Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.14/17565
Título: Propensity for biofilm formation by clinical isolates from urinary tract infections: Developing a multifactorial predictive model to improve antibiotherapy
Autor: Alves, Maria José
Barreira, João C. M.
Carvalho, Inês
Trinta, Luis
Pereira, Liliana
Ferreira, Isabel C. F. R.
Pintado, Manuela
Data: 2014
Citação: ALVES, Maria José …[et al.] - Propensity for biofilm formation by clinical isolates from urinary tract infections: Developing a multifactorial predictive model to improve antibiotherapy. Journal of Medical Microbiology. ISSN 0022-2615. N.º 63, Part.3 (2014), p. 471-477
Resumo: A group of biofilm-producing bacteria isolated from patients with urinary tract infections was evaluated, identifying the main factors contributing to biofilm formation. Among the 156 isolates, 58 (37.2 %) were biofilm producers. The bacterial species (P,0.001), together with patient’s gender (P50.022), were the factors with the highest influence for biofilm production. There was also a strong correlation of catheterization with biofilm formation, despite being less significant (P50.070) than species or gender. In fact, some of the bacteria isolated were biofilm producers in all cases. With regard to resistance profile among bacterial isolates, b-lactam antibiotics presented the highest number of cases/percentages – ampicillin (32/55.2 %), cephalothin (30/ 51.7 %), amoxicillin/clavulanic acid (22/37.9 %) – although the carbapenem group still represented a good therapeutic option (2/3.4 %). Quinolones (nucleic acid synthesis inhibitors) also showed high resistance percentages. Furthermore, biofilm production clearly increases bacterial resistance. Almost half of the biofilm-producing bacteria showed resistance against at least three different groups of antibiotics. Bacterial resistance is often associated with catheterization. Accordingly, intrinsic (age and gender) and extrinsic (hospital unit, bacterial isolate and catheterization) factors were used to build a predictive model, by evaluating the contribution of each factor to biofilm production. In this way, it is possible to anticipate biofilm occurrence immediately after bacterial identification, allowing selection of a more effective antibiotic (among the susceptibility options suggested by the antibiogram) against biofilm-producing bacteria. This approach reduces the putative bacterial resistance during treatment, and the consequent need to adjust antibiotherapy
Peer review: yes
URI: http://hdl.handle.net/10400.14/17565
Aparece nas colecções:CBQF - Artigos em revistas internacionais com Arbitragem / Papers in international journals with Peer-review



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