Spectrum and Antibiotic Resistance of Uropathogens in Romanian Females.
Razvan Cosmin PetcaCristian MaresAida PetcaSilvius NegoițăRăzvan-Ionuț PopescuMihaela BoțEnikő BarabásCălin Bogdan ChibeleanPublished in: Antibiotics (Basel, Switzerland) (2020)
Urinary tract infections (UTIs) in women represent a common bacteriological finding, with negligible recent and consistent research on antimicrobial resistance (AMR) in the female population. We designed a retrospective study to observe the incidence of frequent uropathogens and their resistance rates to common antibiotics. We elaborated multicenter research in three different teaching hospitals in Romania, analyzing 13,081 urine samples, of which 1588 met the criteria of inclusion. Escherichia coli (58.37%) was the most frequent Gram-negative uropathogen, presenting high resistance rates to levofloxacin (R = 29.66%), amoxicillin-clavulanic ac. (R = 14.13%), and ceftazidime (R = 6.68%). We found good sensitivity to imipenem and meropenem (both 98.16%), amikacin (S = 96.0%), and fosfomycin (S = 90.39%). The second most prevalent uropathogen was Klebsiella (16.93%), with the highest resistance quota to amoxicillin-clavulanic ac. (R = 28.62%), levofloxacin and nitrofurantoin (both R = 15.61%), and ceftazidime (R = 15.24%), and good sensitivity to imipenem (S = 93.93%), meropenem (S = 91.91%), and amikacin (S = 88.47%). Enterococcus (13.35%) was the most encountered Gram-positive pathogen. It proved the highest resistance to levofloxacin (R = 32.07%), penicillin (R = 32.07%), and ampicillin (R = 14.62%) and good sensitivity to vancomycin (S = 91.98%), fosfomycin (S = 94.4%), and nitrofurantoin (S = 89.15%). Considering the lack of recent and consistent data on this topic, we find our survey a valuable starting research study in this area with high significance for an accurate clinical approach.
Keyphrases
- gram negative
- multidrug resistant
- urinary tract infection
- antimicrobial resistance
- escherichia coli
- klebsiella pneumoniae
- cross sectional
- polycystic ovary syndrome
- machine learning
- type diabetes
- electronic health record
- metabolic syndrome
- staphylococcus aureus
- cystic fibrosis
- mass spectrometry
- methicillin resistant staphylococcus aureus
- skeletal muscle