AMR - An R Package for Working with Antimicrobial Resistance Data

Berends, Matthijs S.; Luz, Christian F.; Friedrich, AW; Sinha, Bhanu; Albers, Casper J.; Glasner, Corinna

doi:10.1101/810622

Cited by 24 publications

(28 citation statements)

References 20 publications

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“…One of the most signi cant obstacles hindering hospitals' stewardship efforts lies the di culty in extracting and analysing EHRs from a range of diverse systems [16]. Reproducible analytical tools are now available to assist microbiology culture and sensitivity analytics [51]. Software development is underway support other hospitals in adopting the approach tested in the present study.…”

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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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

View full text Add to dashboard Cite

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“…18 Reproducible analytical tools are now available to assist microbiology culture and susceptibility analytics. 49 Software development is underway to support other hospitals in adopting the approach tested in the present study. Notes: CI: confidence interval; DOT: days of therapy; IQR: interquartile range; LOS: length of stay (days); LOT: total length of therapy per admission (days); SD: standard deviation; *column does not add up to total as patients may change age group during the year.…”

Section: Discussionmentioning

confidence: 99%

Feasibility study of hospital antimicrobial stewardship analytics using electronic health records

Dutey-Magni

Gill

McNulty

et al. 2021

Preprint

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Background: Hospital antimicrobial stewardship (AMS) programmes are multidisciplinary initiatives to optimise the use of antimicrobials. Most hospitals depend on time-consuming manual audits to monitor clinicians’ prescribing. But much of the information needed could be sourced from electronic health records (EHRs).Objectives: To develop an informatics methodology to analyse characteristics of hospital AMS practice using routine electronic prescribing and laboratory records.Methods: Feasibility study using electronic prescribing, laboratory and clinical coding records from adult patients admitted to six specialties at Queen Elizabeth Hospital, Birmingham, UK (September 2017–August 2018). The study involved: (1) a review of antimicrobial stewardship standards of care; (2) their translation into concepts measurable from commonly available EHRs; (3) pilot application in an EHR cohort study (n=61,679 admissions).Results: We developed data modelling methods to characterise the use of antimicrobials (antimicrobial therapy episode linkage methods, therapy table, therapy changes). Prescriptions were linked into antimicrobial therapy episodes (mean 2.4 prescriptions/episode; mean length of therapy of 5.8 days) enabling production of several actionable findings. For example, 22% of therapy episodes for low-severity community acquired pneumonia were congruent with prescribing guidelines, with a tendency to use antibiotics with a broader spectrum. Analysis of therapy changes revealed a delay in switching from intravenous to oral therapy by an average 3.6 days [95% CI: 3.4; 3.7]. Performance of microbial cultures prior to treatment initiation occurred in just 22% of antibacterial prescriptions. The proposed methods enabled fine-grained monitoring of AMS practice all the way down to specialties, wards, and individual clinical teams by case mix, enabling more meaningful peer comparison. Conclusions: It is feasible to use hospital EHRs to construct rapid, meaningful measures of prescribing quality with potential to support quality improvement interventions (audit/feedback to prescribers), engagement with front-line clinicians on optimising prescribing, and AMS impact evaluation studies.

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“…Despite these guidelines, it remains difficult for clinical microbiologists to calculate the cumulative antibiogram based on the first isolates. Software specializing in cumulative antibiograms such as WHONET or the R package AMR can be used [ 24 ]. WHONET is a free Windows-based database software developed for managing and analyzing microbiology data by the WHO collaborating center for surveillance of antimicrobial resistance [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

Importance of Susceptibility Rate of ‘the First’ Isolate: Evidence of Real-World Data

2020

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Background and objectives: For proper antimicrobial therapy, cumulative antibiograms should be representative of geographic region and be accurate. Clinical and Laboratory Standards Institute (CLSI) guidelines recommend that only the first isolates (FI) of a species per patient are used when reporting cumulative antibiograms. However, >50% of hospitals in the United States report antibiograms of all isolates. We compared antibiograms from the FI with those from total isolates (TI). Materials and Methods: Antimicrobial data of all isolates identified in the Microbiology unit of Ilsan Paik Hospital in 2019 were retrospectively acquired from the hospital information system. The susceptibility rates to antimicrobials of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Enterococcus faecalis were analyzed by FI and TI, respectively. Isolate counts and susceptibility rates of each species for the reported antimicrobials were compared. Results: The numbers of isolates by FI/TI were as follows: 1824/2692 E. coli, 480/1611 A. baumannii, and 662/1306 K. pneumoniae, and 407/953 P. aeruginosa for gram-negative bacteria and 649/1364 S. aureus, 211/313 E. faecium, and 323/394 E. faecalis for gram-positive bacteria. All antimicrobial agents showed higher susceptibility rates when calculated as FI than as TI in gram-negative bacteria except colistin: 3.7% for E. coli, 14.5% for A. baumannii, 8.3% for K. pneumoniae, and 7.9% for P. aeruginosa. In S. aureus, 8/11 antimicrobial agents revealed higher susceptibility rates for FI than for TI. E. faecalis and E. faecium showed lower susceptibility rates for 7/10 antimicrobial agents for FI than for TI. The oxacillin susceptibility rates of S. aureus were 36.6%/30.2% with FI/TI and vancomycin susceptibility rates for E. faecium were 54.1% and 49.5%, respectively. Conclusions: When comparing cumulative antibiograms by FI with TI using real-world data, there is a large gap for critical species requiring hospital infection control. Although FI calculation is difficult, antibiograms must be calculated as FI for proper preemptive antimicrobial therapy because FI provides proper antimicrobial susceptibility data.

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AMR - An R Package for Working with Antimicrobial Resistance Data

Cited by 24 publications

References 20 publications

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

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

Feasibility study of hospital antimicrobial stewardship analytics using electronic health records

Importance of Susceptibility Rate of ‘the First’ Isolate: Evidence of Real-World Data

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