Phenotypic Detection of Biofilm Formation in Clinically Significant Isolates and Its Correlation with Clinical Outcomes and Antibiotic Resistance Patterns
Keywords:
Biofilm, Antimicrobial Resistance, Phenotypic Detection, Microtiter Plate Assay, Healthcare-Associated Infection, CLSI, Central India.Abstract
Healthcare-associated infections (HAIs) are a major challenge in tertiary-care hospitals, largely driven by the ability of microorganisms to form biofilms. Biofilms are structured microbial communities embedded in an extracellular polymeric matrix that protect pathogens from host immune responses and antimicrobial agents. This study aims to detect biofilm formation in clinically significant bacterial and fungal isolates using standardized phenotypic assays and correlate these findings with antibiotic resistance patterns and patient outcomes. A cross-sectional observational study will be conducted over three years at Datta Meghe Medical College and affiliated hospitals, including approximately 700–800 unique clinical isolates from blood, urine, pus, and device-related samples. Biofilm detection will be performed using Congo Red Agar (CRA), Tube Adherence Method (TAM), and the Microtiter Plate Assay (MTP), the latter serving as the gold standard. Antibiotic susceptibility testing will follow CLSI M100 (2024) guidelines. Correlations will be analyzed between biofilm strength, antimicrobial resistance (MDR/XDR), and clinical outcomes such as hospital-stay duration, complications, and mortality. The study anticipates providing evidence for incorporating biofilm surveillance into infection-control policies and antibiotic-stewardship programs, thereby improving patient prognosis and reducing the burden of persistent HAIs.
Need for the Study
In India, and particularly in Central India, limited longitudinal data link phenotypic biofilm production with both antimicrobial resistance and clinical outcomes. Establishing a standardized, cost-effective phenotypic protocol and correlating laboratory findings with clinical variables will generate translational evidence crucial for infection-control programs. This study addresses this gap by integrating routine phenotypic assays with patient-level data to create a practical surveillance model applicable to resource-limited tertiary-care centers.
