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. 2025 Apr 17:21925682251336714.
doi: 10.1177/21925682251336714. Online ahead of print.

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

Paul G Mastrokostas  1   2 , Leonidas E Mastrokostas  1   2 , Abigail Razi  2 , John K Houten  3 , Jad Bou Monsef  1   2 , Afshin E Razi  2 , Mitchell K Ng  2
Affiliations

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

Paul G Mastrokostas et al. Global Spine J. .

Abstract

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.

Keywords: cost-effectiveness; lumbar arthrodesis; lumbar fusion; machine learning; national inpatient sample; value-based care.

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Conflict of interest statement

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Mitchell K. Ng is a paid consultant at Pacira BioSciences Inc., Sage Products Inc., Alafair Biosciences Inc., Next Science LLC, Bonutti Technologies Inc., Johnson & Johnson Ethicon Inc., Hippocrates Opportunities Fund LLC, and Ferghana Partners Inc.

Figures

Figure 1.
Figure 1.
Feature importance as determined through a random forest classifier via permutation method for prediction of high charge of care following single-level lumbar arthrodesis. APR-DRG indicates all patient refined diagnosis-related group.
Figure 2.
Figure 2.
Receiver operating characteristic curve with area under the curve (AUC) calculation of each machine learning algorithm in the prediction of high charges following single-level lumbar arthrodesis.
See this image and copyright information in PMC

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