Effects of a Collaborative, Community Hospital Network for Antimicrobial Stewardship Program Implementation

Park

Antimicrob Resist Infect Control

et al. 2022

Background Although non-teaching community hospitals form the majority of healthcare providers in South Korea, there is limited data on antibiotic usage in them. To evaluate the pattern of antibiotic usage and its appropriateness in hospitals with < 400 beds in South Korea. Methods A multicentre retrospective study was conducted in 10 hospitals (six long-term care hospitals, three acute care hospitals, and one orthopaedic hospital), with < 400 beds in South Korea. We analysed patterns of antibiotic prescription in 2019, and their appropriateness in the participating hospitals. For the evaluation of the appropriateness of the prescription, 25 patients under antibiotic therapy were randomly selected at each hospital, over two separate periods. Due to the heterogeneity of their characteristics, the orthopaedics hospital was excluded from the analysis. Results The most commonly prescribed antibiotics in long-term care hospitals was fluoroquinolone, followed by beta-lactam/beta-lactamase inhibitor (anti-pseudomonal). In acute care hospitals, these were third generation cephalosporin, followed by first generation cephalosporin, and second generation cephalosporin. The major antibiotics that were prescribed in the orthopedics hospital was first generation cephalosporin Only 2.3% of the antibiotics were administered inappropriately. In comparison, 15.3% of patients were prescribed an inappropriate dose. The proportion of inappropriate antibiotic prescriptions was 30.6% of the total antibiotic prescriptions. Conclusions The antibiotic usage patterns vary between non-teaching community hospitals in South Korea. The proportion of inappropriate prescriptions exceeded 30% of the total antibiotic prescriptions.

Section: Discussionmentioning

confidence: 99%

Prescriptions patterns and appropriateness of usage of antibiotics in non-teaching community hospitals in South Korea: a multicentre retrospective study

Park

Antimicrob Resist Infect Control

et al. 2022

Open Forum Infectious Diseases

“…This level of centralized ID involvement was beyond what we used in our model. Unlike the DASON experience [ 16 ], our model exists within a single health system, so we were able to access the shared EMR and discuss or intervene on active inpatients when needed because local hospital credentialing and malpractice insurance coverage were not barriers within our own health system.…”

Section: Discussionmentioning

confidence: 99%

“…Several health systems have reported their approach and initial results from implementing AS at a system-wide level [ 11–13 ]. Experts at Intermountain Healthcare demonstrated community hospital success by incorporating centralized ID involvement into daily AS activities at system hospitals [ 14 , 15 ], whereas the Duke Antimicrobial Stewardship Outreach Network (DASON) demonstrated successful improvements in antimicrobial usage at community hospitals outside of a single health system through collaboration with their independent team of ID and AS experts [ 16 ]. However, given variability between health systems with regard to size, staffing, degree of centralized control and oversight, and individual hospital characteristics, further study is needed into best approaches for system-wide stewardship.…”

mentioning

confidence: 99%

Development of a Centralized Antimicrobial Stewardship Program Across a Diverse Health System and Early Antimicrobial Usage Trends

Khadem

Nguyen

Mellors

et al. 2022

Background Expanding antimicrobial stewardship to community hospitals is vital and now required by regulatory agencies. UPMC instituted the Centralized Health system Antimicrobial Stewardship Efforts (CHASE) Program to expand antimicrobial stewardship to all UPMC hospitals regardless of local resources. For hospitals with few local stewardship resources, we utilized a model integrating local non-ID trained pharmacists with centralized ID experts. Methods Thirteen hospitals were included. Eleven were classified as robust (4) or non-robust (7) depending on local stewardship resources and fulfillment of CDC core elements of hospital antimicrobial stewardship. In addition to general stewardship oversight at all UPMC hospitals, the centralized team interacted regularly with non-robust hospitals for individual patient reviews and local projects. We compared inpatient antimicrobial usage rates at non-robust vs. robust hospitals and at 2 UPMC academic medical centers. Results The CHASE Program expanded in scope between 2018-2020. During this period, antimicrobial usage at these 13 hospitals decreased by 16% with a monthly change of -4.7 DOT/1000 PD (95% CI -5.5 to -3.9, p<0.0001). Monthly decrease at non-robust hospitals was -3.3 DOT/1000 PD per month (-4.5 to -2.0, p<0.0001), similar to rates of decline at both robust hospitals (-3.3 DOT/1000 PD) and academic medical centers (-4.8 DOT/1000 PD) (p=0.167). Conclusions Coordinated antimicrobial stewardship can be implemented across a large and diverse health system. Our hybrid model incorporating a central team of experts with local community hospital pharmacists led to usage decreases over 3 years at a rate comparable to that seen in larger hospitals with more established stewardship programs.

“…To estimate the patient contact networks, we used data from the Duke Antimicrobial Stewardship Outreach Network (DASON) 20 and Duke Health System which contained curated hospital encounter records using uniform definitions for 24 community hospitals and one academic medical center in the Southeastern United States between October 2015 and December 2017. This data is made up of 299 total units across all the hospitals, ranging from 4 units at the smallest hospital to 30 units at the largest.…”

Section: Patient Datamentioning

confidence: 99%

“…The mixing patterns for some other units, such as Operating Room Suites and 24-Hour Observation, showed wide variation between hospitals, representing differing catchment populations and patient mixes. Outpatient units, such as Emergency Departments (Figure 3C) and Behavioral Health (Figure 3D) units had mixing between broager age ranges, with a larger proportion of the mixing between patients in the [20,50] age range. In addition, the ED also showed mixing between adult and child age groups.…”

Section: Mixing Matrix By Agementioning

confidence: 99%

Who Is Hospitalized With Whom? Inpatient Contact Networks and Mixing Patterns

Madhobi

Kalyanaraman

Anderson

et al. 2022

Preprint

Importance: Person-to-person contact is important for the transmission of healthcare-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission. Objective: Generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units. Design, Setting, and Participants: The study was conducted in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network (DASON) between January 2015 and December 2017. There were a total of 1,569,413 patients and 299 hospital units. Main Outcome and Measures: The mixing matrices of patients for each hospital unit using age, Elixhauser Score, and a measure of antibiotic exposure. Results: Mixing matrices were calculated from a database of 24 hospitals, which included 2.9 million admission records for nearly 1.6 million patients. Some units had highly similar patterns across multiple hospitals although the number of patients might vary to a great extent. Within a period of 26 months (October 2015 and December 2017), the highest daily average is 765 patients in the ED of Hospital-12 and lowest daily average is only 2 patients in some of the smaller hospital units. For most of the adult inpatient units, frequent mixing was observed for older adult groups while outpatient units e.g. ED and Behavioral Health etc. units showed mixing between different age groups. From the mixing matrices by Elixhauser Score, we observed mixing between patients with relatively higher comorbidity index on the ICUs. Mixing matrices by Antibiotic Rank, a 4-point scale based on priority for antibiotic stewardship programs, resulted in six major distinct patterns due to the variation of the type of antibiotics used in different units. Conclusions and Relevance: The mixing patterns of patients both within and between hospitals followed broadly expected patterns, though with a considerable amount of heterogeneity. These patterns can be used to evaluate the appropriateness of policies and guidelines for smaller community hospitals, as well as improve the design of interventions that rely on altering patient contact patterns.