ArticleJournal of surgical oncology2025
Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach.
Article in Journal of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Article
- Artificial Intelligence-predicted Outcomes of Breast Reconstruction: Progress, Pitfalls, and Path Forward.Plastic and reconstructive surgery. Global open · 2026Article
- Artificial Intelligence and Robotic Surgery in Lymphedema: A Scoping Review of Current Applications, Clinical Translation, and Evidence Gaps.Plastic and reconstructive surgery. Global open · 2026Article
- Using Machine Learning to Predict Breast Cancer-Related Lymphedema Following Axillary Lymph Node Dissection.Annals of surgical oncology · 2026Article
- Artificial Intelligence in Breast Reconstruction: A Scoping Review of Pre-, Intra-, and Postoperative Applications.Plastic and reconstructive surgery. Global open · 2026Article
- Machine Learning Modeling of Hospital Length of Stay After Breast Cancer Surgery: Comparison of Random Forest and Linear Regression Approaches.Medicina (Kaunas, Lithuania) · 2025Article
- Artificial Intelligence in Breast Reconstruction: Enhancing Surgical Planning, Aesthetic Outcomes, and Patient-Centered Care.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundPredicting short-term postoperative complications after breast reconstruction is critical for improving patient outcomes and reducing costs. This study investigated the utility of machine learning (ML) algorithms to predict complications in breast reconstruction patients.
methodsData were collected from patients who underwent autologous, implant, and tissue expander-based reconstruction in the National Surgical Quality Improvement Program (NSQIP) database (2020-2022). Six ML models were trained to predict 30-day readmission, 30-day reoperation, and prolonged length of stay (LOS). Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, and Brier score. SHapley Additive exPlanations (SHAP) values ranked predictors influencing model outcomes.
resultsA total of 27 718 patients (5584 autologous; 8170 implant; 13 964 TE) were included. Top-performing models showed moderate to strong predictive performance across cohorts for all complications. AUCs ranged from 0.614 to 0.861. The highest AUCs were achieved for prolonged LOS in implants patients (AUC 0.861) and for 30-day readmission in the delayed autologous cohort (AUC 0.859). Key predictors of complications included operative time, BMI, age, reconstruction timing, and ASA class.
conclusionML can predict short-term postoperative outcomes in breast reconstruction patients. With further model refinement and data quality optimization, these models may improve preoperative risk stratification and patient outcomes in breast reconstruction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.