Prediction of primary admission total charges following cervical disc arthroplasty utilizing machine learning

Cervical disc arthroplasty (CDA) has become an increasingly popular alternative to anterior cervical discectomy and fusion, offering benefits such as motion preservation and reduced risk of adjacent segment disease. Despite its advantages, understanding the economic implications associated with varying patient and hospital factors remains critical. To evaluate how hospital size, geographic region, and patient-specific variables influence charges associated with the primary admission period following CDA. A retrospective analysis using machine learning models to predict and analyze charge factors associated with CDA. Data from the National Inpatient Sample (NIS) database from 2016 to 2020 was used, focusing on patients undergoing CDA. The primary outcome was total charge associated with the primary admission for CDA, analyzed against patient demographics, hospital characteristics, and regional economic conditions. Multivariate linear regression and machine learning algorithms including logistic regression, random forest, and gradient boosting trees were employed to assess their predictive power on charge outcomes. Statistical significance was set at the 0.003 level after applying a Bonferroni correction. The analysis included 3,772 eligible CDA cases. Major predictors of charge identified were hospital size and ownership type, with large and privately owned hospitals associated with higher charges (p