Prediction of primary admission total charges following cervical disc arthroplasty utilizing machine learning
Paul G Mastrokostas1, Leonidas E Mastrokostas2, Ahmed K Emara3
1Department of Orthopaedic Surgery, SUNY Downstate Health Sciences University, Brooklyn, NY, USA; Department of Orthopaedic Surgery, Maimonides Medical Center, Brooklyn, NY, USA.
Background Context:
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.
Purpose:
To evaluate how hospital size, geographic region, and patient-specific variables influence charges associated with the primary admission period following CDA.
Study Design:
A retrospective analysis using machine learning models to predict and analyze charge factors associated with CDA.
Patient Sample:
Data from the National Inpatient Sample (NIS) database from 2016 to 2020 was used, focusing on patients undergoing CDA.
Outcome Measures:
The primary outcome was total charge associated with the primary admission for CDA, analyzed against patient demographics, hospital characteristics, and regional economic conditions.
Methods:
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.
Results:
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<.001). The Western region of the U.S. also showed significantly higher charges compared to the Northeast (p<.001). The gradient boosting trees model showed the highest accuracy (AUC=85.6%). Length of stay and wage index were significant charge drivers, with each additional inpatient day increasing charges significantly (p<.001) and higher wage index regions correlating with increased charges (p<.001).
Conclusions:
Hospital size, geographic region, and specific patient demographics significantly influence the charges of CDA. Machine learning models proved effective in predicting these charges, suggesting that they could be instrumental in guiding economic decision-making in spine surgery. Future efforts should aim to incorporate these models into broader clinical practice to optimize healthcare spending and enhance patient care outcomes.
