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May 20, 2024NeurospineOpen Access

Using Machine Learning Models to Identify Factors Associated With 30-Day Readmissions After Posterior Cervical Fusions: A Longitudinal Cohort Study

Aneysis D. Gonzalez-Suarez Paymon G. Rezaii Daniel B. Herrick Seth Tigchelaar John K. Ratliff Mirabela Rusu David Scheinker Ikchan Jeon Atman Desai

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Abstract

Readmission rates after posterior cervical fusion (PCF) significantly impact patients and healthcare, with complication rates at 15%-25% 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.

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Gonzalez-Suarez et al. (2024) studied this question.

synapsesocial.com/papers/68e694b6b6db64358761b24e https://doi.org/10.14245/ns.2347340.670

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