A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care


Rawson, Timothy M ORCID: 0000-0002-2630-9722, Hernandez, Bernard, Moore, Luke SP, Herrero, Pau, Charani, Esmita ORCID: 0000-0002-5938-1202, Ming, Damien, Wilson, Richard C ORCID: 0000-0002-3275-6932, Blandy, Oliver, Sriskandan, Shiranee, Gilchrist, Mark et al (show 3 more authors) (2021) A Real-world Evaluation of a Case-based Reasoning Algorithm to Support Antimicrobial Prescribing Decisions in Acute Care. CLINICAL INFECTIOUS DISEASES, 72 (12). pp. 2103-2111.

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Abstract

<h4>Background</h4>A locally developed case-based reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.<h4>Methods</h4>Prescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in 2 patient populations: first, in patients with confirmed Escherichia coli blood stream infections ("E. coli patients"), and second 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 World Health Organization 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.<h4>Results</h4>In total, 224 patients (145 E. coli patients and 79 ward patients) were included. Mean (standard deviation) age was 66 (18) years with 108/224 (48%) female sex. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (odds ratio [OR]: 1.24 95% confidence interval [CI]: .392-3.936; P = .71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (P < .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 < .01). Results were similar for E. coli and ward patients on subgroup analysis.<h4>Conclusions</h4>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 behaviors more broadly and patient outcomes.

Item Type: Article
Uncontrolled Keywords: Artificial intelligence, machine learning, sepsis, antimicrobial stewardship, clinical decision support systems
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology
Depositing User: Symplectic Admin
Date Deposited: 17 May 2023 10:21
Last Modified: 17 May 2023 10:21
DOI: 10.1093/cid/ciaa383
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3170309
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