Accounting for age in prediction of discharge destination following elective lumbar fusion: a supervised machine learning approach

Abstract

BACKGROUND CONTEXT: The number of elective spinal fusion procedures performed each year continues to grow, making risk factors for post-operative complications following this procedure increasingly clinically relevant. Nonhome discharge (NHD) is of particular interest due to its associations with increased costs of care and rates of complications. Notably, increased age has been found to influence rates of NHD. PURPOSE: To identify aged-adjusted risk factors for nonhome discharge following elective lumbar fusion through the utilization of Machine Learning-generated predictions within stratified age groupings. STUDY DESIGN: Retrospective Database Study. PATIENT SAMPLE: The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database years 2008 to 2018. OUTCOME MEASURES: Postoperative discharge destination. METHODS: ACS-NSQIP was queried to identify adult patients undergoing elective lumbar spinal fusion from 2008 to 2018. Patients were then stratified into the following age ranges: 30 to 44 years, 45 to 64 years, and ≥65 years. These groups were then analyzed by eight ML algorithms, each tasked with predicting post-operative discharge destination. RESULTS: Prediction of NHD was performed with average AUCs of 0.591, 0.681, and 0.693 for those aged 30 to 44, 45 to 64, and ≥65 years respectively. In patients aged 30 to 44, operative time (p