Development and validation of a Nomogram to predict postoperative flap necrosis risk in breast cancer patients...

Flap necrosis is a critical complication following modified radical mastectomy (MRM) for breast cancer (BC). It not only impairs wound healing but also delays postoperative treatment, adversely affecting patient survival rate and the overall quality of life. Thus, developing an accurate prediction model is crucial for early intervention and improving patient prognosis.

To develop and validate a Nomogram model based on Logistic regression to assess the risk of postoperative flap necrosis in BC patients undergoing MRM.

A retrospective study was conducted on 605 BC patients who underwent MRM. These patients were stratified into a training group (n=406) and a validation group (n=199) in a 33:67 ratio. Univariate and multivariate Logistic regression analyses were performed to identify risk factors for flap necrosis, and a Nomogram prediction model was subsequently constructed. The model's discriminatory power (assessed via the receiver operating characteristic [ROC] curve), calibration accuracy (evaluated by calibration curve), and clinical benefit (analyzed through decision curve analysis) were comprehensively evaluated. Moreover, essential performance metrics such as sensitivity, specificity, and accuracy were systematically recorded and analyzed.

Nine independent risk factors were identified, including age, body mass index (BMI), neutrophil count, hemoglobin level, drainage volume on the third postoperative day, axillary lymph node metastasis (ALNM), surgical duration, intraoperative bleeding volume, and drainage duration. The area under the curve (AUC) of the Nomogram model was 0.898 in the training group and 0.886 in the validation group, indicating good discriminatory capacity. Calibration curves demonstrated good agreement between predicted values and actual values, with P-values for goodness-of-fit of 0.1761 (training) and 0.0648 (validation), respectively. Decision curve analysis revealed significant clinical benefits, with maximum benefit rates of 76.84% (training) and 80.40% (validation), respectively.

The Nomogram model developed in this study accurately predicts flap necrosis risk in BC patients post-MRM, offering significant clinical utility for risk management and improved patient outcomes.