Post-operative wound infections (POWIs) are among the most common complications following surgical procedures, contributing significantly to patient morbidity, prolonged hospital stay, increased healthcare costs, and in severe cases, mortality [1,2]. Surgical site infections (SSIs), a subset of POWIs, account for a substantial proportion of nosocomial infections, with incidence varying between 2% and 20% depending on the type of surgery, patient population, and healthcare setting [3,4]

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The pathogenesis of post-operative wound infections is multifactorial, involving host factors such as diabetes mellitus, immunosuppression, and age, as well as procedural factors including duration of surgery, use of implants, and adherence to aseptic techniques [5,6]. The microbial flora responsible for POWIs is diverse, with Gram-positive cocci, especially Staphylococcus aureus, and Gram-negative bacilli, including Escherichia coli, Pseudomonas aeruginosa, and Klebsiellapneumoniae, being the most frequently isolated pathogens [7,8]. Methicillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum beta-lactamase (ESBL) producing Gram-negative bacteria have emerged as significant contributors to antimicrobial resistance in these infections, posing a challenge for effective management [9,10].

Early identification of the causative organisms and their antimicrobial susceptibility patterns is crucial for initiating appropriate empirical therapy and guiding rational antibiotic use [11]. Antimicrobial stewardship programs have emphasized the need for local surveillance studies to monitor pathogen prevalence and resistance trends, which can aid in updating hospital antibiotic policies and reducing the burden of multidrug-resistant infections [12,13].

Despite the global burden of POWIs, there is limited data from tertiary care centers in India documenting the bacterial profile, prevalence of resistant strains, and their antibiotic susceptibility patterns. Understanding these parameters is essential for improving post-operative care and reducing infection-related complications [14,15]. This study was therefore designed to evaluate the bacteriological profile of post-operative wound infections, analyze the prevalence of drug-resistant strains, including MRSA and ESBL producers, and determine their antimicrobial susceptibility patterns in patients attending a tertiary care hospital.

MATERIALS AND METHODS

Study Locale

The study was conducted in the Diagnostic Microbiology Division, Central Service Laboratory, Karpagam Faculty of Medical Sciences and Research, Othakkalmandapam, Coimbatore.

Study Population

A total of 250 patients with post-operative wound infections attending as outpatients and inpatients in various surgical departments of our hospital during the study period were included in the study.

Study Design

This was a hospital-based cross-sectional study.

Study Period

The study was conducted over one year and six months, from 1st January 2019 to 30th June 2020.

Sampling Method

Continuous sampling was employed.

Sample Size

The sample size was calculated using the formula:

Where:

• Prevalence (P) = 20%
• Confidence Interval (CI) = 95%
• Margin of error (e) = 5%
• Z value = 1.654

Sample size taken for the study: 250 patients.

Inclusion Criteria

• All inpatients and outpatients of both genders in the post-operative period attending various surgical departments.

Exclusion Criteria

1. Patients on definitive antimicrobial therapy in the last 1 week.
2. Patients unwilling to provide informed consent.
3. Patients with stitch abscesses or focal sepsis.
4. Patients on immunosuppressive drugs.
5. Immunocompromised patients.

Methodology

Ethical Approval and Consent

• The study was conducted after obtaining approval from the Institutional Human Ethics Committee (IHEC).
• Informed consent was obtained from all participants in the vernacular language.

Patient Data Collection

• Relevant past medical history including diabetes mellitus, hypertension, bronchial asthma, ischemic heart disease, etc., was recorded.

Sample Collection

• Specimens (pus, tissue material, wound discharge) were collected from surgical wounds showing signs of infection 48 hours post-operation or during follow-up for 30 days.
• Wounds were wiped with sterile saline; two swabs were collected from the depth of the wound.
• Color, consistency, and odor were noted, followed by smear examination and culture.

Specimen Processing

1. Macroscopic examination (color, consistency, odor)
2. Direct Gram staining
3. Culture on Nutrient agar, Blood agar, and MacConkey agar
4. Preliminary identification by colony morphology
5. Biochemical characterization for species identification
6. Antimicrobial susceptibility testing

Microscopy

• Direct Gram smear: first swab smeared, heat-fixed, and Gram-stained. Presence of pus cells, Gram reaction, size, shape, and arrangement of organisms were noted.

Culture of Organisms

• The second swab was inoculated onto Nutrient agar, 5% Sheep Blood agar, and MacConkeyagarand incubated at 37 °C for 24 – 48 hours.
• Blood agar was incubated in 5-10% CO₂.
• Plates were observed after 24-48 hours for colony growth and hemolysis.

Identification of Pathogens

• Gram-positive cocci (GPC): Catalase, coagulase (slide and tube), mannitol motility, bile esculin, heat tolerance, and OF tests.
• Gram-negative bacilli (GNB): Motility, oxidase, indole, citrate, urease, MR, VP, TSI, nitrate reduction, sugar fermentation.

Controls were included in all tests (e.g., Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922, Enterococcus faecalis ATCC 29212, Pseudomonasaeruginosa ATCC 27853).

Antimicrobial Susceptibility Testing

• Performed using modified Kirby-Bauer disk diffusion method on Mueller-Hinton agar.
• Inoculum turbidity adjusted to 0.5 McFarland standard.
• Antibiotic discs placed ≥25 mm apart and incubated at 35-37°C for 16-18 hours.
• GNB and GPC were tested with antibiotics relevant to their species, including cephalosporins, aminoglycosides, fluoroquinolones, and glycopeptides.

Detection of ESBL Producers

1. Double Disk Synergy Test: synergy between third-generation cephalosporin and amoxicillin/clavulanate indicated ESBL production.
2. ESBL Screening: isolates with reduced zone diameters were subjected to confirmatory tests using cephalosporin/clavulanate disks or broth microdilution.

Detection of MRSA

• Staphylococcus aureus isolates were screened using cefoxitin (30 μg) disk diffusion according to CLSI guidelines.

D-Test for Inducible Clindamycin Resistance

• Performed on erythromycin-resistant, clindamycin-susceptible isolates by placing erythromycin and clindamycin disks 15–20 mm apart.

Statistical Analysis

• Data were analysed using SPSS and Epi-Info.
• Proportional data were evaluated using the Chi-square test and Binomial proportion test.

RESULTS AND OBSERVATIONS

Table 1: Age and Gender Distribution of Study Population (N = 250)

Table 2: Occupation and Socioeconomic Status of Study Population (N = 250)

Table 3: Patient Status and Wound Site Distribution in Study Population (N = 250)

Table 4: Types of Surgery and Post-Operative Day (POD) Distribution in Study Population (N = 250)

Table 5: Complications and Wound Type Distribution in Study Population (N = 250)

Figure 1: Descriptive analysis of growth rate from different types of wounds

Table 6: Types of Organisms and Distribution of Gram-Negative and Gram-Positive Bacteria in Study Population

Table 7: Distribution of Bacterial Species in Wound Infections (N = 184)

Table 8: Distribution of MSSA and MRSA among Staphylococcus aureus

Table 9: Antibiotic Sensitivity and Resistance Patterns of Major Isolates

Table 10: Sensitivity and Resistance Pattern of Pseudomonas aeruginosa and Acinetobacter Species

Table 11: Sensitivity and Resistance Pattern of Proteus vulgaris, Proteus mirabilis, and Morganellamorganii

Table 12: Drug-Resistant Strains and Distribution of ESBL and MDR among Isolates (N=184)

DISCUSSION

Post-operative wound infections (POWIs) remain a significant challenge in surgical practice, often leading to prolonged hospitalisation, increased morbidity, and higher healthcare costs [16]. In the present study of 250 post-operative patients, the overall culture positivity rate was 73.6% (184/250), with 37.6% mono-microbial and 18% polymicrobial growth. Similar findings were reported by Sharma et al., where the rate of bacterial isolation in surgical wounds was 70–75% [17].

Demographic Profile

The mean age of patients in our study was 49.14 ± 16.58 years, with a male predominance (57.2%). This is consistent with previous studies indicating that middle-aged and elderly patients are more prone to POWIs, possibly due to comorbidities such as diabetes mellitus and hypertension [18,19]. In our cohort, 18% of patients had diabetes mellitus, 3.6% had hypertension, and 5.6% had both, which likely contributed to increased susceptibility to infection. Host-related factors such as diabetes have been associated with impaired wound healing and increased risk of surgical site infection [20].

Distribution of Wound Types and Surgery

Most wounds were classified as “clean + contamination” (45.2%) and “clean” (40%), with the remainder being contaminated (6%) or dirty (8.8%). Excision procedures (20.8%) and split skin grafting (12.4%) were the most common surgeries. The predominance of contaminated or clean-contaminated wounds correlates with the higher prevalence of Gram-negative bacilli, as reported by Allegranzi et al. [21]. POWIs are more frequent in surgeries involving tissue manipulation or foreign body implantation, consistent with our findings in appendicectomy, hernioplasty, and orthopedic procedures.

Microbiological Profile

Among 184 bacterial isolates, Gram-negative bacilli predominated (64.6%) over Gram-positive cocci (35.4%), with Escherichia coli (20.1%), Pseudomonas aeruginosa (16.8%), and Klebsiellapneumoniae (8.2%) being the most frequent. Among Gram-positive isolates, Staphylococcus aureus (34.3%) was predominant, of which 22/63 (34.9%) were MRSA. Similar trends have been documented in Indian tertiary care hospitals, where Gram-negative bacteria account for 60–70% of post-operative wound infections [22,23]. The high prevalence of E. coli and Pseudomonas may reflect endogenous gut and skin flora contamination during surgery [24].

Antimicrobial Susceptibility Patterns

MRSA isolates showed 100% sensitivity to vancomycin and high sensitivity to linezolid (90.9%) and clindamycin (86.4%), consistent with CLSI guidelines and previous studies highlighting vancomycin and linezolid as first-line agents against MRSA [25,26]. MSSA isolates retained full sensitivity to clindamycin, vancomycin, and linezolid, confirming the continued efficacy of these drugs for Gram-positive infections.

Among Gram-negative bacilli, E. coli exhibited high resistance to ampicillin (100%), ciprofloxacin (89.2%), and cephalosporins (CTX 78.4%), whereas aminoglycosides (amikacin 75.7%) and carbapenems (imipenem 89.2%) remained highly effective. Similar resistance trends have been reported in other Indian studies, indicating the emergence of ESBL-producing and multidrug-resistant strains in surgical wounds [27,28]. Klebsiellapneumoniae and Enterobacter species also showed marked resistance to fluoroquinolones and beta-lactams, with carbapenems retaining 80–100% sensitivity.

For non-fermenting Gram-negative bacilli, Pseudomonas aeruginosa showed high sensitivity to amikacin, meropenem, imipenem, and levofloxacin (90.9% each), whereas ciprofloxacin sensitivity was lower (48.5%). Acinetobacter species demonstrated 100% sensitivity to most tested antibiotics, except for ampicillin, cefuroxime, amoxicillin-clavulanate, and cotrimoxazole, which showed 100% resistance. These results align with previous reports highlighting amikacin and carbapenems as the most reliable agents for Pseudomonas infections [29,30].

Among Proteus and Morganella species, high sensitivity to piperacillin and imipenem (100%) was observed, whereas resistance to ciprofloxacin and ampicillin was notable. This reflects the need for guided therapy, as empirical use of fluoroquinolones may be ineffective in these infections [31].

Drug-Resistant Strains

In this study, MRSA constituted 12% of isolates, ESBL-producing Gram-negative bacteria 28.3%, and multidrug-resistant (MDR) strains 5.4%. Escherichia coliwas the predominant ESBL producer (53.8%), followed by Klebsiellapneumoniae (15.4%). Among MDR strains, E. coli, Klebsiellapneumoniae, and Enterobacter species each accounted for 30%, and Pseudomonas aeruginosa 10%. These findings are consistent with reports from tertiary care hospitals, highlighting the growing prevalence of multidrug-resistant organisms in surgical site infections [32,33].

Clinical Implications

The high prevalence of Gram-negative bacteria and resistant strains underscores the importance of local antimicrobial surveillance. Empirical therapy for POWIs should consider local resistance patterns, with carbapenems and aminoglycosides reserved for severe infections, and vancomycin or linezolid for MRSA. Rational antibiotic stewardship and adherence to aseptic surgical techniques are essential to limit the emergence of resistant strains [34,35].

CONCLUSION

Post-operative wound infections remain a significant cause of morbidity in surgical patients, with a predominance of Gram-negative bacilli, particularly Escherichia coli and Pseudomonas aeruginosa, alongside Gram-positive Staphylococcus aureus. The study highlights a considerable burden of multidrug-resistant organisms, including MRSA (12%) and ESBL-producing Gram-negative bacteria (28.3%), emphasising the need for targeted antibiotic therapy. Carbapenems and aminoglycosides demonstrated the highest efficacy against Gram-negative isolates, while vancomycin and linezolid were effective against MRSA. Rational antibiotic stewardship, strict adherence to aseptic surgical techniques, and local antimicrobial surveillance are essential to curb the emergence of resistant pathogens and improve post-operative outcomes.

REFERENCES

1. Mangram AJ, Horan TC, Pearson ML, Silver LC, Jarvis WR. Guideline for prevention of surgical site infection, 1999. Infect Control HospEpidemiol. 1999;20(4):250–278.
2. Kirkland KB, Briggs JP, Trivette SL, Wilkinson WE, Sexton DJ. The impact of surgical-site infections in the 1990s: Attributable mortality, excess length of hospitalization, and extra costs. Infect Control HospEpidemiol. 1999;20(11):725–730.
3. Allegranzi B, BagheriNejad S, Combescure C, et al. Burden of endemic health-care-associated infection in developing countries: systematic review and meta-analysis. Lancet. 2011;377:228–241.
4. Ban KA, Minei JP, Laronga C, et al. American College of Surgeons and Surgical Infection Society: Surgical site infection guidelines, 2016 update. J Am Coll Surg. 2017;224(1):59–74.
5. de Lissovoy G, Fraeman K, Hutchins V, Murphy D, Song D, Vaughn BB. Surgical site infection: incidence and impact on hospital utilization and treatment costs. Am J Infect Control. 2009;37(5):387–397.
6. Whitehouse JD, Friedman ND, Kirkland KB, Richardson WJ, Sexton DJ. The impact of surgical-site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control HospEpidemiol. 2002;23(4):183–189.
7. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care–associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36(5):309–332.
8. Choudhry AJ, Sharma A, Misra MC. Wound infection in orthopaedics: A prospective study of incidence and risk factors in a tertiary care center. Indian J Orthop. 2011;45(2):167–171.
9. Chambers HF, Deleo FR. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat Rev Microbiol. 2009;7:629–641.
10. Paterson DL, Bonomo RA. Extended-spectrum β-lactamases: a clinical update. ClinMicrobiol Rev. 2005;18(4):657–686.
11. Mangram AJ. Surgical site infections: epidemiology and prevention. SurgClin North Am. 1999;79(2):437–454.
12. Berríos-Torres SI, Umscheid CA, Bratzler DW, et al. Centers for Disease Control and Prevention guideline for the prevention of surgical site infection, 2017. JAMA Surg. 2017;152(8):784–791.
13. Cosgrove SE. The relationship between antimicrobial resistance and patient outcomes: mortality, length of hospital stay, and health care costs. Clin Infect Dis. 2006;42(Suppl 2):S82–S89.
14. Sivanandan S, Gupta D, Sehgal R. Bacteriological profile of post-operative wound infections in a tertiary care hospital. Indian J Med Microbiol. 2015;33(4):584–588.
15. Sharma S, Sharma D, Lal P. Antibiotic susceptibility pattern of bacterial isolates from post-operative wound infections in a tertiary care hospital. Int J Med Microbiol. 2016;306(4):335–340.

16. Allegranzi B, BagheriNejad S, Combescure C, et al. Burden of endemic health-care-associated infection in developing countries: systematic review and meta-analysis. Lancet. 2011;377:228–241.
17. Sharma S, Sharma D, Lal P. Antibiotic susceptibility pattern of bacterial isolates from post-operative wound infections in a tertiary care hospital. Int J Med Microbiol. 2016;306(4):335–340.
18. Choudhry AJ, Sharma A, Misra MC. Wound infection in orthopaedics: A prospective study of incidence and risk factors in a tertiary care center. Indian J Orthop. 2011;45(2):167–171.
19. Whitehouse JD, Friedman ND, Kirkland KB, et al. The impact of surgical-site infections following orthopedic surgery. Infect Control HospEpidemiol. 2002;23(4):183–189.
20. Martin ET, Kaye KS, Knott C, et al. Diabetes and risk of surgical site infection: a systematic review and meta-analysis. Infect Control HospEpidemiol. 2016;37(1):88–99.
21. Allegranzi B, BagheriNejad S, et al. World Health Organization guidelines for the prevention of surgical site infections. Lancet Infect Dis. 2016;16:e188–e201.
22. Ban KA, Minei JP, Laronga C, et al. American College of Surgeons and Surgical Infection Society: Surgical site infection guidelines, 2016 update. J Am Coll Surg. 2017;224(1):59–74.
23. Ghotaslou R, et al. Bacteriological profile of post-operative wound infections in an Iranian tertiary care hospital. J Infect DevCtries. 2014;8:1202–1207.
24. Horan TC, et al. CDC/NHSN surveillance definition of health care–associated infection. Am J Infect Control. 2008;36:309–332.
25. Chambers HF, Deleo FR. Waves of resistance: Staphylococcus aureus in the antibiotic era. Nat Rev Microbiol. 2009;7:629–641.
26. Performance Standards for Antimicrobial Susceptibility Testing, 31st edition. Clinical and Laboratory Standards Institute; 2021.
27. Paterson DL, Bonomo RA. Extended-spectrum β-lactamases: a clinical update. ClinMicrobiol Rev. 2005;18:657–686.
28. Gupta V, et al. Antimicrobial resistance among Gram-negative pathogens in India. Indian J Med Res. 2017;145:1–7.
29. Poole K. Pseudomonas aeruginosa: resistance to the max. Front Microbiol. 2011;2:65.
30. Tamma PD, Cosgrove SE, Maragakis LL. Combination therapy for treatment of infections with Gram-negative bacteria. ClinMicrobiol Rev. 2012;25:450–470.
31. Sivanandan S, Gupta D, Sehgal R. Bacteriological profile of post-operative wound infections. Indian J Med Microbiol. 2015;33:584–588.
32. Veeramachaneni R, et al. Multidrug-resistant organisms in surgical site infections: an Indian experience. J Lab Physicians. 2017;9:136–142.
33. Ranjan N, et al. Prevalence of ESBL-producing Gram-negative bacteria in surgical wounds. IntSurg J. 2016;3:1294–1299.
34. Berríos-Torres SI, et al. CDC guideline for the prevention of surgical site infection, 2017. JAMA Surg. 2017;152:784–791.
35. Cosgrove SE. The relationship between antimicrobial resistance and patient outcomes. Clin Infect Dis. 2006;42(Suppl 2):S82–S89.