Linear Regression Equations To Predict β-Lactam, Macrolide, Lincosamide, and Fluoroquinolone MICs from Molecular Antimicrobial Resistance Determinants in Streptococcus pneumoniae.

ABSTRACT: Antimicrobial resistance in Streptococcus pneumoniae represents a threat to public health, and monitoring the dissemination of resistant strains is essential to guiding health policy. Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial MICs for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim-sulfamethoxazole. Training data sets consisting of Canadian S. pneumoniae isolates obtained from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial. The regression equations were then applied to validation data sets of Canadian (n = 439) and U.S. (n = 607 and n = 747) isolates. Th

SUBMITTER: Demczuk W 

PROVIDER: S-EPMC8765234 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature