| Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study |
Aneysis D. Gonzalez-Suarez1 
, Paymon G. Rezaii1, Daniel Herrick1, Seth Stravers Tigchelaar1, John K Ratliff1, Mirabela Rusu2, David Scheinker1, Ikchan Jeon1, Atman M Desai1 |
1Department of Neurosurgery, Stanford University, Stanford, CA, USA
2Department of Radiology, Stanford University, Stanford, CA, USA |
Corresponding Author:
Aneysis D. Gonzalez-Suarez
Email: adg52@stanford.edu
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Received: December 20, 2023 Revised: March 22, 2024 Accepted: April 2, 2024 |
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| Abstract |
Objective
Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%–5% and up to 12% 90-day readmission rates. In this study, we aim to test whether machine learning (ML) models that capture interfactorial interactions outperform traditional logistic regression (LR) in identifying readmission-associated factors.
Methods
The Optum Clinformatics Data Mart database was used to identify patients who underwent PCF between 2004–2017. To determine factors associated with 30-day readmissions, 5 ML models were generated and evaluated, including a multivariate LR (MLR) model. Then, the best-performing model, Gradient Boosting Machine (GBM), was compared to the LACE (Length patient stay in the hospital, Acuity of admission of patient in the hospital, Comorbidity, and Emergency visit) index regarding potential cost savings from algorithm implementation.
Results
This study included 4,130 patients, 874 of which were readmitted within 30 days. When analyzed and scaled, we found that patient discharge status, comorbidities, and number of procedure codes were factors that influenced MLR, while patient discharge status, billed admission charge, and length of stay influenced the GBM model. The GBM model significantly outperformed MLR in predicting unplanned readmissions (mean area under the receiver operating characteristic curve, 0.846 vs. 0.829; p<0.001), while also projecting an average cost savings of 50% more than the LACE index.
Conclusion
Five models (GBM, XGBoost [extreme gradient boosting], RF [random forest], LASSO [least absolute shrinkage and selection operator], and MLR) were evaluated, among which, the GBM model exhibited superior predictive performance, robustness, and accuracy. Factors associated with readmissions impact LR and GBM models differently, suggesting that these models can be used complementarily. When analyzing PCF procedures, the GBM model resulted in greater predictive performance and was associated with higher theoretical cost savings for readmissions associated with PCF complications.
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| Keywords:
Machine learning, Predictive modeling, Readmission, Spine, Costs, Posterior cervical fusion |
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