Leclerc Quentin, Clements Alastair, Dunn Helen, Hatcher James, Lindsay Jodi A, Grandjean Louis, Knight Gwenan M
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Public Health, London School of Hygiene & Tropical Medicine, UK.
Antimicrobial Resistance Centre, London School of Hygiene & Tropical Medicine, UK.
medRxiv. 2023 Feb 16:2023.02.15.23285946. doi: 10.1101/2023.02.15.23285946.
Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen . The reported prevalence of these resistances vary, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures. We explored AMR diversity in 70,000 isolates from a UK paediatric hospital between 2000-2020, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms, hospitalisation information, and antibiotic consumption. At the hospital-level, the proportion of isolates that were meticillin-resistant (MRSA) increased between 2014-2020 from 25 to 50%, before sharply decreasing to 30%, likely due to a change in inpatient demographics. Temporal trends in the proportion of isolates resistant to different antibiotics were often correlated in MRSA, but independent in meticillin-susceptible . Ciprofloxacin resistance in MRSA decreased from 70% to 40% of tested isolates between 2007-2020, likely linked to a national policy to reduce fluoroquinolone usage in 2007. At the patient level, we identified frequent AMR diversity, with 4% of patients ever positive for simultaneously carrying, at some point, multiple isolates with different resistances. We detected changes over time in AMR diversity in 3% of patients ever positive for . These changes equally represented gain and loss of resistance. Within this routinely collected dataset, we found that 65% of changes in resistance within a patient’s population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single clones.
在该病原体中已发现对所有抗生素类别的抗菌药物耐药性（AMR）。这些耐药性的报告流行率各不相同，受患者层面宿主内AMR进化以及医院层面宿主间传播的驱动。如果没有密集的纵向采样，利用常规监测数据对多个层面的AMR动态进行务实分析对于制定控制措施至关重要。我们利用包含每位患者多个常规收集的分离株的电子数据集，这些数据集具有表型抗菌谱、住院信息和抗生素消耗情况，对2000年至2020年期间一家英国儿科医院的70000株分离株中的AMR多样性进行了探索。在医院层面，耐甲氧西林（MRSA）分离株的比例在2014年至2020年期间从25%增加到50%，随后急剧下降至30%，这可能是由于住院患者人口结构的变化。在MRSA中，对不同抗生素耐药的分离株比例的时间趋势通常相互关联，但在甲氧西林敏感菌株中则相互独立。2007年至2020年期间，MRSA中对环丙沙星的耐药性从70%的测试分离株降至40%，这可能与2007年国家减少氟喹诺酮使用的政策有关。在患者层面，我们发现了频繁的AMR多样性，4%的患者曾检测出 呈阳性，在某些时候同时携带多种具有不同耐药性的分离株。我们在3%曾检测出 呈阳性的患者中检测到AMR多样性随时间的变化。这些变化同样代表了耐药性的获得和丧失。在这个常规收集的数据集中，我们发现患者群体中65%的耐药性变化无法用抗生素暴露或细菌在患者间的传播来解释，这表明通过频繁获得和丧失AMR基因在宿主内发生的进化可能是这些不断变化的AMR谱的原因。我们的研究强调了探索现有常规监测数据以确定AMR潜在机制的价值。这些见解可能会极大地增进我们对抗生素暴露变化的重要性以及单个 克隆成功情况的理解。
