Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

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.

Factors Impacting Discharge Destination Following Head and Neck Microvascular Reconstruction

“…19 Computerized discharge destination algorithms that include patient functional status and baseline health have been developed to predict patients' discharge destination. 29,30 A previous study demonstrated a machine learning algorithm could be developed to anticipate the discharge destination in head and neck surgery patients. 29 While computerized algorithms may serve as a tool to predict discharge destination, standardized assessments of patients' frailty may more accurately reflect the functional status and subsequent discharge placement.…”

“…29,30 A previous study demonstrated a machine learning algorithm could be developed to anticipate the discharge destination in head and neck surgery patients. 29 While computerized algorithms may serve as a tool to predict discharge destination, standardized assessments of patients' frailty may more accurately reflect the functional status and subsequent discharge placement. 29 A prior study reviewing patients undergoing total laryngectomy found patients with a higher Modified Frailty Index had an increased chance of requiring discharge to an acute care facility.…”

“…29 While computerized algorithms may serve as a tool to predict discharge destination, standardized assessments of patients' frailty may more accurately reflect the functional status and subsequent discharge placement. 29 A prior study reviewing patients undergoing total laryngectomy found patients with a higher Modified Frailty Index had an increased chance of requiring discharge to an acute care facility. 31 However, another study found that while Modified Frailty Index did not correlate with discharge destination in head and neck patients undergoing free flap reconstruction, Charleston Comorbidity Index showed a slight correlation.…”

“…Computerized discharge destination algorithms that include patient functional status and baseline health have been developed to predict patients' discharge destination 29,30 . A previous study demonstrated a machine learning algorithm could be developed to anticipate the discharge destination in head and neck surgery patients 29 .…”

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“…Using ML based prediction models (intelligence system) is proven to be useful for optimum LOS estimation. This led to reducing uncertainty and ambiguity by offering systematic and evidence based system for hospital resource utilization and care planning [11,43]. For this purpose, several ML methods, including ANN, RBF, SVM, FNN, PNN, Pattern recognition network, and DT were fed by using the optimized predictor variables.…”