Abstract
INTRODUCTION: Head and neck infections, particularly odontogenic infections, can lead to serious complications if not properly managed. While swabs are commonly used for microbial identification, their reliability in polymicrobial infections is debated. This study evaluates the advantages of native tissue samples over swabs in the management of severe head and neck infections. MATERIAL AND METHODS: This prospective cohort study included patients with severe acute head and neck infections requiring hospitalization, surgical drainage, and microbiologic analysis. Swabs and native tissue/fluid samples were collected for pathogen cultivation, Gram staining, and resistance testing. Clinical data, infection characteristics, and antimicrobial resistance profiles were analyzed using descriptive and inferential statistics. RESULTS: 60 patients, 55% male (45.7 years) and 45% female (48.1 years) were analyzed. After antibiotic treatment, CRP and leukocyte levels decreased significantly, with higher CRP correlating with longer hospital stays. ICU admission correlated with hospital stay > 7 days. More Actinomyces and fungal species were identified in native tissue samples and more Streptococci in swabs. Antibiotic resistance, especially to clindamycin (1/3 of the cases), was associated with longer hospital and ICU stays. Clindamycin resistance correlated with increased ICU admission, while metronidazole resistance (10% of the cases) was associated with longer ICU stays. ICU admission was also associated with higher Cormack-Lehane scores. CONCLUSION: Severe head and neck infections require a comprehensive multidisciplinary approach. Native tissue should be obtained whenever possible. While microbiological findings varied between sampling methods, native samples may provide a broader spectrum of detected pathogens, which could be relevant for infection management. Given the increasing resistance to clindamycin, its indications should be critically re-evaluated. The implementation of targeted antimicrobial strategies and a risk-based classification system may help optimize patient management and improve outcomes.