A web-based predictive model for secondary skin infections in breast cancer patients undergoing reconstruction.

Background: Breast cancer (BC) is one of the most common malignancies in women worldwide, with surgical interventions such as mastectomy and implant-based reconstruction playing a key role in management. While implant-based reconstruction offers immediate breast contour restoration, complications such as infection, capsular contracture, and implant failure are influenced by patient-specific factors, including age, body mass index (BMI), smoking, and adjuvant therapies like radiation. This study aimed to develop a predictive model for postoperative skin infections to enhance personalized risk assessment and optimize surgical outcomes in BC patients.

Methods: This retrospective study included 166 Chinese female patients with BC who underwent unilateral mastectomy followed by implant-based reconstruction. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for postoperative skin infections. A nomogram was constructed based on significant variables, with its accuracy assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: The 166 patients were divided into training and validation cohorts (6:4). Univariate analysis identified BMI, chemotherapy, radiotherapy, and prosthesis thickness as significant factors for postoperative skin infections. Multivariate analysis confirmed BMI, chemotherapy, and prosthesis thickness as independent risk factors. The predictive model demonstrated strong performance, with area under the curve (AUC) values of 0.87 and 0.812 for the training and validation cohorts, respectively. Calibration curves showed good agreement between predicted and observed outcomes, and DCA confirmed the model's clinical utility. A web-based calculator was developed to estimate infection risk (https://kevinpan.shinyapps.io/InfectionStatus/).

Conclusions: BMI, prosthesis thickness, and chemotherapy are key factors influencing the risk of postoperative skin infections in BC patients undergoing implant-based reconstruction. The predictive model developed in this study provides a valuable tool for clinicians to assess risk and personalize treatment plans. Further studies with larger cohorts are needed to validate and refine the model for broader clinical use.