Predictors of Surgical Site Infection in Clean Abdominal Surgeries: A Prospective Observational Study
Predictors of SSI in Clean Abdominal Surgery
DOI:
https://doi.org/10.69750/dmls.02.01.0163Keywords:
Surgical site infection, clean abdominal surgery, obesity, diabetes mellitus, operative duration, postoperative complicationsAbstract
Background: Surgical site infections (SSIs) remain one of the most frequent postoperative complications, even in clean abdominal surgeries that traditionally carry a low infection risk. Identifying key predictors of SSI is essential for improving outcomes and reducing preventable morbidity in surgical patients.
Objective: To determine the incidence and independent predictors of surgical site infection in patients undergoing elective clean abdominal surgeries at Shaikh Zayed Medical Complex, Lahore.
Methods: A prospective observational study was conducted from January 2022 to March 2023, including 120 patients undergoing clean abdominal surgeries. Demographic variables, comorbidities, preoperative laboratory findings, operative characteristics, and postoperative outcomes were recorded. Patients were followed for 30 days, and SSIs were diagnosed based on CDC criteria. Statistical analysis included univariate testing and multivariate logistic regression to identify independent predictors of SSI.
Results: The incidence of SSI was 14.1% (17/120). Patients with SSI had significantly higher rates of obesity (BMI >30 kg/m²; p = 0.004), diabetes mellitus (p = 0.009), and preoperative hemoglobin <10 g/dL (p = 0.03). Operative duration >120 minutes was strongly associated with infection (p = 0.002). Multivariate analysis identified obesity (AOR 3.42), diabetes mellitus (AOR 2.89), and operative duration >120 minutes (AOR 4.15) as independent predictors of SSI. Age, gender, smoking, drain placement, and surgical approach showed no significant association.
Conclusion: Obesity, diabetes, and prolonged operative duration significantly increase the risk of SSI in clean abdominal surgeries. Preoperative optimization and improved intraoperative efficiency are crucial for reducing postoperative infection rates.
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