Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP
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View abstract on PubMed
Summary
This summary is machine-generated.Accurate prediction of hospital length of stay (LOS) for oral cavity cancer (OCC) surgery is crucial. A machine learning model outperformed statistical models and the ACS-NSQIP calculator in predicting LOS.
Area Of Science
- Oncology
- Surgical Outcomes Research
- Health Informatics
Background
- Accurate prediction of hospital length of stay (LOS) after oral cavity cancer (OCC) surgery aids patient counseling and resource management.
- Existing prediction tools may lack sufficient accuracy for complex reconstructive procedures.
Purpose Of The Study
- To compare the predictive performance of statistical models, a machine learning (ML) model, and the ACS-NSQIP calculator for LOS in OCC patients.
- To identify novel predictors of LOS in this patient population.
Main Methods
- Retrospective multicenter study of 837 patients undergoing free flap reconstruction for OCC.
- Development and validation of statistical and ML models using training and validation datasets.
- Performance evaluation based on correlation coefficients and percent accuracy of predicted vs. actual LOS.
Main Results
- The ML model achieved the highest accuracy (validation correlation 0.48, 70% 4-day accuracy).
- Statistical models showed moderate performance (multivariate analysis: 0.45, 67%; LASSO: 0.42, 70%).
- The ACS-NSQIP calculator demonstrated the lowest accuracy (0.23, 59%).
Conclusions
- Developed statistical and ML models accurately predict LOS after OCC free flap reconstruction.
- ML and statistical models significantly outperformed the ACS-NSQIP calculator.
- The ML model identified new predictors of LOS, requiring further external validation for clinical use.
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