Predicting reoperation and readmission for head and neck free flap patients using machine learning
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Updated: Jul 3, 2025
Porcine As a Training Module for Head and Neck Microvascular Reconstruction
Published on: September 29, 2018
Stephanie Y Wang1, Louis-Xavier Barrette1,2, Jinggang J Ng1
1Department of Otolaryngology - Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Background:
To develop machine learning (ML) models predicting unplanned readmission and reoperation among patients undergoing free flap reconstruction for head and neck (HN) surgery.
Methods:
Data were extracted from the 2012-2019 NSQIP database. eXtreme Gradient Boosting (XGBoost) was used to develop ML models predicting 30-day readmission and reoperation based on demographic and perioperative factors. Models were validated using 2019 data and evaluated.
Results:
Four-hundred and sixty-six (10.7%) of 4333 included patients were readmitted within 30 days of initial surgery. The ML model demonstrated 82% accuracy, 63% sensitivity, 85% specificity, and AUC of 0.78. Nine-hundred and four (18.3%) of 4931 patients underwent reoperation within 30 days of index surgery. The ML model demonstrated 62% accuracy, 51% sensitivity, 64% specificity, and AUC of 0.58.
Conclusion:
XGBoost was used to predict 30-day readmission and reoperation for HN free flap patients. Findings may be used to assist clinicians and patients in shared decision-making and improve data collection in future database iterations.
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