Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach

Abstract

Readmission after spine surgery is costly and a relatively common occurrence. Previous research identified several risk factors for readmission; however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspective in analysis of risk factors for readmission and can help predict the likelihood of this occurrence. This study evaluated a neural network (NN), a supervised machine learning technique, to determine whether it could predict readmission after 3 lumbar fusion procedures. The American College of Surgeons National Surgical Quality Improvement Program database was queried between 2009 and 2018. Patients who had undergone anterior, lateral, and/or posterior lumbar fusion were included in the study. The Python scikit Learn package was used to run the NN algorithm. A multivariate regression was performed to determine risk factors for readmission. There were 63,533 patients analyzed (12,915 anterior lumbar interbody fusion, 27,212 posterior lumbar interbody fusion, and 23,406 posterior spinal fusion cases). The NN algorithm was able to successfully predict 30-day readmission for 94.6% of anterior lumbar interbody fusion, 94.0% of posterior lumbar interbody fusion, and 92.6% of posterior spinal fusion cases with area under the curve values of 0.64-0.65. Multivariate regression indicated that age >65 years and American Society of Anesthesiologists class >II were linked to increased risk for readmission for all 3 procedures. The accurate metrics presented indicate the capability for NN algorithms to predict readmission after lumbar arthrodesis. Moreover, the results of this study serve as a catalyst for further research into the utility of machine learning in spine surgery.