Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes for Escherichia coli sequence type 131 clade C surveillance across infection types

Rising antimicrobial resistance (AMR) in <i>Escherichia coli</i> bloodstream infections (BSIs) in high-income settings has typically been dominated by one clone, the sequence type (ST)131. More specifically, ST131 clade C (ST131-C) is associated with fluoroquinolone resistance and extended-spectrum <i>β</i>-lactamases (ESBLs). Even though urinary tract infections (UTIs) are a known common precursor to BSIs, there is currently limited knowledge on the longitudinal prevalence of ST131-C in UTIs and, therefore, the temporal link between the two infection types. Leveraging available genomic and antimicrobial susceptibility test (AST) data for ciprofloxacin, gentamicin and ceftazidime in 2,790 <i>E. coli</i> BSI isolates, we trained Random Forest and extreme gradient boosting (XGBoost) classifiers to predict if an <i>E. coli</i> isolate belongs to ST131-C using only AST data. These models were used to predict the yearly prevalence of ST131-C in 22942 UTI and 24866 BSI isolates from Norway. The XGBoost classifier achieved a prediction F1-score of over 70% on a highly unbalanced dataset where only 4.3% of the genomic BSI isolates belonged to ST131-C. The predicted prevalence of ST131-C in UTIs exhibited a similar annual trend to that of BSIs, with a stable infection burden for 8 years after its rapid expansion, confirming that the persistence of ST131-C in BSIs is largely driven by ST131-C UTIs. However, a higher prevalence of ST131-C in BSIs (~7 %) compared to UTIs (~4 %) suggests a subsequent enrichment of ST131-C. Our study highlights how existing epidemiological knowledge can be supplemented by utilizing extensive data from AMR surveillance efforts without genomic markers.