In International journal of antimicrobial agents ; h5-index 49.0
Antimicrobial resistance (AMR) is a global health threat, rapid and timely identification of AMR improves patient prognosis and reduces inappropriate antibiotic use. We systematically searched relevant literature in PubMed, Web of Science, Embase and Institute of Electrical and Electronics Engineers prior to Sep 28, 2021. The study that deployed machine learning or risk score as tool to predict AMR was included in the final review. There were 25 studies that employed the ML algorithm to predict AMR. ESBL, MRSA and carbapenem 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 is 0.48-0.93. The pooled AUC for ML prediction is 0.82(0.78-0.85). Compared with risk score, higher specificity [87% (82-91) vs. 37% (25-51)] is indicated for ML prediction, but not sensitivity [67% (62-72) vs. 73% (67-79)]. ML might be a potential technology for AMR prediction. However, retrospective methodology for model development, nonstandard data processing and scarcity of validation in a randomized controlled trial or real-world study limit the application of these models in clinical practice.
Tang Rui, Luo Rui, Tang Shiwei, Song Haoxin, Chen Xiujuan
antimicrobial, machine learning, prediction, resistance, risk score