Effective Antimicrobial StewaRdship StrategIES (ARIES): Cluster Randomized Trial of Computerized Decision Support System and Prospective Review and Feedback

Antibiotic utilisation and resistance over the first decade of nationally funded antimicrobial stewardship programmes in Singapore acute-care hospitals

OBJECTIVE

The aim of this study was to describe the time series of broad-spectrum antibiotic utilisation and incidence of antibiotic-resistant organisms during the implementation of antimicrobial stewardship programmes (ASP) in Singapore.

METHODS

An observational study was conducted using data from 2011 to 2020 in seven acute-care public hospitals. We applied joinpoint regressions to investigate changes in antibiotic utilisation rate and incidence density of antibiotic-resistant organisms.

RESULTS

Across the seven hospitals, quarterly broad-spectrum antibiotic utilisation rate remained stable. Half-yearly incidence density of antibiotic-resistant organisms with two joinpoints at first half (H1) of 2012 and second half (H2) of 2014 decreased significantly in the second and third period with a half-yearly percentage change (HPC) of -2.9% and - 0.5%, respectively. Across the five hospitals with complete data, half-yearly broad-spectrum antibiotic utilisation rate with one joinpoint decreased significantly from H1 of 2011 to H2 of 2018 (HPC - 4.0%) and H2 of 2018 to H2 2020 (HPC - 0.5%). Incidence density of antibiotic-resistant organisms decreased significantly in the two joinpoint periods from H1 of 2012 to H2 of 2014 (HPC - 2.7%) and H2 of 2014 to H2 of 2020 (HPC - 1.0%). Ceftriaxone with one joinpoint decreased significantly from H1 of 2011 to H1 of 2014 (HPC - 6.0%) and H1 of 2014 to H2 of 2020 (HPC - 1.8%) and ceftriaxone-resistant E. coli and K. pneumoniae decreased significantly in later periods, from H2 of 2016 to H2 of 2020 (HPC - 2.5%) and H1 of 2012 to H2 of 2015 (HPC - 4.6%) respectively. Anti-pseudomonal antibiotics with one joinpoint decreased significantly from H1 of 2011 to H2 of 2014 (HPC - 4.5%) and H2 of 2014 to H2 of 2020 (HPC - 0.8%) and that of quinolones with one joinpoint at H1 of 2015 decreased significantly in the first period. C. difficile with one joinpoint increased significantly from H1 of 2011 to H1 of 2015 (HPC 3.9%) and decreased significantly from H1 of 2015 to H2 of 2020 (HPC - 4.9%).

CONCLUSIONS

In the five hospitals with complete data, decrease in broad-spectrum antibiotic utilisation rate was followed by decrease in incidence density of antibiotic-resistant organisms. ASP should continue to be nationally funded as a key measure to combat antimicrobial resistance in acute care hospitals.

Exploring Antimicrobial Stewardship Influential Interventions on Improving Antibiotic Utilization in Outpatient and Inpatient Settings: A Systematic Review and Meta-Analysis

Antimicrobial stewardship interventions are targeted efforts by healthcare organizations to optimize antimicrobial use in clinical practice. The study aimed to explore effective interventions in improving antimicrobial use in hospitals. Literature was systemically searched for interventional studies through PubMed, CINAHL, and Scopus databases that were published in the period between January 2010 to April 2022. A random-effects model was used to pool and evaluate data from eligible studies that reported antimicrobial stewardship (AMS) interventions in outpatient and inpatient settings. Pooled estimates presented as proportions and standardized mean differences. Forty-eight articles were included in this review: 32 in inpatient and 16 in outpatient settings. Seventeen interventions have been identified, and eight outcomes have been targeted. AMS interventions improved clinical, microbiological, and cost outcomes in most studies. When comparing non-intervention with intervention groups using meta-analysis, there was an insignificant reduction in length of stay (MD: -0.99; 95% CI: -2.38, 0.39) and a significant reduction in antibiotics' days of therapy (MD: -2.73; 95% CI: -3.92, -1.54). There were noticeable reductions in readmissions, mortality rates, and antibiotic prescriptions post antimicrobial stewardship multi-disciplinary team (AMS-MDT) interventions. Studies that involved a pharmacist as part of the AMS-MDT showed more significant improvement in measured outcomes than the studies that did not involve a pharmacist.

Sustaining Antimicrobial Stewardship in a High-Antibiotic Resistance Setting

IMPORTANCE

There is a lack of studies comparing the intended and unintended consequences of prospective review and feedback (PRF) with computerized decision support systems (CDSS), especially in the longer term in antimicrobial stewardship.

OBJECTIVE

To examine the outcomes associated with the sequential implementation of PRF and CDSS and changes to these interventions with long-term use of antibiotics for and incidence of multidrug resistant organisms (MDROs) and other unintended outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used an interrupted time series with segmented regression analysis of data from January 2007 to December 2018. Data were extracted from the electronic medical records of patients admitted at a large university teaching hospital with high rates of antibiotic resistance in Singapore. Data were analyzed from June 2019 to June 2020.

EXPOSURES

PRF of piperacillin-tazobactam and carbapenems (intervention 1, April 2009), with the addition of hospital-wide CDSS (intervention 2, April 2011), and lifting of CDSS for half of the hospital wards for 6 months (intervention 3, March 2017).

MAIN OUTCOMES AND MEASURES

Monthly antimicrobial use was measured in defined daily doses (DDDs) per 1000 patient-days. The monthly incidence of MDROs was calculated as number of clinical isolates detected per 1000 inpatient-days over a 6-month period. Unintended outcomes examined included in-hospital mortality and age-adjusted length of stay (LOS).

RESULTS

The number of inpatients increased from 56 263 in 2007 to 63 572 in 2018. During the same period, the mean monthly patient days increased from 33 929 in 2007 to 45 603 in 2018, and the proportion of patients older than 65 years increased from 45.5% in 2007 to 56.6% in 2018. After intervention 1, there were 0.33 (95% CI, 0.18 to 0.48) more DDDs per 1000 patient-days per month of piperacillin-tazobactam and carbapenems and -11.05 (95% CI, -15.55 to -6.55) fewer DDDs per 1000 patient-days per month for other broad-spectrum antibiotics. After intervention 2, there were -0.22 (95% CI, -0.33 to -0.10) fewer DDDs per 1000 patient-days per month of piperacillin-tazobactam and carbapenems and -2.10 (95% CI, -3.13 to -1.07) fewer DDDs per 1000 patient-days per month for other broad-spectrum antibiotics. After intervention 3, use of piperacillin-tazobactam and carbapenem increased by 0.28 (95% CI, 0.02 to 0.55) DDDs per 1000 patient-days per month. After intervention 2, incidence of Clostridioides difficile decreased (estimate, -0.02 [95% CI, -0.03 to -0.01] cases per 1000 patient-days per month).

CONCLUSIONS AND RELEVANCE

In this cohort study, concurrent PRF and CDSS were associated with limiting the use of piperacillin-tazobactam and carbapenems while reducing use of other antibiotics.

Machine Learning and Antibiotic Management

Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.

Effects of coronavirus disease 2019 (COVID-19) pandemic on antimicrobial prevalence and prescribing in a tertiary hospital in Singapore

Background

The deployment of antimicrobial stewardship (AMS) teams to deal with the COVID-19 pandemic can lead to a loss of developed frameworks, best practices and leadership resulting in adverse impact on antimicrobial prescribing and resistance. We aim to investigate effects of reduction in AMS resources during the COVID-19 pandemic on antimicrobial prescribing.

Methods

One of 5 full-time equivalent AMS pharmacists was deployed to support pandemic work and AMS rounds with infectious disease physicians were reduced from 5 to 2 times a week. A survey in acute inpatients was conducted using the Global Point Prevalence Survey methodology in July 2020 and compared with those in 2015 and 2017–2019.

Results

The prevalence of antimicrobial prescribing (55% in 2015 to 49% in 2019 and 47% in 2020, p = 0.02) and antibacterials (54% in 2015 to 45% in 2019 and 42% in 2020, p < 0.01) have been reducing despite the pandemic. Antimicrobial prescribing in infectious disease wards with suspected or confirmed COVID-19 cases was 29% in 2020. Overall, antimicrobial prescribing quality indicators continued to improve (e.g. reasons in notes, 91% in 2015 to 94% in 2019 and 97% in 2020, p < 0.01) or remained stable (compliance to guideline, 71% in 2015 to 62% in 2019 and 73% in 2020, p = 0.08).

Conclusion

During the COVID-19 pandemic, there was no increase in antimicrobial prescribing and no significant differences in antimicrobial prescribing quality indicators.