Closed Incisional Negative Pressure Wound Therapy is Cost-Effective at Reducing Superficial Surgical Site Infections

Background: Surgical site infections (SSIs) have a significant health economic burden, accounting for more than US $3.3 billion in costs, and lead to increased microbial resistance, prolonged hospital stays, elevated 30-day mortality rates, greater incidences of reoperation, and decreased quality of life. Recently, evidence has emerged suggesting that prophylactic closed incision negative pressure wound therapy (ciNPWT) may substantially reduce the risk of post-operative wound complications, specifically SSIs. This study aimed to evaluate whether ciNPWT is cost-effective compared with routine incision care for the prevention of superficial SSIs. Hypothesis: We hypothesized that ciNPWT is cost-effective compared with routine incision care for the prevention of superficial SSIs. Methods: A cost-effectiveness decision analytic model was created comparing the use and non-use of ciNPWT. Superficial SSI probabilities, cost of care for patients with and without post-operative infection, and quality of life Short Form (SF)-36 survey data were obtained from a literature review. Cost of ciNPWT was obtained from health administrative data. A decision tree was constructed using TreeAge Software Pro Version 2020 (TreeAge Software, Inc., Williamstown, MA). Deterministic and probabilistic sensitivity analyses were performed to evaluate the robustness and reliability of the model. Results: One-way sensitivity analysis with a willingness-to-pay threshold of $5,000 demonstrated that above a baseline infection rate of approximately 6.4%, ciNPWT is cost-effective at reducing superficial SSI. Probabilistic sensitivity analysis indicated that even with uncertainty present in the parameters analyzed, the majority of simulations (95.4%) favored ciNPWT as the more effective tactic. Conclusions: Despite the added device cost, ciNPWT is cost-effective for superficial SSI prevention across a variety of surgical infection risk profiles.