World neurosurgery | 2021
Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.
Abstract
OBJECTIVE\nReadmission after spine surgery is a costly, but relatively common occurrence. Previous research has identified several risk factors for readmission however, the conclusions remain equivocal. Machine learning algorithms offer a unique perspective in the analysis of risk factors for readmission and can help predict the likelihood of this occurrence. In this investigation, a neural network, a supervised machine learning technique, is evaluated to determine whether it can predict readmission after three lumbar fusion procedures.\n\n\nMETHODS\nThe American College of Surgeon s database, the National Surgical Quality Improvement Program (NSQIP), was queried between 2009 and 2018. Patients who had undergone anterior, lateral, and/or posterior lumbar fusion were included in the study. The Python Sci-Kit Learn package was utilized to run the neural network algorithms. A multivariate regression was performed to determine risk factors for readmission.\n\n\nRESULTS\nIn total, 63,533 patients were analyzed (12,915 ALIF, 27,212 PLIF, and 23,406 PSF). The neural network algorithm was able to successful predict 30-day readmission for 94.6% of ALIF, 94.0% of PLIF, and 92.6% of PSF cases with AUC values of between 0.64-0.65. The multivariate regression indicated that age > 65 years and ASA > 2 were linked to increased risk for readmission for all three procedures.\n\n\nCONCLUSION\nThe accurate metrics presented here indicate the capability for neural network 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.