Risk exploration and prediction model construction for linezolid-resistant Enterococcus faecalis based on big data in a province in southern China

Background Enterococcus faecalis is a common cause of healthcare-associated infections. Its resistance to linezolid, the antibiotic of last resort for vancomycin-resistant enterococci, has become a growing threat in healthcare settings. Methods We analyzed the data of E. faecalis isolates from 26 medical institutions between 2018 and 2020 and performed univariate and multivariate logistic regression analyses to determine the independent predictors for linezolid-resistant E. faecalis (LREFs). Then, we used the artificial neural network (ANN) and logistic regression (LR) to build a prediction model for linezolid resistance and performed a performance evaluation and comparison. Results Of 12,089 E. faecalis strains, 755 (6.25%) were resistant to linezolid. Among vancomycin-resistant E. faecalis , the linezolid-resistant rate was 24.44%, higher than that of vancomycin-susceptible E. faecalis ( p