Background: Free flap breast reconstruction (FFBR) is a well-established technique for postmastectomy rehabilitation. However, chronic immunosuppressive therapy may compromise wound healing, increase infection susceptibility, and adversely affect surgical results. However, evidence regarding the impact of chronic immunosuppression on FFBR outcomes remains sparse.
Methods: We analyzed the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (2013-2023), identifying female breast cancer patients who underwent immediate FFBR. Chronic immunosuppression was defined as continuous systemic administration of corticosteroids or other immunosuppressants for ≥ 30 days preoperatively. Multivariable logistic regression and propensity score matching were applied to assess the impact of immunosuppression on surgical outcomes.
Results: Of 5473 patients (mean age 52 ± 9.4 years; mean BMI 30 ± 5.6 kg/m²), 139 (2.5%) were chronically immunosuppressed. In confounder-adjusted multivariable analysis, chronic immunosuppression was independently associated with an increased risk of any complication (OR 1.5; 95% CI, 1.0-2.2; P = .048) and surgical complications (OR 2.0; 95% CI, 1.3-3.0; P = .0011), particularly postoperative bleeding (23% vs. 10%; P < .001). No significant associations were observed with medical complications (P = .73), reoperations (P = .11), or readmissions (P = .45). Propensity score matching validated these correlations, revealing elevated odds of any complications (OR 1.7; P = .044) and surgical complications (OR 2.4; P = .0026) in chronically immunosuppressed patients.
Conclusion: Chronic immunosuppression doubles the risk of surgical complications following FFBR, with postoperative bleeding representing the predominant concern. These findings mandate enhanced perioperative surveillance and bleeding prevention protocols for immunosuppressed patients while supporting the continued feasibility of FFBR in this population when appropriate precautions are implemented.
Purpose: The purpose of this study is to develop a machine learning (ML) model to predict skeletal-related events (SREs) in patients with bone metastasis from breast cancer.
Patients and methods: Publicly available, patient-level data of patients with bone metastasis from breast cancer receiving zoledronic acid from a previous clinical trial was analyzed. Five feature sets (FS) and seven algorithms were utilized to develop ML models to predict SREs within 18 months. The model was trained with ten-fold cross-validation, repeated three times, and was evaluated through four-fold external cross-validation. Model performances were assessed by multiple metrics and the ability to differentiate cumulative risks of SREs. The model was interpreted by Shapley Additive Explanation.
(shap) results: Four hundred sixty cases with bone metastatic breast cancer were incorporated for ML model development. The ML model that utilized six features selected by the Boruta method and random forest algorithm demonstrated the numerically highest performance. (F1 score of 0.70) The mean absolute SHAP values suggested performance status, history of SREs, and serum alkaline phosphatase were the most important features. The ML model differentiated the cases with a high risk and a low risk of SREs, with median time for the first SRE of 248 days and 867 days, respectively. (Hazard ratio: 2.43 and 95% confidence interval: 1.86-3.18) CONCLUSION: A machine learning model to predict SREs in patients with bone metastatic breast cancer demonstrated the features related to SRE risk and its ability to identify the population with a high risk of SREs.

