ABSTRACT
Background:Lower Respiratory Tract Infection(LRTI) is one of the commonest health problem which is not a single disease but a group of specific infections with varying etiology and symptomatology. This study was undertaken to determine bacterial etiology of LRTI and associated risk factors 250 samples from patients with suspected LRTI were collected Methodology:and processed by standard microbiological procedure. Risk factors relating to LRTI were also further evaluated from clinical history of the patient. Total 100 isolates were recovered where Klebsiella pneumoniae(57%) was predominant followed by Pseudomonas Results:aeruginosa(19%), Acinetobacter spp.,(9%), Citrobacter freundii(7%), Staphylococcus aureus(3%), and Streptococcus pneumoniae(2%). Multiple co-morbidities are more associated with LRTI than single risk factor like Diabetes mellitus, Smoking, Alcohol, Hypertension and COPD. Knowledge of bacterial agents causing LRTI and prevalent risk factors in our geographical area is to be required for better Conclusion:treatment and prevention of the disease
ABSTRACT
Introduction: Resistance to antibiotics is an extremely common phenomenon in bacteria isolated from clinical material. However there is scarcity of data on antibiotic resistance from North Eastern part of India. In this Study, we aimed to generate data to find out the trend of Antibiotic resistance among the clinical isolates. Methods: Involving both indoor and outdoor patients attending this hospital are included in this study. Culture & Microbial sensitivity were performed by ‘Kirby Bauer‘ method and an according to current CLSI guidelines. Drug resistance pattern were analyzed among the Common clinical isolates. Results: Majority of isolated organisms were klebsiella sp and Staphylococcus sp . Cephalosporins resistance (99%) was more in Gram negative bacilli as compare to Fluroquinolones among Gram positive cocci (85%). Conclusion: This pilot study from a Medical College of North East has demonstrated very high levels of resistance to commonly used antibiotics and generated data can be used to formulate antibiotic use protocols in the state.