Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis

Introduction: Antimicrobial resistance (AMR) is a global health threat; rapid and timely identification of AMR improves patient prognosis and reduces inappropriate antibiotic use.Methods: Relevant literature in PubMed, Web of Science, Embase and Institute of Electrical and Electron-ics Engineers prior to 28 September 2021 was searched. Any study that deployed machine learning (ML) or a risk score as a tool to predict AMR was included in the final review; there were 25 studies that employed the ML algorithm to predict AMR.Results: Extended spectrum beta-lactamases, methicillin-resistant Staphylococcus aureus (MRSA) and car-bapenem resistance were the most common outcomes in studies with a specific resistance pattern. The most common algorithms in ML prediction were logistic regression ( n = 14 studies), decision tree ( n = 14) and random forest ( n = 7). The area under the curve (AUC) range for ML prediction was 0.48- 0.93. The pooled AUC for ML prediction was 0.82 (0.78-0.85). Compared with risk score, higher specificity [87% (82-91) vs. 37% (25-51)] was indicated for ML prediction, but not sensitivity [67% (62-72) vs. 73% (67-79)].Conclusions: Machine learning might be a potential technology for AMR prediction; however, retrospec-tive methodology for model development, nonstandard data processing and scarcity of validation in a randomised controlled trial or real-world study limit the application of these models in clinical practice.(c) 2022 Elsevier Ltd and International Society of Antimicrobial Chemotherapy. All rights reserved.

Automated summary

Machine learning may be a potential technology for predicting antimicrobial resistance, but limitations in the retrospective methodology for model development, nonstandard data processing, and scarcity of validation in randomized controlled trials or real-world studies restrict the application of these models in clinical practice.