• Anirudh K Gowd, Avinesh Agarwalla, Edward C Beck, Peter B Derman, Siamak Yasmeh, Todd J Albert, and Joseph N Liu.
• Department of Orthopaedic Surgery, Wake Forest University Baptist Medical Center, Winston-Salem, NC.
• Spine. 2022 Nov 15; 47 (22): 154915571549-1557.

Study DesignRetrospective case series.ObjectivePredict cost following anterior cervical discectomy and fusion (ACDF) within the 90-day global period using machine learning models.BackgroundThe incidence of ACDF has been increasing with a disproportionate decrease in reimbursement. As bundled payment models become common, it is imperative to identify factors that impact the cost of care.Materials And MethodsThe Nationwide Readmissions Database (NRD) was accessed in 2018 for all primary ACDFs by the International Classification of Diseases 10th Revision (ICD-10) procedure codes. Costs were calculated by utilizing the total hospital charge and each hospital's cost-to-charge ratio. Hospital characteristics, such as volume of procedures performed and wage index, were also queried. Readmissions within 90 days were identified, and cost of readmissions was added to the total admission cost to represent the 90-day healthcare cost. Machine learning algorithms were used to predict patients with 90-day admission costs >1 SD from the mean.ResultsThere were 42,485 procedures included in this investigation with an average age of 57.7±12.3 years with 50.6% males. The average cost of the operative admission was $24,874±25,610, the average cost of readmission was $25,371±11,476, and the average total cost was $26,977±28,947 including readmissions costs. There were 10,624 patients who were categorized as high cost. Wage index, hospital volume, age, and diagnosis-related group severity were most correlated with the total cost of care. Gradient boosting trees algorithm was most predictive of the total cost of care (area under the curve=0.86).ConclusionsBundled payment models utilize wage index and diagnosis-related groups to determine reimbursement of ACDF. However, machine learning algorithms identified additional variables, such as hospital volume, readmission, and patient age, that are also important for determining the cost of care. Machine learning can improve cost-effectiveness and reduce the financial burden placed upon physicians and hospitals by implementing patient-specific reimbursement.Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.

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