Zhong Xiaomin, Pate Alexander, Yang Ya-Ting, Fahmi Ali, Ashcroft Darren M, Goldacre Ben, MacKenna Brian, Mehrkar Amir, Bacon Sebastian C J, Massey Jon, Fisher Louis, Inglesby Peter, Hand Kieran, van Staa Tjeerd, Palin Victoria
Centre for Health Informatics, School of Health Sciences, Faculty of Biology, Medicine, and Health, the University of Manchester, M13 9PL, UK.
Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK; NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9PL, UK.
J Infect. 2023 Jul;87(1):1-11. doi: 10.1016/j.jinf.2023.05.010. Epub 2023 May 12.
This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19.
With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection.
The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found.
Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information.
本研究旨在预测潜在不适当抗生素类型和重复开药的风险，并评估新冠疫情期间的变化。
在英国国家医疗服务体系（NHS）英格兰地区批准下，我们使用OpenSAFELY平台访问TPP SystmOne电子健康记录（EHR）系统，并选取了2019年至2021年期间开具抗生素处方的患者。多项逻辑回归模型预测了每种常见感染患者接受不适当抗生素类型或重复抗生素疗程的概率。
研究人群包括910万患者，共开具了2920万份抗生素处方。29.1%的处方被确定为重复开药。电子健康记录中记录有当日感染事件的患者重复开药率相当低（18.0%），8.6%的患者使用了潜在不适当的抗生素类型。未发现新冠疫情期间重复抗生素开药率有重大变化。在10个风险预测模型中，校准水平良好，区分度中等。
我们的研究没有发现新冠疫情开始后不适当或重复抗生素开药水平发生变化的证据。重复抗生素开药很常见，且因地区和患者特征而异。需要围绕抗生素治疗失败制定治疗指南，并为临床医生提供个性化的患者信息。
