Machine learning models to predict length of stay and discharge destination in complex head and neck surgery
Khodayar Goshtasbi, Tyler M. Yasaka, Mehdi Zandi‐Toghani, Hamid R. Djalilian, William B. Armstrong, Tjoson Tjoa, Yarah M. Haidar, Mehdi Abouzari
Abstract
BACKGROUND: This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) and length-of-stay (LOS) following complex head and neck surgeries. METHODS: Patients undergoing laryngectomy or composite tissue excision followed by free tissue transfer were extracted from the 2005 to 2017 NSQIP database. RESULTS: Among the 2786 included patients, DNHF and mean LOS were 421 (15.1%) and 11.7 ± 8.8 days. Four classification models for predicting DNHF with high specificities (range, 0.80-0.84) were developed. The generalized linear and gradient boosting machine models performed best with receiver operating characteristic (ROC), accuracy, and negative predictive value (NPV) of 0.72-0.73, 0.75-0.76, and 0.88-0.89. Four regression models for predicting LOS in days were developed, where all performed similarly with mean absolute error and root mean-squared errors of 3.95-3.98 and 5.14-5.16. Both models were developed into an encrypted web-based interface: https://uci-ent.shinyapps.io/head-neck/. CONCLUSION: Novel and proof-of-concept ML models to predict DNHF and LOS were developed and published as web-based interfaces.