Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022
β-Lactam-Resistant Streptococcus pneumoniae Dynamics Following Treatment: A Dose-Response Meta-analysis.
Систематический обзорМетаанализСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Griskaitis Matas, Furuya-Kanamori Luis, Allel Kasim, Stabler Richard, Harris Patrick, Paterson David L, Yakob Laith
Аннотация
BACKGROUND: Patient exposure to antibiotics promotes the emergence of drug-resistant pathogens. The aim of this study was to identify whether the temporal dynamics of resistance emergence at the individual-patient level were predictable for specific pathogen-drug classes.
METHODS: Following a systematic review, a novel robust error meta-regression method for dose-response meta-analysis was used to estimate the odds ratio (OR) for carrying resistant bacteria during and following treatment compared to baseline. Probability density functions fitted to the resulting dose-response curves were then used to optimize the period during and/or after treatment when resistant pathogens were most likely to be identified.
RESULTS: Studies of Streptococcus pneumoniae treatment with β-lactam antibiotics demonstrated a peak in resistance prevalence among patients 4 days after completing treatment with a 3.32-fold increase in odds (95% confidence interval [CI], 1.71-6.46). Resistance waned more gradually than it emerged, returning to preexposure levels 1 month after treatment (OR, 0.98 [95% CI, .55-1.75]). Patient isolation during the peak dose-response period would be expected to reduce the risk that a transmitted pathogen is resistant equivalently to a 50% longer isolation window timed from the first day of treatment.
CONCLUSIONS: Predictable temporal dynamics of resistance levels have implications both for surveillance and control.
METHODS: Following a systematic review, a novel robust error meta-regression method for dose-response meta-analysis was used to estimate the odds ratio (OR) for carrying resistant bacteria during and following treatment compared to baseline. Probability density functions fitted to the resulting dose-response curves were then used to optimize the period during and/or after treatment when resistant pathogens were most likely to be identified.
RESULTS: Studies of Streptococcus pneumoniae treatment with β-lactam antibiotics demonstrated a peak in resistance prevalence among patients 4 days after completing treatment with a 3.32-fold increase in odds (95% confidence interval [CI], 1.71-6.46). Resistance waned more gradually than it emerged, returning to preexposure levels 1 month after treatment (OR, 0.98 [95% CI, .55-1.75]). Patient isolation during the peak dose-response period would be expected to reduce the risk that a transmitted pathogen is resistant equivalently to a 50% longer isolation window timed from the first day of treatment.
CONCLUSIONS: Predictable temporal dynamics of resistance levels have implications both for surveillance and control.