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Predicting Breast Reconstruction Readmission, Reoperation, and Prolonged Length of Stay: A Machine Learning Approach.

Ariel J Gabay1, Jonlin Chen1, Carrie S Stern1

  • 1Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

Journal of Surgical Oncology
|June 20, 2025
PubMed
Summary

Machine learning models can predict short-term complications after breast reconstruction, aiding in risk stratification. These models show moderate to strong performance, identifying key predictors like operative time and BMI.

Keywords:
autologousbreast reconstructionimplantmachine learningprolonged length of stayreadmissionreoperation

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Area of Science:

  • Plastic Surgery
  • Medical Informatics
  • Surgical Outcomes Research

Background:

  • Predicting postoperative complications after breast reconstruction is crucial for patient outcomes and cost reduction.
  • Machine learning (ML) algorithms offer a novel approach to identifying patients at high risk for complications.

Purpose of the Study:

  • To investigate the utility of ML algorithms in predicting short-term postoperative complications in breast reconstruction patients.
  • To assess the predictive performance of various ML models for readmission, reoperation, and prolonged length of stay.

Main Methods:

  • Utilized data from the National Surgical Quality Improvement Program (NSQIP) database (2020-2022) for 27,718 breast reconstruction patients.
  • Trained six ML models to predict 30-day readmission, 30-day reoperation, and prolonged length of stay (LOS).
  • Evaluated model performance using AUC, sensitivity, specificity, and Brier score; SHAP values identified key predictors.

Main Results:

  • ML models demonstrated moderate to strong predictive performance across all complication types, with AUCs ranging from 0.614 to 0.861.
  • Highest AUCs were observed for prolonged LOS in implant patients (0.861) and 30-day readmission in delayed autologous reconstruction (0.859).
  • Key predictors identified include operative time, BMI, age, reconstruction timing, and ASA class.

Conclusions:

  • ML models can effectively predict short-term postoperative outcomes in breast reconstruction.
  • Further refinement and data optimization of these ML models hold potential for improved preoperative risk stratification.
  • Enhanced risk stratification can lead to better patient outcomes in breast reconstruction surgery.