Prediction of Primary Admission Total Charges Following Single-Level Lumbar Arthrodesis Utilizing Machine Learning

Study DesignRetrospective analysis utilizing machine learning.ObjectivesThis study aims to identify the key factors influencing total charges during the primary admission period following single-level lumbar arthrodesis, using machine learning models to enhance predictive accuracy.MethodsData were extracted from the National Inpatient Sample (NIS) database and analyzed using various machine learning models, including random forest, gradient boosting trees, and logistic regression. A total of 78,022 unweighted cases of patients who underwent single-level lumbar arthrodesis were identified using the NIS database from 2016 to 2020. Variables included hospital size, region, patient-specific factors, and procedural details. Multivariate linear regression was also used to identify charge-related variables.ResultsThe average total charge for single-level lumbar arthrodesis was $145,600 ± $102,500. Significant predictors of charge included length of stay, hospital size, hospital ownership, and region. Private investor-owned hospitals and procedures performed in the Western U.S. were associated with higher charges. Random forest models demonstrated superior predictive accuracy with an AUC of .866, outperforming other models.ConclusionsHospital characteristics, regional factors, and patient-specific variables significantly influence the charges of single-level lumbar arthrodesis. Machine learning models, particularly random forest, provide robust tools for predicting healthcare costs, enabling better resource allocation and decision-making. Future research should explore these dynamics further to optimize cost management and improve care quality.