TO STUDY THE CLINICO-MICROBIOLOGICAL PROFILE OF AN INTRA-ABDOMINAL INFECTIONS IN THE SURGICAL INTENSIVE CARE UNIT
DOI:
https://doi.org/10.22159/ajpcr.2024v17i11.52085Keywords:
Intra-abdominal infections,, Surgical ICU, antimicrobial susceptibility testingAbstract
Objective: Intra-abdominal infections (IAIs) pose a severe challenge in surgical intensive care units (ICUs). These infections, which can develop from conditions such as peritonitis, appendicitis, and intra-abdominal abscesses, often worsen into severe complications such as sepsis and multi- organ failure. Early and accurate diagnosis, along with rapid and effective treatment, is crucial for improving the survival and recovery of patients in the surgical ICU who are affected by these life-threatening infections. This study focuses on the clinical and microbiological profile of IAIs in the surgical ICU, emphasizing the importance of understanding the microbial landscape.
Methods: This prospective study was carried out in the Department of Microbiology over the course of 1 year. Patients with suspected IAIs admitted to the surgical ICU were included in the study. The samples were received and processed as per standard protocols. Identification and antimicrobial susceptibility testing were done by the Vitek-2 system, and isolates were characterized as multi-/extensively/pan-drug resistant.
Results: Out of 1010 patients admitted to the surgical ICU, infections were present in 81 patients (53 males and 28 females), resulting in an infection rate of 8%. The most common comorbidity was diabetes mellitus (28.3%), and the most common risk factors were sepsis (19.7%), alcohol intake (18.5%), and immobilization (16%). A total of 87 isolates were obtained. The most common isolate was Escherichia coli (47.1%), followed by Klebsiella pneumoniae (26.4%). E. coli showed maximum sensitivity for amikacin (76.7%), followed by tigecycline (74.4%). 81.6% of isolates were multidrug-resistant, and 62.7% were extensively drug-resistant.
Conclusion: Knowledge of antimicrobial resistance patterns provides guidance for the treatment, thus improving outcomes.
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