Indian Journal of Dermatology
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ORIGINAL ARTICLE
Year : 2012  |  Volume : 57  |  Issue : 5  |  Page : 358-361

Bacteriological study of pyodermas in a tertiary care dermatological center


1 Department of Dermatology, Government Medical College, Amritsar, Punjab, India
2 Department of Microbiology, Government Medical College, Amritsar, Punjab, India

Correspondence Address:
Suresh K Malhotra
Department of Dermatology HIG 943, Sector 3, Ranjit Avenue, Amritsar-143001, Punjab
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0019-5154.100475

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Background: Bacterial skin infection especially Pyoderma, commonly caused by Staphylococcus aureus and group A Streptococci, is quite common in Indian dermatology clinics. Despite a number of new antibiotics, the incidence of bacterial resistance is rising. Aim: To find out causative organisms and their latest antibiotic susceptibility patterns in pyodermas. Materials and Methods: All in-patients admitted in the Dermatology wards in Government Medical College, Amritsar were screened over 18 months and those with erosive skin lesions and/or purulent discharge were included in the study and swabs were sent for culture and sensitivity. Results: Majority 49/61 cases (80.33%) comprised of secondary pyodermas while primary pyodermas constituted only 12/61 cases (19.67%). Single organism was isolated in 49 cases (80.33%). More than one type of organism was isolated in 3 cases (4.92%) while none could be isolated from 9 (14.75%) cases. Staphylococcus aureus spp. was the commonest organism isolated in 36 (59.01%) cases and out of these, coagulase positive strains were found to be highly susceptible to amikacin (21cases-100%). Coagulase negative strains were sensitive to amikacin (7 cases-77.7%) and gentamycin (6 cases-66.6%) respectively. Conclusion: This study gives an indication of the present pattern of bacteriological profile of pyodermas in a tertiary care hospital in north-west India. In-vitro testing is essential as knowledge of the causative organisms and resistance patterns can help us select appropriate antibiotics without wasting time in using resistant drugs.


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