Healing soles: a microbiology-driven electronic health record-algorithm and order set to decrease antipseudomonal use in diabetic foot infections, a retrospective, observational, quasi-experimental study.

Antimicrobial stewardship & healthcare epidemiology : ASHE Pub Date : 2025-03-27 eCollection Date: 2025-01-01 DOI:10.1017/ash.2025.59
Antoinette Marie Acbo, Naida Koura-Mola, Terrence McSweeney, Hongkai Bao, Mei Chang, Kelsie Cowman, Priya Nori, Yi Guo
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Abstract

Background: Antipseudomonal antibiotics are commonly prescribed for diabetic foot infections (DFI) at our institution despite a low local prevalence of Pseudomonas aeruginosa. A multidisciplinary team implemented a DFI electronic health record (EHR)-embedded treatment algorithm and order set.

Methods: This multi-center, quasi-experimental study evaluated adults on antibiotics admitted for DFI to vascular surgery or medical units pre- and post-implementation of an EHR-embedded treatment algorithm and order set. Exclusion criteria: duplicate patients, concomitant infection, transfer from an outside hospital. Primary endpoint: antipseudomonal use among included patients (DOT/1000 DFI days present). Secondary outcomes: empiric antipseudomonal use, length of stay, 30-day readmission, mortality, amputation, and Clostridioides difficile infection. Patient demographics, diagnostics, treatments, and outcomes were evaluated.

Results: Two hundred ten patients were included with 70 patients included in each group. The post-algorithm group had lower antipseudomonal DOT/1000 DFI days present compared to the pre-intervention group (360 vs 503, P < 0.001). The post-order set group had the lowest antipseudomonal use (347 vs 503, P < 0.001). Empiric antipseudomonal use decreased from 85.7% pre-intervention to 72% post-algorithm and 68.5% post-order set. Collectively, 30-day mortality was < 5%. Amputation during and within 30 days of hospitalization was similar in the pre-intervention (48.6%), post-algorithm (30%), and post-order set (41.4%) groups. Methicillin-susceptible Staphylococcus aureus and Streptococcus spp. were most frequently isolated. Wound cultures were not collected in 24.3%, 22.9%, and 40% of the pre-intervention, post-algorithm, and post-order set group.

Conclusions: EHR-embedded clinical decision-making tools reduce antipseudomonal use for DFI treatment without increasing 30-day mortality or amputation.

愈合鞋底:微生物驱动的电子健康记录算法和顺序设置,以减少糖尿病足感染的抗假单孢菌使用,回顾性,观察性,准实验研究。
背景:尽管铜绿假单胞菌在当地的流行率很低,但在我院,抗假单胞菌抗生素是糖尿病足感染(DFI)的常用处方。一个多学科团队实施了糖尿病足感染电子病历(EHR)嵌入式治疗算法和医嘱集:这项多中心、准实验性研究对血管外科或内科因 DFI 而使用抗生素的成人进行了评估,包括实施 EHR 嵌入式治疗算法和医嘱集前后的情况。排除标准:重复患者、合并感染、从外院转入。主要终点:纳入患者的抗伪药使用情况(DOT/1000 DFI 天数)。次要结局:经验性抗伪药使用率、住院时间、30 天再入院率、死亡率、截肢率和艰难梭菌感染率。对患者的人口统计学、诊断、治疗和结果进行了评估:结果:共纳入 210 名患者,每组 70 名。与干预前相比,算法后组的抗伪菌 DOT/1000 DFI 天数较低(360 vs 503,P < 0.001)。排序后组的抗伪药使用量最低(347 对 503,P < 0.001)。经验性抗伪药使用率从干预前的 85.7% 降至算法后的 72%,顺序设定后的 68.5%。总的来说,30 天死亡率小于 5%。干预前(48.6%)、算法后(30%)和订单组后(41.4%)组在住院期间和住院 30 天内的截肢率相似。最常分离出的是甲氧西林敏感的金黄色葡萄球菌和链球菌。24.3%、22.9% 和 40% 的干预前、算法后和订单设置后组均未收集伤口培养物:结论:电子病历嵌入式临床决策工具减少了 DFI 治疗中抗假名药物的使用,但不会增加 30 天死亡率或截肢率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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