European Journal of Cardiovascular Medicine

Biofilms are complex microbial communities embedded within a self-produced extracellular polymeric matrix that adhere to both biotic and abiotic surfaces. In clinical settings, biofilm formation plays a central role in the pathogenesis of various health care-associated infections (HAIs), often complicating treatment and leading to persistent and recurrent infections [1]. These microbial communities exhibit altered phenotypic traits, including increased resistance to antibiotics and evasion of host immune responses, which contribute to therapeutic failure and prolonged hospital stays [2,3].

Health care-associated infections such as catheter-associated urinary tract infections (CAUTI), central line-associated bloodstream infections (CLABSI), ventilator-associated pneumonia (VAP), and prosthetic device-related infections frequently involve biofilm-producing organisms [4,5]. Common pathogens implicated in biofilm-associated infections include Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Acinetobacter baumannii, and members of the Enterobacteriaceae family. The prevalence of multidrug-resistant strains within these biofilms further exacerbates the global burden of antimicrobial resistance [6].

Detection of biofilm formation is essential for infection control strategies and effective clinical management. While traditional culture techniques identify planktonic bacteria, specialized assays such as the Congo red agar method, tube adherence method, and microtiter plate assay are utilized to determine a strain’s biofilm-forming ability [7,8]. The identification of strong biofilm formers among clinical isolates may serve as an early warning for poor response to antibiotics and increased risk of chronic infection.

India faces a unique challenge due to high patient density, resource constraints, and suboptimal infection control practices, which facilitate biofilm-mediated colonization in hospital environments. Tertiary care centres, particularly in low-resource settings, report alarmingly high incidences of device-associated infections, a large proportion of which are biofilm-related [5,9]. Understanding the local epidemiology and resistance patterns of biofilm-producing organisms is critical for optimizing empirical therapy and establishing targeted prevention protocols.

Given this context, the present study was undertaken at GSL General Hospital and Medical College, Rajamundry, Andhra Pradesh, to investigate the prevalence of biofilm formation among bacterial isolates from health care-associated infections, and to evaluate its clinical impact in terms of antimicrobial resistance and patient outcomes.

AIMS AND OBJECTIVES

The primary aim of this study is to evaluate the role of biofilm formation in clinical bacterial isolates obtained from patients with health care-associated infections (HAIs). Specifically, the study seeks to determine the prevalence of biofilm-producing organisms among these isolates and to assess their impact on antimicrobial resistance patterns and clinical outcomes.

The objectives include identifying the common biofilm-forming pathogens in device-associated infections such as catheter-associated urinary tract infections (CAUTI), ventilator-associated pneumonia (VAP), and central line-associated bloodstream infections (CLABSI); comparing the antibiotic susceptibility profiles of biofilm producers versus non-producers; and examining the correlation between biofilm production and adverse clinical outcomes such as prolonged hospitalization, need for device removal, and treatment failure.

By highlighting the significance of biofilm detection and its clinical implications, the study aims to support improved infection control practices and guide empirical antibiotic therapy in tertiary care settings.

This prospective observational study was conducted over a period of one year, from January 2024 to December 2024, at the Department of Microbiology in collaboration with the Departments of Medicine and Surgery at GSL General Hospital and Medical College, Rajamundry, Andhra Pradesh. The study included 100 patients admitted to intensive care units and general wards who were diagnosed with health care-associated infections (HAIs) based on CDC definitions.

Inclusion criteria encompassed patients with clinical evidence of device-associated infections such as catheter-associated urinary tract infections (CAUTI), central line-associated bloodstream infections (CLABSI), ventilator-associated pneumonia (VAP), and surgical site infections (SSI), from whom appropriate clinical specimens yielded positive bacterial isolates. Patients with community-acquired infections or those receiving antibiotic therapy prior to sample collection were excluded from the study.

Clinical samples such as urine from indwelling catheters, endotracheal aspirates, blood from central venous catheters, and wound swabs were collected using sterile techniques. All samples were processed following standard microbiological procedures. Bacterial isolates were identified using colony morphology, Gram staining, and biochemical tests in accordance with CLSI guidelines.

Detection of biofilm production was performed using two phenotypic methods: the tube adherence method and the Congo red agar (CRA) method. For the tube adherence assay, isolates were inoculated into tryptic soy broth supplemented with 1% glucose and incubated at 37°C for 24 hours. Tubes were then washed, stained with 0.1% crystal violet, and visually assessed for the presence of an adherent biofilm layer. In the CRA method, isolates were streaked onto Congo red agar plates and incubated for 24–48 hours; black colonies with a dry crystalline consistency were interpreted as positive for biofilm production.

Antibiotic susceptibility testing was carried out using the Kirby-Bauer disk diffusion method on Mueller-Hinton agar as per CLSI guidelines. Multidrug resistance (MDR) was defined as resistance to at least one agent in three or more antimicrobial categories. Comparative analysis was conducted between biofilm-producing and non-producing strains to assess differences in resistance profiles.

Clinical outcome parameters, including duration of hospitalization, requirement for device removal, and therapeutic response, were recorded. Data were compiled using Microsoft Excel and analyzed using SPSS version 26.0 (IBM Corp., Armonk, NY). Categorical variables were expressed as frequencies and percentages. Chi-square or Fisher’s exact test was used for comparison between groups. A p-value < 0.05 was considered statistically significant.

Study Population and Infection Characteristics

A total of 100 patients with health care-associated infections were included in the study. The most common infection types identified were catheter-associated urinary tract infections (CAUTI) in 29 patients (29.0%), followed by central line-associated bloodstream infections (CLABSI) in 27 patients (27.0%), ventilator-associated pneumonia (VAP) in 25 patients (25.0%), and surgical site infections (SSI) in 19 patients (19.0%) (Table 1).

The study population consisted of 58 males and 42 females, with a predominance of patients in the 60–79 years age group. The overall biofilm positivity rate among isolates was 63%, with higher proportions observed in CAUTI and VAP cases. A wide spectrum of bacterial pathogens was identified, with Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii accounting for the majority of isolates. The prevalence of comorbidities was notable, with diabetes mellitus being the most frequently recorded underlying condition.

Table 1: Demographic and Clinical Profile

 Distribution of Pathogens and Biofilm Positivity

Among the 100 bacterial isolates obtained from patients with health care-associated infections, Klebsiella pneumoniae was the most frequently isolated pathogen (n=22), followed by Pseudomonas aeruginosa (n=20), and Acinetobacter baumannii (n=18). The overall rate of biofilm production was highest in Acinetobacter baumannii (72.2%), followed by Pseudomonas aeruginosa (65.0%) and Klebsiella pneumoniae (63.6%) (Table 2).

Although the distribution of biofilm production varied across pathogens, the association between organism type and biofilm formation did not reach statistical significance (χ² = 4.65, df = 5, p = 0.4601; Cramer’s V = 0.216). Notably, the multidrug resistance (MDR) rate was also highest among Acinetobacter baumannii (78%) and Klebsiella pneumoniae (72%), indicating a potential overlap between virulence and resistance traits.

Table 2: Distribution of Pathogens and Biofilm Positivity

Association between Infection Type and Biofilm Formation

Biofilm formation was assessed across four major types of health care-associated infections. Among catheter-associated urinary tract infections (CAUTI), 20 of the 29 cases (69.0%) showed biofilm positivity. In ventilator-associated pneumonia (VAP), biofilm was detected in 15 of 25 cases (60.0%), and in central line-associated bloodstream infections (CLABSI), 16 of 27 cases (59.3%) were biofilm-positive. Surgical site infections (SSI) exhibited the lowest biofilm positivity rate at 10 of 19 cases (52.6%) (Table 3).

Statistical analysis using the Chi-square test revealed no significant association between the type of infection and biofilm formation (χ² = 2.37, df = 3, p = 0.4989; Cramer’s V = 0.089), indicating that biofilm production was relatively consistent across different clinical infection types.

Table 3: Biofilm Positivity by Infection Type

 Correlation with Comorbidities and Hospital Stay

Among the patients with health care-associated infections, biofilm positivity varied across comorbidity groups. The highest proportion of biofilm producers was observed in patients with diabetes mellitus (68.6%), followed by those with multiple comorbidities (66.7%) and chronic kidney disease (61.1%). However, no statistically significant association was found between comorbidity type and biofilm formation (χ² = 4.44, df = 4, p = 0.3497; Cramer’s V = 0.122) (Table 4).

The median hospital stay for biofilm-positive patients was marginally longer than that for biofilm-negative patients, but this difference did not reach statistical significance (Mann–Whitney U = 1162.5, p = 0.3974; effect size r = 0.107). These findings suggest a trend toward prolonged hospitalization in biofilm-producing infections, although not statistically supported in this cohort.

Table 4: Biofilm Positivity by Comorbidity

Antibiotic Resistance in Biofilm Producers vs Non-Producers

A total of 100 isolates were analyzed for multidrug resistance (MDR), defined as resistance to three or more classes of antibiotics. Among biofilm-positive isolates, 47 of 63 (74.6%) demonstrated MDR, whereas among biofilm-negative isolates, only 17 of 37 (45.9%) were MDR (Table 5). The difference in MDR prevalence between the two groups was statistically significant (χ² = 7.62, df = 1, p = 0.0058; Cramer’s V = 0.276), indicating a moderate association between biofilm production and resistance to multiple antibiotics. These findings underscore the clinical challenge posed by biofilm-producing organisms in terms of antimicrobial treatment.

Table 5: Antibiotic Resistance in Biofilm Producers vs Non-Producers

 Multivariate Analysis – Predictors of Biofilm Formation

To identify independent predictors of biofilm formation, a multivariate logistic regression model was constructed incorporating infection type, organism isolated, comorbidity profile, and multidrug resistance (MDR) status. The overall model fit was satisfactory, with a log-likelihood value of 0.4899.

Among the tested variables, isolation of Enterococcus faecalis was associated with significantly increased odds of biofilm formation (OR = 12.48, 95% CI: 1.23–126.44, p = 0.0326). Other variables, including infection type, comorbidity, and MDR status, were not statistically significant predictors in the adjusted model (Table 6). These results underscore the role of specific pathogens, particularly Enterococcus species, in the pathogenesis of biofilm-related infections.

Table 6: Logistic Regression Predicting Biofilm Formation

This study reinforces the critical role of biofilm formation in the pathogenesis and clinical progression of healthcare-associated infections (HAIs), particularly in resource-constrained tertiary care settings. With a biofilm positivity rate of 63% among culture isolates, our findings are in line with global data demonstrating biofilm as a dominant phenotype in nosocomial pathogens [10]. The majority of biofilm-forming isolates in our cohort were multidrug-resistant (MDR), significantly prolonging hospital stay and complicating treatment, which supports the concept of the “biofilm–antibiotic resistance–infection” triangle, a relationship now widely recognized in clinical microbiology [11].

Mechanistically, biofilms confer resistance through various adaptations, including efflux pump overexpression, restricted antimicrobial penetration, and phenotypic heterogeneity of embedded bacteria [12,13]. Notably, Pseudomonas aeruginosa and Acinetobacter baumannii, both prominent in our sample, have been previously implicated in efflux-mediated resistance and heightened virulence when forming biofilms in hospital settings [14]. Our logistic regression model also demonstrated that isolation of Enterococcus faecalis was independently associated with biofilm formation, highlighting the need to consider Gram-positive cocci, in addition to Gram-negative bacilli, as key contributors to biofilm-related morbidity.

The relationship between biofilm formation and antimicrobial resistance is well established and bidirectional; not only do biofilms promote resistance, but exposure to sub-inhibitory antibiotic levels can also stimulate biofilm formation via stress-response signaling pathways [15]. This dynamic becomes particularly problematic in intensive care units, where device-related infections such as CLABSIs, CAUTIs, and VAP dominate the nosocomial infection burden [16,17]. In our study, a substantial proportion of biofilm producers were isolated from catheterized patients, supporting prior observations that 60–70% of all nosocomial infections are device-associated and biofilm-driven [18]. Of particular concern is the growing evidence of waterborne biofilm reservoirs within hospital plumbing systems. Recent surveillance data have documented clinically significant pathogens—including Staphylococcus spp., Acinetobacter, and Pseudomonas—harboring resistance genes and forming resilient biofilms in hospital water distribution systems [19,20]. These environmental sources can act as persistent reservoirs for HAIs, especially in wards with high patient turnover or where immunocompromised individuals are housed. Our findings that a considerable number of MDR isolates came from ICUs echo this concern and suggest an urgent need for enhanced surveillance of biofilms in environmental reservoirs.

In light of these observations, interventions such as routine screening for strong biofilm producers, integration of antimicrobial stewardship programs, and implementation of surface decontamination strategies may help reduce nosocomial transmission. Novel strategies—such as antimicrobial surface coatings, shown to reduce HAI incidence by up to 36% in clinical trials—should also be explored in high-burden settings [21].

Biofilm formation is highly prevalent among HAI pathogens and strongly correlates with antimicrobial resistance and prolonged hospitalization. Routine screening for biofilm-forming capacity and targeted infection control measures are warranted to improve patient outcomes in high-risk hospital environments

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