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医院范围内计算机化方法对优化严重肥胖患者抗菌药物处方质量的影响:一项准实验研究。

Impact of a hospital-wide computerised approach to optimise the quality of antimicrobial prescriptions in patients with severe obesity: a quasi-experimental study.

机构信息

Department of Microbiology and Infectious Diseases, Université de Sherbrooke, 3001, 12e Avenue Nord, Sherbrooke, Québec, J1H 5N4, Canada.

Medical Division, Lumed Inc., Sherbrooke, Québec, J1H 5C7, Canada.

出版信息

BMC Infect Dis. 2021 Sep 18;21(1):972. doi: 10.1186/s12879-021-06682-8.

DOI:10.1186/s12879-021-06682-8
PMID:34537005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8449866/
Abstract

BACKGROUND

Rates of adherence to available recommendations for dose adjustments in patients with severe obesity are generally low. Hence, antimicrobials are often underdosed in these patients. Antimicrobial stewardship programmes can improve the use of antimicrobials in hospitalised patients. The aim of the study was to analyse the impact of an antimicrobial stewardship programme based on a computerised clinical decision support system for optimal dosing and antimicrobial use in inpatients with severe obesity.

METHODS

This quasi-experimental retrospective study using interrupted time series was conducted in an academic centre in Canada from August 2008 to June 2018. The Antimicrobial Prescription Surveillance System was implemented in August 2010 (intervention 1) and specific rules targeting patients with class III obesity (body mass index ≥ 40 kg/m) were added in June 2014 (intervention 2). Data were collected from all hospitalised adults receiving antimicrobials which required dose adjustment for severe obesity and were stratified by body mass index. Segmented regression analysis of interrupted time series was used to evaluate the impact of the Antimicrobial Prescription Surveillance System on the proportion of inappropriate days of therapy according to posology and on antimicrobial consumption.

RESULTS

Overall, 65 205 antimicrobial prescriptions (68% non-obese, 25% class I-II obesity, and 7% class III obesity) were analysed. In patients with class III obesity, the intervention was associated with a decrease in the proportion of inappropriate days of therapy (trend after the first intervention, -0.8% per 2-month period [95% CI -1.1 to -0.5], p < 0.001; intercept, 11.3% [95% CI 8.2 to 14.5], p < 0.001), which led to a reduction of 35% over an eight-year period (from pre-intervention level of 19.1%). Intervention 1 resulted in a downward trend in antimicrobial consumption, followed by an increasing trend after intervention 2. In these patients, the most frequent interventions made by pharmacists targeted posology (46%).

CONCLUSIONS

Antimicrobial Prescription Surveillance System had a positive impact on dosing optimisation and antimicrobial consumption in patients with class III obesity. Improving antimicrobial prescriptions in these patients is important because suboptimal dosing could be associated with unfavourable outcomes.

摘要

背景

严重肥胖患者对现有剂量调整建议的依从率通常较低。因此,这些患者的抗菌药物往往剂量不足。抗菌药物管理计划可以改善住院患者的抗菌药物使用。本研究的目的是分析基于计算机临床决策支持系统的抗菌药物管理计划对严重肥胖住院患者最佳剂量和抗菌药物使用的影响。

方法

本研究为加拿大某学术中心 2008 年 8 月至 2018 年 6 月期间进行的准实验回顾性研究。2010 年 8 月实施抗菌药物处方监测系统(干预 1),2014 年 6 月添加针对 III 类肥胖(体重指数≥40kg/m)患者的特定规则(干预 2)。从所有接受需要根据严重肥胖调整剂量的抗菌药物治疗的住院成人患者中收集数据,并根据体重指数分层。使用中断时间序列分段回归分析评估抗菌药物处方监测系统对根据方案和抗菌药物消耗计算的治疗不适当天数比例的影响。

结果

共分析了 65205 例抗菌药物处方(68%非肥胖患者,25% I-II 类肥胖患者,7% III 类肥胖患者)。在 III 类肥胖患者中,干预与治疗不适当天数比例降低相关(第一次干预后的趋势,每 2 个月降低 0.8%[95%CI-1.1 至-0.5],p<0.001;截距 11.3%[95%CI8.2 至 14.5],p<0.001),在八年期间减少了 35%(从干预前的 19.1%)。干预 1 导致抗菌药物消耗呈下降趋势,干预 2 后呈上升趋势。在这些患者中,药剂师最常进行的干预是调整剂量(46%)。

结论

抗菌药物处方监测系统对 III 类肥胖患者的剂量优化和抗菌药物消耗产生了积极影响。改善这些患者的抗菌药物处方非常重要,因为剂量不足可能与不良结局相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f2b/8449866/5e26f96971d9/12879_2021_6682_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f2b/8449866/4837663a4ee2/12879_2021_6682_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f2b/8449866/5e26f96971d9/12879_2021_6682_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f2b/8449866/4837663a4ee2/12879_2021_6682_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f2b/8449866/5e26f96971d9/12879_2021_6682_Fig2_HTML.jpg

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