{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:04:04Z","timestamp":1777658644443,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Global Spine Journal"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective analysis utilizing machine learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The average total charge for single-level lumbar arthrodesis was $145,600 \u00b1 $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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Hospital 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21925682251336714","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T07:00:23Z","timestamp":1744873223000},"page":"55-63","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Primary Admission Total Charges Following Single-Level Lumbar Arthrodesis Utilizing Machine Learning"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1196-517X","authenticated-orcid":false,"given":"Paul G.","family":"Mastrokostas","sequence":"first","affiliation":[{"name":"SUNY Downstate Health Sciences University"},{"name":"Maimonides Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8926-3502","authenticated-orcid":false,"given":"Leonidas E.","family":"Mastrokostas","sequence":"additional","affiliation":[{"name":"SUNY Downstate Health Sciences University"},{"name":"Maimonides Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1582-0809","authenticated-orcid":false,"given":"Abigail","family":"Razi","sequence":"additional","affiliation":[{"name":"Maimonides Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John K.","family":"Houten","sequence":"additional","affiliation":[{"name":"Mount Sinai School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jad","family":"Bou Monsef","sequence":"additional","affiliation":[{"name":"SUNY Downstate Health Sciences University"},{"name":"Maimonides Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Afshin E.","family":"Razi","sequence":"additional","affiliation":[{"name":"Maimonides Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5831-055X","authenticated-orcid":false,"given":"Mitchell K.","family":"Ng","sequence":"additional","affiliation":[{"name":"Maimonides Medical Center"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0000000000002822"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.21037\/JSS-20-630"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/S10195-004-0035-8\/METRICS"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.21037\/JSS.2019.04.04"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocn.2015.05.005"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1097\/BSD.0000000000001460"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.14444\/8503"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/S13037-024-00393-0\/METRICS"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJIN.2022.05.002"},{"key":"e_1_3_3_11_2","unstructured":"HCUP-US NIS overview. https:\/\/hcup-us.ahrq.gov\/nisoverview.jsp. 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