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Showing 2 results for Acinetobacter
Prevalence and antibiotic susceptibility of isolated acinetobacters from blood culture in shariati hospital
A Rasti , Y Erfani, H Yazdanbod,
Volume 3, Issue 3 (3-2010)
Abstract

Background and Aim: Acinetobacters are opportunistic pathogens and one of the important agents of nosocomial infections that causes many infections like septicemia and  pneumonia. For resistance to antibiotics acinetobacters are mentioned as a healthcare system complications and are transmitted by the hands of healthcare workers. This research has performed in order to determine prevalence and antibiotic susceptibility of isolated acinetobacters from blood cultures.

Material and methods: This study was  performed during a nine months period in  shariati hospital. All 750  positive  blood cultures were distinguished and prevalence of acinetobacter and antibiotic susceptibility of isolated acinetobacters were determined  using disk diffusion agar method. Data were analyzed using SPSS software .

Results: According to our findings,133 blood cultures were positive for acinetoloacters(17.7% of cases). The most observed cases were isolated  from emergency ward of hospital (65.5%). Antibiogram  results  using  ciprofloxacin ,cotrimoxasol,gentamicin, ceftazidime, amikacine, tobramicine and ceftriaxone, showed maximum  sensitivity to ciprofloxacine(91%) and  cotrimoxazole (57.5%) and  maximum  resistance to ceftriaxone respectively.

Discussion and Conclusion: Because the most acinetobacter  isolation was from emergency ward of hospital, it seems that a part of such infections is more due to contamination than real infection. Therefore it is  recommended  that a prospective cohort study considering standard  and sterile conditions  during  sampling , by  considering  patients clinical features  has to  be performed.


Prediction of Sepsis Due to Acinetobacter Infection in Neonates Admitted to NICU
Niloofar Mohammadzadeh, Ziba Mosayebi, Hamid Beigy, Mohammad Shojaeinia,
Volume 14, Issue 6 (1-2021)
Abstract
Background and Aim: Sepsis is the most important disease in the first 28 days of life and one of the main causes of infant mortality in the intensive care unit. Its definitive diagnosis is possible by performing blood culture. Neonatal sepsis can be a clinical sign of nosocomial infections that are often resistant to antibiotics. Therefore, the purpose of this study was to create and evaluate a hospital sepsis prediction model and present its results to health care providers.
Materials and Methods: In this descriptive-applied study, the research population includes neonates admitted to the intensive care unit of Valiasr Hospital in Tehran and the research sample is the data of 4196 neonates admitted to this ward from 2016 to August, 2020. The initial features for creating a predictive model of sepsis were prepared by examining the relevant information sources and under the supervision of professors and officials of Valiasr Hospital's mother and fetus research center and its validity was confirmed by 5 neonatal professors of this hospital. In this research, machine learning algorithms have been used to create a sepsis prediction model.
Results: Accuracy and AUROC(area under the ROC curve) parameters were used to evaluate the generated models. The highest values of Accuracy and AUROC are related to Adaptive Boosting and random forest algorithms, respectively.
Conclusion: Learning curves show that using different training examples and more complex selection of combination features improves the performance of the models. Further research is needed to evaluate the clinical effectiveness of machine learning models in a trial.

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