Objective:

Readmission 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.

Methods:

The 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.

Results:

In 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.

Conclusion:

The 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.