Fecal Microbiota Transplantation for multidrug-resistant organism: Efficacy and Response prediction

粪便细菌疗法 多重耐药 粪便 移植 微生物学 有机体 生物 医学 免疫学 抗生素 内科学 艰难梭菌 遗传学
作者
Hye Seong,Sang Kil Lee,Jae Hee Cheon,Dong Yong,Hong Koh,Yunkoo Kang,Woo Young Jeong,Woon Ji Lee,Yujin Sohn,Yunsuk Cho,Jong Hoon Hyun,Yae Jee Baek,Moo Hyun Kim,Jung Ho Kim,Jin Young Ahn,Nam Su Ku,Su Jin Jeong,Joon‐Sup Yeom,Min Seok Cho,Je Hee Lee
出处
期刊:Journal of Infection [Elsevier BV]
卷期号:81 (5): 719-725 被引量:43
标识
DOI:10.1016/j.jinf.2020.09.003
摘要

Abstract

Objectives

The increasing prevalence of multidrug-resistant microorganisms (MDRO) is increasing the frequency of poor clinical outcomes, prolonging hospitalizations, and raising healthcare costs. This study evaluated the eradication efficacy of fecal microbiota transplantation (FMT) and identified microbial and functional biomarkers of MDRO decolonization.

Methods

Fecal solution obtained from healthy unrelated donors was infused in the participants' guts which had been colonized with carbapenemase-producing enterobacteriacea (CPE), vancomycin-resistant enterococci (VRE), or both CPE and VRE. Fecal samples from recipients were collected and microbiome changes before and after FMT were assessed.

Results

Twenty-four (68.6%) out of 35 patients were decolonized within one year of receiving FMT. Multivariate analysis showed that FMT (FMT: hazard ratio (HR) = 5.343, 95% confidence interval (CI) = 1.877-15.212, p = 0.002) and MDRO types (CPE: HR = 11.146, 95% CI = 2.420-51.340, p = 0.002; CPE/VRE: HR = 2.948, 95% CI = 1.200-7.246, p = 0.018; VRE served as the reference) were significant independent factors associated with time to decolonization. Microbiota analysis showed higher richness and biodiversity before FMT resulted in VRE decolonization. The species Clostridium ramosum and the genuses Anaerostipes and Eisenbergiella could serve as taxonomic biomarkers and K02017 could serve as a functional biomarker for VRE clearance.

Conclusion

FMT is an effective way to decolonize MDRO and its effectiveness may be predicted by microbiome analysis.
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