PDF(Pubmed)
Abstract：
BACKGROUND: The digitalization of information systems allows automatic measurement of antimicrobial consumption (AMC), helping address antibiotic resistance from inappropriate drug use without compromising patient safety.
OBJECTIVE: Describe and characterize a new automated AMC surveillance service for intensive care units (ICUs), with data stratified by referral clinic and linked with individual patient risk factors, disease severity, and mortality.
METHODS: An automated service collecting data from the electronic medical record was developed, implemented, and validated in a healthcare region in northern Sweden. We performed an observational study from January 1, 2018, to December 31, 2021, encompassing general ICU care for all ≥18-years-olds in a catchment population of 270000 in secondary care and 900000 in tertiary care. We used descriptive analyses to associate ICU population characteristics with AMC outcomes over time, including days of therapy (DOT), length of therapy, defined daily doses, and mortality.
RESULTS: There were 5608 admissions among 5190 patients with a median age of 65 (IQR 48-75) years, 41.2% females. The 30-day mortality was 18.3%. Total AMC was 1177 DOTs in secondary and 1261 DOTs per 1000 patient days and tertiary care. AMC varied significantly among referral clinics, with the highest total among 810 general surgery admissions in tertiary care at 1486 DOTs per 1000 patient days. Case-mix effects on the AMC were apparent during COVID-19 waves highlighting the need to account for case-mix. Patients exposed to more than three antimicrobial drug classes (N = 242) had a 30-day mortality rate of 40.6%, with significant variability in their expected rates based on admission scores.
CONCLUSIONS: We introduce a new service and instructions for automating local ICU-AMC data collection. The versatile long-term ICU-AMC metrics presented, covering patient factors, referral clinics and mortality outcomes, are expected to be beneficial in refining antimicrobial drug use.
OBJECTIVE: Describe and characterize a new automated AMC surveillance service for intensive care units (ICUs), with data stratified by referral clinic and linked with individual patient risk factors, disease severity, and mortality.
METHODS: An automated service collecting data from the electronic medical record was developed, implemented, and validated in a healthcare region in northern Sweden. We performed an observational study from January 1, 2018, to December 31, 2021, encompassing general ICU care for all ≥18-years-olds in a catchment population of 270000 in secondary care and 900000 in tertiary care. We used descriptive analyses to associate ICU population characteristics with AMC outcomes over time, including days of therapy (DOT), length of therapy, defined daily doses, and mortality.
RESULTS: There were 5608 admissions among 5190 patients with a median age of 65 (IQR 48-75) years, 41.2% females. The 30-day mortality was 18.3%. Total AMC was 1177 DOTs in secondary and 1261 DOTs per 1000 patient days and tertiary care. AMC varied significantly among referral clinics, with the highest total among 810 general surgery admissions in tertiary care at 1486 DOTs per 1000 patient days. Case-mix effects on the AMC were apparent during COVID-19 waves highlighting the need to account for case-mix. Patients exposed to more than three antimicrobial drug classes (N = 242) had a 30-day mortality rate of 40.6%, with significant variability in their expected rates based on admission scores.
CONCLUSIONS: We introduce a new service and instructions for automating local ICU-AMC data collection. The versatile long-term ICU-AMC metrics presented, covering patient factors, referral clinics and mortality outcomes, are expected to be beneficial in refining antimicrobial drug use.
摘要：
背景：信息系统的数字化允许自动测量抗菌剂的消耗量（AMC），在不影响患者安全的情况下，帮助解决因不适当药物使用而产生的抗生素耐药性。
目的：描述并描述一种用于重症监护病房（ICU）的新的自动AMC监视服务，根据转诊诊所的数据进行分层，并与个体患者的危险因素相关联，疾病严重程度,和死亡率。
方法：开发了一种从电子病历中收集数据的自动化服务，已实施，并在瑞典北部的医疗保健地区进行了验证。我们从2018年1月1日至2021年12月31日进行了一项观察性研究，包括对所有≥18岁的人群的一般ICU护理，在二级护理和三级护理的流域人口分别为270000和900000。我们使用描述性分析将ICU人群特征与AMC结果随着时间的推移联系起来，包括治疗天数（DOT），治疗的长度，定义的每日剂量，和死亡率。
结果：5190例患者中，有5608例入院，中位年龄为65岁（IQR48-75），女性占41.2%。30天死亡率为18.3%。总AMC为1177个DOT，二级和1261个DOT，每1000个患者天和三级护理。AMC在转诊诊所之间差异很大，在接受三级护理的810例普外科手术中，每1000例患者天1486例DOT的总入院人数最高。在COVID-19波期间，病例混合对AMC的影响很明显，这突出了需要考虑病例混合。暴露于三种以上抗菌药物类别（N=242）的患者30天死亡率为40.6％，根据入院分数，他们的预期比率存在显著差异。
结论：我们引入了一项新的服务和说明，用于自动化本地ICU-AMC数据收集。提出了通用的长期ICU-AMC指标，涵盖患者因素，转诊诊所和死亡率结果，有望有利于完善抗菌药物的使用。
目的：描述并描述一种用于重症监护病房（ICU）的新的自动AMC监视服务，根据转诊诊所的数据进行分层，并与个体患者的危险因素相关联，疾病严重程度,和死亡率。
方法：开发了一种从电子病历中收集数据的自动化服务，已实施，并在瑞典北部的医疗保健地区进行了验证。我们从2018年1月1日至2021年12月31日进行了一项观察性研究，包括对所有≥18岁的人群的一般ICU护理，在二级护理和三级护理的流域人口分别为270000和900000。我们使用描述性分析将ICU人群特征与AMC结果随着时间的推移联系起来，包括治疗天数（DOT），治疗的长度，定义的每日剂量，和死亡率。
结果：5190例患者中，有5608例入院，中位年龄为65岁（IQR48-75），女性占41.2%。30天死亡率为18.3%。总AMC为1177个DOT，二级和1261个DOT，每1000个患者天和三级护理。AMC在转诊诊所之间差异很大，在接受三级护理的810例普外科手术中，每1000例患者天1486例DOT的总入院人数最高。在COVID-19波期间，病例混合对AMC的影响很明显，这突出了需要考虑病例混合。暴露于三种以上抗菌药物类别（N=242）的患者30天死亡率为40.6％，根据入院分数，他们的预期比率存在显著差异。
结论：我们引入了一项新的服务和说明，用于自动化本地ICU-AMC数据收集。提出了通用的长期ICU-AMC指标，涵盖患者因素，转诊诊所和死亡率结果，有望有利于完善抗菌药物的使用。
