Fernández, Javier

ABSTRACT Errors in antibiotic prescriptions are frequent, often resulting from the inadequate coverage of the infection-causative microorganism. The efficacy of iAST, a machine-learning-based software offering empirical and organism-targeted antibiotic recommendations, was assessed. The study was conducted in a 12-hospital Spanish institution. After model fine-tuning with 27,531 historical antibiograms, 325 consecutive patients with acute infections were selected for retrospective validation. The primary endpoint was comparing each of the top three of iAST’s antibiotic recommendations’ success rates (confirmed by antibiogram results) with the antibiotic prescribed by the physicians. Secondary endpoints included examining the same hypothesis within specific study population subgroups and assessing antibiotic stewardship by comparing the percentage of antibiotics recommended that belonged to different World Health Organization AWaRe groups within each arm of the study. All of iAST first three recommendations were non-inferior to doctor prescription in the primary endpoint analysis population as well as the secondary endpoint. The overall success rate of doctors’ empirical treatment was 68.93%, while that of the first three iAST options was 91.06% ( P < 0.001), 90.63% ( P < 0.001), and 91.06% ( P < 001), respectively. For organism-targeted therapy, the doctor’s overall success rate was 84.16%, and that of the first three ranked iAST options was 97.83% ( P < 0.001), 94.09% ( P < 0.001), and 91.30% ( P < 0.001), respectively. In empirical therapy, compared to physician prescriptions, iAST demonstrated a greater propensity to recommend access antibiotics, fewer watch antibiotics, and higher reserve antibiotics. In organism-targeted therapy, iAST advised a higher utilization of access antibiotics. The present study demonstrates iAST accuracy in predicting antibiotic susceptibility, showcasing its potential to promote effective antibiotic stewardship. CLINICAL TRIALS This study is registered with ClinicalTrials.gov as NCT06174519 .

ABSTRACT Ureaplasma urealyticum and Ureaplasma parvum are pathogens involved in urogenital tract and intrauterine infections and also in systemic diseases in newborns and immunosuppressed patients. There is limited information on the antimicrobial susceptibility and clonality of these species. In this study, we report the susceptibility of 250 contemporary isolates of Ureaplasma (202 U. parvum and 48 U. urealyticum isolates) recovered at Mayo Clinic, Rochester, MN. MICs of doxycycline, azithromycin, ciprofloxacin, tetracycline, erythromycin, and levofloxacin were determined by broth microdilution, with MICS of the last three interpreted according to CLSI guidelines. Levofloxacin resistance was found in 6.4% and 5.2% of U. parvum and U. urealyticum isolates, respectively, while 27.2% and 68.8% of isolates, respectively, showed ciprofloxacin MICs of ≥4 μg/ml. The resistance mechanism of levofloxacin-resistant isolates was due to mutations in parC , with the Ser83Leu substitution being most frequent, followed by Glu87Lys. No macrolide resistance was found among the 250 isolates studied; a single U. parvum isolate was tetracycline resistant. tet (M) was found in 10 U. parvum isolates, including the single tetracycline-resistant isolate, as well as in 9 isolates which had low tetracycline and doxycycline MICs. Multilocus sequence typing (MLST) performed on a selection of 46 isolates showed high diversity within the clinical Ureaplasma isolates studied, regardless of antimicrobial susceptibility. The present work extends previous knowledge regarding susceptibility to antimicrobial agents, resistance mechanisms, and clonality of Ureaplasma species in the United States.