Data of 3,419 patients in 4 hospitals from Jan 1, 2012 to Dec 30, 2021 were retrospectively collected and were evaluated aiming to develop a point-based prediction model of deep surgical site infection in patients receiving open posterior instrumented thoracolumbar surgery. Clinical knowledge-driven, data-driven and decision tree model was used to identify predictive variables of deep SSI. Internal validation was performed by using bootstrapping methods.
Surgical site infection (SSI), particularly deep SSI, is one of the most serious complications after spinal surgery. evaluating the risk of SSI and, correspondingly, prescription of prophylactic measures are extremely important to prevent SSI and avoid potentially devastating consequences. A retrospective study was conducted aiming to develop a point-based prediction model of deep surgical site infection in patients receiving open posterior instrumented thoracolumbar surgery.