Introduction

Healthcare-associated infections (HAIs), including SSIs, are a major global health concern affecting both developed and developing countries (1). The prevalence of HAIs varies significantly worldwide, ranging from 1.5% to 25% globally, with rates in Iran reported between 3.1% and 10%. SSIs are among the most common and severe HAIs, particularly in patients undergoing surgical procedures (2-4). These infections can lead to increased mortality, prolonged hospital stays, and higher healthcare costs (5, 6). The introduction of implants, drains, and surgical techniques significantly influences the incidence of SSIs, making it imperative to address these factors in the context of orthopedic surgeries (7, 8). As such, understanding the risk factors, microbial profiles, and antibiotic resistance patterns associated with SSIs in orthopedic surgery is crucial for developing effective prevention and treatment strategies.

Orthopedic surgeries are particularly susceptible to SSIs due to the frequent use of implants such as screws, plates, and prosthetic joints. These foreign materials provide surfaces for bacterial adhesion and biofilm formation, which are resistant to both the host immune response and antibiotic treatment (9, 10). Consequently, infections involving implants can be challenging to eradicate and often require additional surgical interventions. Moreover, the presence of comorbid conditions such as diabetes and hypertension further complicate the management of SSIs in orthopedic patients (11, 12).

The microbial landscape of SSIs in orthopedic surgeries is dominated by a variety of pathogens, with Staphylococcus aureus being the most prevalent (13). Methicillin-resistant Staphylococcus aureus (MRSA) poses a particular challenge due to its resistance to multiple antibiotics, complicating treatment regimens (14). The resistance patterns of these pathogens necessitate continuous monitoring and updating of antibiotic prophylaxis protocols to ensure their efficacy (15).

This study aims to provide a comprehensive analysis of the risk factors, microbial profiles, and antibiotic resistance patterns associated with SSIs in orthopedic surgery patients over a ten-year period at the Babol University of Medical Sciences. By identifying the key determinants of infection and resistance trends, this research seeks to inform clinical practices and contribute to the development of targeted strategies for reducing the incidence and severity of SSIs in orthopedic settings.

Materials and Methods

Study Design

This study is a retrospective observational analysis spanning a ten-year period from 2012 to 2022. It investigates the incidence, risk factors, microbial profiles, and antibiotic resistance patterns associated with surgical site infections (SSIs) in patients who underwent orthopedic surgeries at Babol University of Medical Sciences.

Study Population

Patients who underwent elective orthopedic surgeries for conditions such as fractures, deformities, degenerative diseases, or osteopathies. whose complete medical records and follow-up data was accessible, and diagnosed with SSIs based on the Centers for Disease Control and Prevention (CDC) definition (16) were included in this study. Based on the definition criteria for SSI provided by the CDC, a superficial SSI was defined as an infection occurring within 30 days after surgery and involving only the skin or subcutaneous tissues. A deep SSI was defined as an infection occurring within 1 year after surgery involving deep soft tissue (muscle, bone, or other). Deep SSI was further defined based on one or more of the following conditions: (a) persistent wound discharge or separation from the deep incision; (b) Visible abscess or gangrene requiring surgical debridement and removal or replacement of the implant. and (c) the frequency of positive cultures from the deep cut site.

Patients with incomplete medical records, cases of dislocations or non-fracture-related orthopedic conditions, patients who underwent minimally invasive surgeries such as arthroscopic debridement or percutaneous vertebroplasty, patients admitted solely for SSI treatment without primary orthopedic surgery, and diabetic foot osteomyelitis patients who underwent amputation were excluded.

Data Collection

Data were systematically extracted from patient medical records, including demographic information (age, gender, height, weight, estimated BMI (kg/m2), previous surgery at any site, preoperative and total hospitalization), patient comorbidities (diabetes mellitus, hypertension, heart disease, anemia, rheumatologic disease, kidney disease, steroid use, liver disease, chronic obstructive pulmonary disease (COPD) or asthma, and food/drug allergies.), surgical details (type of surgery, duration of surgery, type of anesthesia, use of implants, blood transfusion requirements, days of hospitalization, use of urinary catheters and surgical drains), and preoperative laboratory indicators (white blood cell count, lymphocyte count, hemoglobin level, platelet count, total serum protein, albumin level, albumin/globulin value, microbial profile of wound culture and antibiotic resistance pattern.

Patients were divided by BMI criteria (17) defined as underweight: <18.5; Normal: 18.5 to 23.9; overweight, 24 to 27.9; obesity: 28 to 31.9; Morbid obesity: 32 and more. Smoking or alcohol consumption was defined as positive if the patients admitted that they had consumed at least once in the 1 month before surgery. Preoperative stay was defined as the interval between admission and operation. Biochemical indices were divided into two or more groups based on the normal range of reference values.

Microbial Analysis

Wound swabs were collected from infected surgical sites under sterile conditions and processed in the microbiology laboratory. Initial screening for bacterial presence and preliminary classification was performed by gram staining; Samples were inoculated on selective and differential media (blood agar, MacConkey agar) and incubated at 37°C for 24-48 hours; Isolates were subjected to a battery of biochemical tests (catalase, coagulase, oxidase tests) for definitive identification of bacterial species.

Antibiotic susceptibility was determined using the Kirby-Bauer disk diffusion method, adhering to Clinical and Laboratory Standards Institute (CLSI) guidelines for Beta-lactams, Glycopeptides, Aminoglycosides, Quinolones, Tetracyclines and Macrolides. Zone diameters were measured and interpreted to classify isolates as susceptible, intermediate, or resistant to the tested antibiotics.

Statistical Analysis

Data were analyzed using SPSS software (version 26.0). Continuous variables were summarized as means and standard deviations (SD), while categorical variables were presented as frequencies and percentages. Each potential risk factor was initially examined using chi-square tests for categorical variables and t-tests for continuous variables. A p-value of <0.05 was considered statistically significant. Variables that were significant in the univariate analysis were subsequently included in a multivariate model to identify independent predictors of SSIs while controlling for potential confounders. Adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated.

Ethical Considerations

The study protocol was reviewed and approved by the Ethics Committee of Babol University of Medical Sciences (IR.MUBABOL.REC.1402.151). Given the retrospective nature of the study, informed consent was waived. Data confidentiality was maintained by anonymizing patient identifiers during data extraction and analysis.

Results

Demographic and Clinical Characteristics

Based on the inclusion criteria, over a 10-year period, 71 patients out of a total of 572 cases following orthopedic surgeries developed SSI. Of these, 20 patients were women (28.2%) and the remaining 51 patients were men (71.8%). The average age of the patients was 31.12 years. Thirty patients (42.2%) received general anesthesia. Twenty-nine patients (40.84%) were smokers, and 5 patients (7.04%) used opium. Additionally, 20 patients (28.16%) had diabetes mellitus (DM), and 18 patients (25.35%) had hypertension (HTN). The results showed that smoking, HTN, DM, previous hospitalization within the past month, BMI, urinary catheterization, general anesthesia, ASA Score less than 3, open drains, corticosteroid use, and surgery duration longer than 2 hours were significantly associated with the occurrence of SSI (all P values < 0.05). The demographic information of the patients included in the study, laboratory data, type of anesthesia, ASA score, type of drainage, and surgery duration are summarized in Table 1.

Classification of SSI cases based on the type of orthopedic surgery

These orthopedic surgeries included: Patella Fracture, Femoral Fracture, Screw and Plate Removal, Locking Pressure Plate (LCP) Placement, Open Reduction Internal Fixation (ORIF), Lower Extremity Surgery (Tibia and Fibula Fracture), External Fixation, Ankle Fracture, Wrist and hand fracture, arthroplasty, tendon repair and mass or foreign body removal. The frequency of SSI was significantly higher in people with orthopedic implants, so that the highest incidence of SSI (26 cases; 36.6%) was related to screw and plate surgeries. The results of the Chi-square test showed a significant relationship between the types of surgery and the incidence of SSIs, Table 2.

Microbial cultures in positive-culture SIIs

Out of the total 71 patients who developed SSI following various orthopedic surgeries, microbiological cultures were positive in 63 patients. The most prevalent pathogen was S. aureus (18 isolates; 28.57%), with a significant proportion being methicillin-resistant (MRSA) (8 isolates; 44.44%). Other reported pathogens included E. coli, A. baumannii, and K. pneumoniae. The antibiotic resistance patterns varied based on the type of surgery and the isolated microorganism. The predominant pathogen contaminating patellar fractures was S. aureus, which showed sensitivity to ceftazidime, vancomycin, and trimethoprim/sulfamethoxazole, while resistance was noted against cefoxitin, clindamycin, gentamicin, and ciprofloxacin. Escherichia coli isolated from these cases were sensitive to ceftazidime, trimethoprim/sulfamethoxazole, and gentamicin, but resistant to ceftriaxone, imipenem, clindamycin, and ciprofloxacin.

In Cases of Femoral Fractures, Staphylococcus aureus exhibited sensitivity to ceftazidime, vancomycin, and trimethoprim/sulfamethoxazole, while resistance was noted against cefoxitin, gentamicin, erythromycin, levofloxacin, ciprofloxacin, and rifampin. Escherichia coli showed sensitivity to ceftriaxone, piperacillin, and amikacin, with resistance to ceftazidime, trimethoprim/sulfamethoxazole, clindamycin, ciprofloxacin, gentamicin, and imipenem. In patients undergoing Screw and Plate Removal, Staphylococcus aureus was sensitive to cefotaxime, vancomycin, and gentamicin, and resistant to erythromycin, clindamycin, azithromycin, levofloxacin, ciprofloxacin, trimethoprim/sulfamethoxazole, and cefoxitin. Escherichia coli isolates were sensitive to imipenem, ceftriaxone, and amikacin, but resistant to ceftazidime, trimethoprim/sulfamethoxazole, ciprofloxacin, and gentamicin.

In SII cases following Hip Arthroplasty, Staphylococcus aureus isolates were sensitive to amikacin, ceftazidime, and clindamycin, but resistant to erythromycin, levofloxacin, ciprofloxacin, trimethoprim/sulfamethoxazole, and cefoxitin. Staphylococcus epidermidis showed sensitivity to ceftazidime and trimethoprim/sulfamethoxazole and resistance to erythromycin, levofloxacin, and ciprofloxacin.

Discussion

Surgical site infections (SSIs) in orthopedic surgeries pose a significant challenge due to their impact on patient morbidity, mortality, and the associated healthcare costs. Our decade-long study, spanning from 2012 to 2022, aimed to evaluate the risk factors, microbial profiles, and antibiotic resistance patterns in orthopedic surgery wounds at Babol University of Medical Sciences. Through meticulous analysis of patient records, we identified 71 SSI cases out of 572, reflecting a prevalence rate that aligns with regional data from other medical centers (18-20). On the other hand, these results are not consistent with Liang et al.'s study of 4,818 patients and the incidence of SSI in 1.5% (74 patients), and the reasons for this discrepancy can be pointed to the large sample size, geographical distance, and type of surgeries (21).

Risk Factors

The study identified several critical risk factors contributing to SSIs. These included smoking, hypertension (HTN), diabetes mellitus (DM), recent hospitalization within the last month, high body mass index (BMI), Urinary catheterization, General anesthesia, ASA scores < 3, use of open drains, corticosteroid use, and extended surgery durations (exceeding 2 hours). These factors were consistently observed in studies from other regions, such as Babol (20), Esfahan (18), Tehran (19), and India (8), underscoring their universal relevance in SSI risk assessment.

In our study, DM and HTN had a significant relationship with SSI. These results were aligned with the study of Arshad et al. in Pakistan (22) and Olsen et al. in America (23). Azizi et al. found that diabetic patients had a higher contamination rate (22.4%) than non-diabetic subjects (14.3%). Also, age, duration of hospitalization and surgery, general anesthesia, history of diabetes and smoking were determined as risk factors for SSI (20). Contrary to the findings of our study, Mehrpour et al. found that there was no significant difference in the groups with and without SSI with regard to chronic kidney disease, diabetes and fracture site (24).

Aligned with Azizi et al.'s study in Babol (20), in our study, the type of anesthesia and ASA score (<3) had a significant relationship with SSI. Suranigi et al. announced that the rate of SSI for American Society of Anesthesiologists (ASA) classification I, II, and III was 70.2%, 25.5%, and 4.25%, respectively (8). Consistent with the results of our study, a recent meta-analysis reported that obesity can increase the risk of surgical site infection approximately 2-fold in orthopedic patients (25). However, Azizi et al. (20) did not find a relationship between BMI and SII in their study.

A notable finding was the higher incidence of SSIs among patients under 35 years of age. This trend was similarly reported by Mosleh et al. in Esfahan (18) and Al-Mulhim et al. in Saudi Arabia (26), suggesting a link between younger age groups and higher infection rates. This could be due to the nature of injuries typically seen in younger populations, often resulting from trauma and necessitating longer surgical and hospitalization periods, both of which have been associated with increased SSI risks.

Our study showed a higher incidence of SII in males. This result is consistent with the majority of previous studies (26, 27), the possible cause of which is more traffic accidents in men. However, a study has shown that the incidence of SII following knee arthroplasty is more common in women (28).

Microbial Profile and Antibiotic Resistance

The microbial analysis revealed that Staphylococcus aureus, including methicillin-resistant Staphylococcus aureus (MRSA), was the predominant pathogen isolated from SSIs. This was followed by Gram-negative bacteria, particularly Pseudomonas aeruginosa and Acinetobacter baumannii. This pathogen profile is consistent with findings from most of similar studies and highlights the significant presence of these organisms in healthcare settings (26).

Suranigi et al. (8) showed that Acinetobacter baumannii and Staphylococcus aureus are the most common microorganisms isolated from SSI related to orthopedics. In Vietnam, Sohn et al. (29) found that out of a total of 702 patients operated on in orthopedic and neurosurgery departments, 80 patients (11.4%) had SSI. The three most common pathogens isolated were Pseudomonas aeruginosa (29.5%), Staphylococcus aureus (11.5%), and Escherichia coli (10.3%). 90% of Staphylococcus aureus isolates were resistant to methicillin (MRSA), 91% of Pseudomonas aeruginosa isolates were resistant to ceftazidime, and 38% of Escherichia coli isolates were resistant to cefotaxime. In a study by Maksimović et al. (30) in Serbia, of a total of 63 cases of SSI, 53 cases (84.1%) had positive cultures and 24 (45.3%) had polymicrobial infections. The most isolated bacteria were Staphylococcus aureus, Acinetobacter, Klebsiella/Enterobacter, Pseudomonas and Enterococcus species. The frequency of MRSA strains was 79.2% (19 out of 24 strains).

A concerning observation was the high incidence of multi-drug resistant (MDR) strains, especially among the Gram-negative bacteria. The presence of MDR organisms complicates treatment regimens, increases the duration of hospital stays, and elevates healthcare costs due to the necessity for more complex and prolonged antimicrobial therapy. This underscores the critical need for effective infection control measures and robust antimicrobial stewardship programs. Our findings emphasize the importance of continuous surveillance and targeted infection control strategies to combat the spread of MDR organisms. Surveillance data on pathogen trends and resistance patterns are essential for developing effective infection prevention protocols and optimizing antibiotic use in clinical practice. This approach can help mitigate the impact of MDR pathogens and improve patient outcomes.

Conclusion

This comprehensive ten-year investigation provides insights into the risk factors, microbial profiles, and antibiotic resistance patterns of SSIs in orthopedic surgery at Babol University of Medical Sciences. The study confirms that several patient and procedural factors significantly contribute to the risk of SSIs. Notably, the high incidence of infections among younger patients and the predominance of MDR pathogens highlight critical areas for intervention. Addressing SSIs in orthopedic surgery necessitates a comprehensive and multifaceted approach. By combining rigorous infection control measures, antimicrobial stewardship, targeted patient care interventions, and enhanced surveillance, healthcare facilities can significantly reduce the burden of SSIs. These strategies not only improve patient outcomes but also optimize resource utilization and enhance overall healthcare quality. Implementing these recommendations can help achieve a significant reduction in SSIs, ultimately leading to better patient care and reduced healthcare costs.

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