Machine Learning Accurately Predicts Patient-reported Outcomes 1 Year After Breast Reconstruction.

OBJECTIVE: To develop and evaluate machine learning algorithms for predicting patient-reported outcomes following breast reconstruction. BACKGROUND: Machine learning may inform patient-reported outcomes in breast reconstruction, possibly enhancing shared decision-making and tailoring patient care. METHODS: Data on patient characteristics, reconstructive technique, and BREAST-Q scores from women undergoing breast reconstruction at Memorial Sloan Kettering Cancer Center (MSKCC) between January 2010 and March 2024 was retrospectively collected. Five machine learning algorithms were developed and validated on this data to predict improved versus not improved BREAST-Q scores after reconstruction. Models were externally validated models using multicenter data from the Mastectomy Reconstruction Outcomes Consortium. Models were evaluated using the area under the receiver operator curve, sensitivity, specificity, and Brier score. RESULTS: A total of 4776 patients (2687 from MSKCC, 2089 from Mastectomy Reconstruction Outcomes Consortium) were included in model development and validation. Machine learning algorithms demonstrated area under the received operator curves of 0.97 for physical well-being of the abdomen, 0.86 for satisfaction with the breast, 0.79 for sexual well-being, 0.78 for physical well-being of the chest, and 0.74 for psychosocial well-being. Variables that contributed the most to model predictions across all domains were preoperative BREAST-Q scores, timing of radiation, body mass index, age, and reconstructive technique. CONCLUSIONS: Machine learning algorithms can accurately predict patient-reported outcomes before breast reconstruction. Ultimately, this data-driven approach may streamline shared decision-making and enhance patient-centered care.