Identification and Validation of a Novel Model: Predicting Short-Term Complications After Local Flap Surgery for Skin Tumor Removal.

BACKGROUND The aim was to analyze the risk factors for the occurrence of complications after local flap transfer and to construct a simple prediction model to help surgeons in the perioperative screening of high-risk patients. MATERIAL AND METHODS Short-term complications were defined as any postoperative infection, dehiscence, bleeding, subcutaneous effusion, fat liquefaction, arteriovenous crisis, and tissue necrosis that required medical consultation or intervention. To explore 16 factors influencing short-term complications after local flap transfer, least absolute shrinkage and selection operator (LASSO) logistic regression was used to reduce the dimensionality of the data and to screen for predictors. Independent risk factors affecting the development of complications after local flap transfer were analyzed using logistic multiple regression models. The consistency (C-)index, receiver operating characteristic (ROC) curves, and calibration curves were used to check the model's discrimination and calibration. Decision curve analysis (DCA) curves were used to evaluate the clinical applicability of this model, and internal validation was assessed using bootstrap validation. RESULTS The C-index of the nomogram model to predict short-term complications after local flap transfer was 0.763 (95% CI: 0.702-0.824), the area under the ROC curve was 0.763, and the internal validation C-index was 0.747. The calibration curve showed good agreement between observed and predicted values, and the DCA showed the model can benefit patients. CONCLUSIONS The model identified the relevant factors influencing short-term complications after local flap transfer, facilitating the identification and targeted intervention of patients at high risk of flap complications after surgery.