Abstract
Background: Multidrug resistant (MDR) and extensively drug resistant (XDR) Acinetobacter baumannii are among important causes of
nosocomial infections and cause therapeutic problems worldwide. The emergence of extensively drug-resistant A. baumannii (XDRAB)
cause serious threats to hospital acquired infections (HAI) worldwide and further limit the treatment options.
Objectives: The current study aimed to identify and isolate the MDR and XDR Acinetobacter baumannii from different wards of a teaching
hospital in Isfahan, Iran, and determine the susceptibility pattern of these bacteria.
Materials and Methods: One hundred and twenty one (121) isolates of A. baumannii collected from a teaching hospital in Isfahan, Iran,
within eight months (between September 2013 and April 2014) were included in the current study. The samples were isolated from different
wards and different specimens. To confirm the species of A. baumannii, Polymerase chain reaction (PCR)was conducted to identify blaoxa-51
gene. Disk diffusion method was employed to evaluate antimicrobial susceptibility against cefotaxime, ceftriaxone, ampicillin-sulbactam,
cefepime, meropenem, tobramycin, amikacin, tetracycline, ciprofloxacin, trimethoprim- sulfamethoxazole, and aztreonam.
Results: Among the 121 isolated A. baumannii, 44% and 56% were isolated from female and male, respectively. Samples cultured from the
trachea (36%), urine (15%), blood (10%), wound (10%), cerebrospinal fluid (7%), bronchial (4%) and the others (18%). Most of the isolates (50%)
were obtained from intensive care unit (ICU). Isolated A. baumannii showed high resistance to the evaluated antibiotics except ampicillinsulbactam,
which showed only 33.9% resistance. Also, 62.8% and 100% of the isolates were identified as XDR and MDR.
Conclusions: The result of the current study showed the growing number of nosocomial infections associated with XDR A. baumannii
causing difficulties in antibiotic therapy. Resistant strains increasingly cause public health problems; therefore, their early detection is
essential for healthcare centers.