The Standardized Antimicrobial Administration Ratio: A New Metric for Measuring and Comparing Antibiotic Use

Santen, Katharina L. van; Edwards, Jonathan R.; Webb, Amy K; Pollack, Lori A.; O’Leary, Erin; Neuhauser, Melinda M.; Srinivasan, Arjun; Pollock, Daniel A.

doi:10.1093/cid/ciy075

Cited by 80 publications

(76 citation statements)

References 13 publications

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“…ASPs within hospitals utilize the defined daily doses (DDDs) per patient day to evaluate antimicrobial consumption for specific drugs [5,6]. Although DDDs per patient day can be useful, this indicator cannot be used to make comparisons within and across hospitals in the absence of risk adjustment [7,8]. However, no indicator in the presence of risk adjustment is available to compare carbapenem consumption in Japan.…”

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confidence: 99%

“…A high difference value between predicted and observed consumption may indicate excessive/insufficient carbapenem use. The Centers for Disease Control and Prevention National Healthcare Safety Network developed the Standardized Antimicrobial Administration Ratio (SAAR) [8] that stratified consumption according to antimicrobial category and location type. This metric was developed based on the Antimicrobial Use and Resistance Module utilized in the United States.…”

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confidence: 99%

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Development of the predicted and standardized carbapenem usage metric: Analysis of the Japanese Diagnosis Procedure Combination payment system data

Maeda

Anno

Sawa

et al. 2020

Journal of Infection and Chemotherapy

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confidence: 99%

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confidence: 99%

Development of the predicted and standardized carbapenem usage metric: Analysis of the Japanese Diagnosis Procedure Combination payment system data

Maeda

Anno

Sawa

et al. 2020

Journal of Infection and Chemotherapy

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“…Strong evidence supports the use of feedback to prescribers [2,47], but feedback needs to be relevant, reliable and timely to in uence prescribing behaviour [48]. Further research is needed to statistically adjust those measures for case mix in the same way as consumption measures [49]. User-centred research [50] is also needed to tailor these measures to individual clinical teams, or to enable AMS teams or hospital managers to monitor speci c prescribing behaviours across hospitals.…”

Section: Discussionmentioning

confidence: 99%

Towards Data-Driven Hospital Antimicrobial Stewardship: Secondary use of Routine Electronic Care and Prescribing Records to Inform Antimicrobial Stewardship Programmes

Dutey-Magni¹,

Gill²,

McNulty³

et al. 2020

Preprint

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Background Hospital antimicrobial stewardship (AMS) programmes are multidisciplinary initiatives to optimise the use of antimicrobials. Most AMS programmes rely on time-consuming manual audits to monitor clinicians’ antibiotic prescribing practice. Much of the information needed is already contained within electronic health records (EHRs) and could be used to inform AMS programmes. The objective of this study was to evaluate the feasibility of building analytics from hospital EHRs to facilitate AMS interventions. Methods This study used electronic prescribing, laboratory and clinical coding records of adult patients admitted to six specialties at Queen Elizabeth Hospital, Birmingham, UK between September 2017 and August 2018 (n = 61,679 admissions). Duration of prescribing, compliance with clinical guidelines, and timing of switch from intravenous to oral antibiotics in relation to clinical progression were assessed. Outputs were visualised at ward, specialty and consultant levels. Results A total of 59,884 antibacterial prescription records were linked into 24,653 therapy episodes. Among the 35% of admissions prescribed an antibacterial, the mean days of therapy per admission was 4.3 days and 9.9 days in elective and emergency hospitalisations respectively. Twenty-two percent (207/997) of therapy episodes for low-severity community-acquired pneumonia were initiated with the antibiotic recommended by locally-approved prescribing guidelines. Data suggested average delays in switching from intravenous to oral therapy of up to 3.6 days [95% CI: 3.4; 3.7]. Microbial cultures were performed prior to treatment initiation in just 22% of antibacterial prescriptions. Conclusions Hospital EHRs can be used to construct meaningful measures of antibiotic stewardship, demonstrating potential value for audit and feedback interventions.

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“…The SAAR provides an observed‐to‐expected ratio and allows a facility or unit to compare to the risk‐adjusted national baseline. Current SAAR risk adjustment models (van Santen et al ) include a limited number of facility‐ and unit‐level predictors such as bed size and location type (ICU or ward; medical, medical surgical or surgical). The literature on benchmarking AU describes a few patient‐ and hospital‐level predictors derived from academic medical centres participating in the University Health Consortium (now Vizient, Inc.) (Pakyz et al ; Polk et al ; Ibrahim and Polk ), large administrative databases (MacDougall and Polk ), standalone padiatric hospitals (Gerber et al ) and a single health system (Stenehjem et al ).…”

Section: Introductionmentioning

confidence: 99%

Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach

et al. 2019

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Aims Analysis and tracking of antimicrobial utilization (AU) are crucial in antimicrobial stewardship efforts which are used to find effective interventions for controlling antimicrobial resistance. In antimicrobial stewardship, standard risk adjustment models are needed for benchmarking appropriate AU and for fair inter‐facility comparison. In this study we identify patient‐ and facility‐level predictors of antimicrobial usage in hospitalized patients using a machine learning approach, which can be used to inform a risk adjustment model to facilitate assessment of AU. To our knowledge, this is the first time machine learning has been applied for this purpose. Methods and Results Patient admission records were retrieved from the Duke Antimicrobial Stewardship Outreach Network which include clinical data for 27 community hospitals in the southeastern United States. Candidate features (predictors) were then generated from these records. The number of features was reduced using a statistical approach, and missing values of the reduced feature set were imputed using bootstrapping and expectation‐maximization algorithm. Finally, support vector regression (SVR) and cubist regression (CB) models were applied to find root‐mean‐square error values which were used to evaluate the selected feature set. The performance of the SVR and CB models was found to be better than that of linear null and negative binomial null models, thereby demonstrating the effectiveness of our selected features. Conclusions Relevant patient‐ and facility‐level predictors of antimicrobial usage in days of therapy were obtained and evaluated. The potential predictor set can be used in risk adjustment strategies for benchmarking antimicrobial use. Significance and Impact of the Study One reason for the rapid emergence of antimicrobial resistance is inappropriate use of antibiotics in hospitalized patients. Identifying predictors of antimicrobial exposure using a machine learning technique can improve the use of AU, enhance patient health outcomes, and reduce the infection spread caused by antimicrobial‐resistant organisms.

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The Standardized Antimicrobial Administration Ratio: A New Metric for Measuring and Comparing Antibiotic Use

Cited by 80 publications

References 13 publications

Development of the predicted and standardized carbapenem usage metric: Analysis of the Japanese Diagnosis Procedure Combination payment system data

Development of the predicted and standardized carbapenem usage metric: Analysis of the Japanese Diagnosis Procedure Combination payment system data

Towards Data-Driven Hospital Antimicrobial Stewardship: Secondary use of Routine Electronic Care and Prescribing Records to Inform Antimicrobial Stewardship Programmes

Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach

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