Predicting reoperation and readmission for head and neck free flap patients using machine learning.
Head Neck
; 46(8): 1999-2009, 2024 Aug.
Article
in En
| MEDLINE
| ID: mdl-38357827
ABSTRACT
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.Key words
Full text:
1
Database:
MEDLINE
Main subject:
Patient Readmission
/
Reoperation
/
Plastic Surgery Procedures
/
Free Tissue Flaps
/
Machine Learning
/
Head and Neck Neoplasms
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
En
Year:
2024
Type:
Article