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A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care

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Title: A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care
Authors: Rawson, TM
Hernandez, B
Moore, L
Herrero, P
Charani, E
Ming, D
Wilson, R
Blandy, O
Sriskandan, S
Toumazou, C
Georgiou, P
Holmes, A
Item Type: Journal Article
Abstract: Background A locally developed Case-Based Reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated. Methods Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in two patient populations. Firstly, in patients with confirmed Escherichia coli blood stream infections (‘E.coli patients’), and secondly in ward-based patients presenting with a range of potential infections (‘ward patients’). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the WHO Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known, or most-likely organism antimicrobial sensitivity profile. Results In total, 224 patients (145 E.coli patients and 79 ward patients) were included. Mean (SD) age was 66 (18) years with 108/224 (48%) female gender. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (OR: 1.24 95%CI:0.392-3.936;p=0.71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (p<0.01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians’ prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77 95%CI:1.212-2.588 p<0.01). Results were similar for E.coli and ward patients on subgroup analysis. Conclusions A CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviours more broadly and patient outcomes.
Issue Date: 15-Jun-2021
Date of Acceptance: 27-Mar-2020
URI: http://hdl.handle.net/10044/1/79074
DOI: 10.1093/cid/ciaa383
ISSN: 1058-4838
Publisher: Oxford University Press (OUP)
Start Page: 2103
End Page: 2111
Journal / Book Title: Clinical Infectious Diseases
Volume: 72
Issue: 12
Copyright Statement: © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. This is a pre-copy-editing, author-produced version of an article accepted for publication in Clinical Infectious Diseases following peer review. The definitive publisher-authenticated version is available online at: https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa383/5815768
Sponsor/Funder: National Institute for Health Research
National Institute for Health Research
National Institute for Health Research
National Institute for Health Research
National Institute for Health Research
National Institute for Health Research
National Institute of Health Research Imperial Biomedical Research Centre
NIHR Invention for Innovation
Economic & Social Research Council (ESRC)
Funder's Grant Number: HPRU-2012-10047
HPRU-2012-10047
NIHR200646
NF-SI-0617-10176
II-LA-0214-20008
II-LA-0214-20008
WMNF_P46472
II-LA-0214-20008
ES/M500562/1
Keywords: Artificial intelligence
antimicrobial stewardship
clinical decision support systems
machine learning
sepsis
Aged
Algorithms
Anti-Bacterial Agents
Anti-Infective Agents
Antimicrobial Stewardship
Escherichia coli
Female
Humans
Inappropriate Prescribing
Practice Patterns, Physicians'
Humans
Escherichia coli
Anti-Infective Agents
Anti-Bacterial Agents
Algorithms
Aged
Female
Inappropriate Prescribing
Practice Patterns, Physicians'
Antimicrobial Stewardship
Microbiology
06 Biological Sciences
11 Medical and Health Sciences
Publication Status: Published
Online Publication Date: 2020-04-04
Appears in Collections:Electrical and Electronic Engineering
Department of Infectious Diseases
Faculty of Medicine
Faculty of Engineering