and fluoroquinolones was generally >90%, meropenem >80%, whereas imipenem showed comparatively higher susceptibility. In Acinetobacter spp., resistance was very high to most agents, with amikacin showing the lowest resistance. In Pseudomonas spp., resistance rates ranged from 40% to 55%, and amikacin emerged as the most active agent. Resistance was systematically higher in ICU-derived isolates than in ward isolates. Joinpoint analysis identified a single breakpoint around 2020; resistance trajectories during 2018-2020 were heterogeneous, with increases observed for some organism-antimicrobial combinations, followed by divergent patterns thereafter.
CONCLUSION: The predominance of Gram-negative pathogens and the high resistance burden in our center support locally tailored Gram-negative coverage for empiric therapy alongside early de-escalation. Temporal patterns underscore the need to update empiric policies using annual local surveillance data and to reinforce infection control and antimicrobial stewardship, particularly in ICUs.