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.
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.
Authors: Laura J White; Hongzheng Zhang; Kaitlyn F Strickland; Mark W El-Deiry; Mihir R Patel; J Tradnor Wadsworth; Amy Y Chen Journal: JAMA Otolaryngol Head Neck Surg Date: 2015-12 Impact factor: 6.223
Authors: Paul T Ogink; Aditya V Karhade; Quirina C B S Thio; William B Gormley; Fetullah C Oner; Jorrit J Verlaan; Joseph H Schwab Journal: Eur Spine J Date: 2019-04-02 Impact factor: 3.134
Authors: Benjamin S Hopkins; Jonathan T Yamaguchi; Roxanna Garcia; Kartik Kesavabhotla; Hannah Weiss; Wellington K Hsu; Zachary A Smith; Nader S Dahdaleh Journal: J Neurosurg Spine Date: 2019-11-29