Yang Ya-Ting, Wong David, Zhong Xiaomin, Fahmi Ali, Ashcroft Darren M, Hand Kieran, Massey Jon, Mackenna Brian, Mehrkar Amir, Bacon Sebastian, Goldacre Ben, Palin Victoria, van Staa Tjeerd
Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, UK.
Leeds Institute of Health Sciences, The University of Leeds, Leeds LS2 9JT, UK.
Antibiotics (Basel). 2024 Jun 18;13(6):566. doi: 10.3390/antibiotics13060566.
Previous studies have demonstrated the association between antibiotic use and severe COVID-19 outcomes. This study aimed to explore detailed antibiotic exposure characteristics among COVID-19 patients. Using the OpenSAFELY platform, which integrates extensive health data and covers 40% of the population in England, the study analysed 3.16 million COVID-19 patients with at least two prior antibiotic prescriptions. These patients were compared to up to six matched controls without hospitalisation records. A machine learning model categorised patients into ten groups based on their antibiotic exposure history over the three years before their COVID-19 diagnosis. The study found that for COVID-19 patients, the total number of prior antibiotic prescriptions, diversity of antibiotic types, broad-spectrum antibiotic prescriptions, time between first and last antibiotics, and recent antibiotic use were associated with an increased risk of severe COVID-19 outcomes. Patients in the highest decile of antibiotic exposure had an adjusted odds ratio of 4.8 for severe outcomes compared to those in the lowest decile. These findings suggest a potential link between extensive antibiotic use and the risk of severe COVID-19. This highlights the need for more judicious antibiotic prescribing in primary care, primarily for patients with higher risks of infection-related complications, which may better offset the potential adverse effects of repeated antibiotic use.
先前的研究已经证明了抗生素使用与严重新冠病毒病（COVID-19）结局之间的关联。本研究旨在探索COVID-19患者详细的抗生素暴露特征。该研究使用了OpenSAFELY平台，该平台整合了大量健康数据，覆盖了英格兰40%的人口，分析了316万例有至少两份既往抗生素处方的COVID-19患者。将这些患者与多达六名无住院记录的匹配对照进行比较。一个机器学习模型根据患者在COVID-19诊断前三年的抗生素暴露史将其分为十组。研究发现，对于COVID-19患者，既往抗生素处方总数、抗生素类型多样性、广谱抗生素处方、首次和末次使用抗生素之间的时间间隔以及近期抗生素使用与严重COVID-19结局风险增加相关。抗生素暴露处于最高十分位数的患者与最低十分位数的患者相比，严重结局的调整比值比为4.8。这些发现表明广泛使用抗生素与严重COVID-19风险之间存在潜在联系。这凸显了在初级保健中更明智地开具抗生素处方的必要性，主要针对感染相关并发症风险较高的患者，这可能更好地抵消反复使用抗生素的潜在不良影响。
