{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T20:00:37Z","timestamp":1789156837854,"version":"build-2803163510"},"reference-count":37,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"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":[[2025,5]]},"abstract":"<jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>Retrospective cohort study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Prolonged ICU stay is a driver of higher costs and inferior outcomes in Adult Spinal Deformity (ASD) patients. Machine learning (ML) models have recently been seen as a viable method of predicting pre-operative risk but are often \u2018black boxes\u2019 that do not fully explain the decision-making process. This study aims to demonstrate ML can achieve similar or greater predictive power as traditional statistical methods and follows traditional clinical decision-making processes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Five ML models (Decision Tree, Random Forest, Support Vector Classifier, GradBoost, and a CNN) were trained on data collected from a large urban academic center to predict whether prolonged ICU stay would be required post-operatively. 535 patients who underwent posterior fusion or combined fusion for treatment of ASD were included in each model with a 70-20-10 train-test-validation split. Further analysis was performed using Shapley Additive Explanation (SHAP) values to provide insight into each model\u2019s decision-making process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model\u2019s Area Under the Receiver Operating Curve (AUROC) ranged from 0.67 to 0.83. The Random Forest model achieved the highest score. The model considered length of surgery, complications, and estimated blood loss to be the greatest predictors of prolonged ICU stay based on SHAP values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed a ML model that was able to predict whether prolonged ICU stay was required in ASD patients. Further SHAP analysis demonstrated our model aligned with traditional clinical thinking. Thus, ML models have strong potential to assist with risk stratification and more effective and cost-efficient care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/21925682241277771","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:31:30Z","timestamp":1724286690000},"page":"1992-2003","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Explainable Machine Learning Approach to Prediction of Prolonged Intensive Care Unit Stay in Adult Spinal Deformity Patients: Machine Learning Outperforms Logistic Regression"],"prefix":"10.1177","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8823-720X","authenticated-orcid":false,"given":"Bashar","family":"Zaidat","sequence":"first","affiliation":[{"name":"The Mount Sinai Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Kurapatti","sequence":"additional","affiliation":[{"name":"The Mount Sinai Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan S.","family":"Gal","sequence":"additional","affiliation":[{"name":"The Mount Sinai Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7511-2486","authenticated-orcid":false,"given":"Samuel K.","family":"Cho","sequence":"additional","affiliation":[{"name":"The Mount Sinai Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun S.","family":"Kim","sequence":"additional","affiliation":[{"name":"The Mount Sinai Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"National and Surgical Health Care Expenditures. 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