{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T01:03:48Z","timestamp":1789434228452,"version":"build-2803163510"},"reference-count":18,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neurocrit Care"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The APACHE-IV is the gold standard for ICU mortality prediction across a multitude of patient conditions. We aim to create a machine learning model to predict ICU mortality for patients with spine fracture with similar predictive capabilities as the APACHE-IV, but with fewer inputs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design<\/jats:title>\n                    <jats:p>\n                      The MIMIC-IV and the eICU databases were queried for patients admitted to the ICU with a spine fracture. Demographics and physiological variables of each patient\u2019s first day of ICU admission were collected. Variables with &gt; 10% missing values were excluded, while others were imputed using K-nearest-neighbors (\n                      <jats:italic>k<\/jats:italic>\n                      = 5).\n                    <\/jats:p>\n                    <jats:p>\n                      Feature selection using MIMIC-IV was performed by training an initial Extreme Gradient Boosting (XGBoost) model. Eight features were selected using gain: systolic blood pressure, fiO\n                      <jats:sub>2<\/jats:sub>\n                      , age, spinal cord injury status, calcium, heart rate, oxygen saturation, and white blood cell count. Hyperparameter tuning was implemented using a 5-fold cross-validated grid search to optimize model performance. The model was externally tested and compared with APACHE-IV using the eICU database.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 926 patients with spine fracture in MIMIC-IV were included, with a mortality rate of 6.6%. A total of 571 patients with spinal fracture in the eICU database were included, with a mortality rate of 6.7%. Our trained XGBoost model achieved an external test area under the receiver operating curve (AUROC) of 0.84 (95% CI: 0.77\u20130.90), area under the precision\u2013recall curve (AUPRC) of 0.22 (95% CI: 0.10\u20130.36), and Brier score of 0.041, compared with the APACHE-IV model of 0.85 (95% CI: 0.78\u20130.91), 0.23 (95% CI: 0.11\u20130.38), and 0.044, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed an eight-variable XGBoost model to predict mortality for patients with spine fracture that performed similarly to the 37-variable APACHE-IV score when applied to a study population from the eICU dataset. Although the APACHE-IV is the most robust method for ICU mortality scoring, novel methods utilizing machine learning guided feature selection should be explored given the potential to simplify and improve on the proprietary APACHE-IV system.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12028-026-02648-3","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:26:52Z","timestamp":1789432012000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction Model for Intensive Care Unit Mortality in Patients with Spine Fracture: Can Machine Learning be Used to Improve Current Standards?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4568-6237","authenticated-orcid":false,"given":"Maor","family":"Shir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariel","family":"Sacknovitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Blakley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bar","family":"Shir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moshe","family":"Serwatien","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harshadkumar A.","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Merritt D.","family":"Kinon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John V.","family":"Wainwright","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"issue":"9","key":"2648_CR1","doi-asserted-by":"publisher","first-page":"e580","DOI":"10.1016\/S2666-7568(21)00172-0","volume":"2","author":"AM Wu","year":"2021","unstructured":"Wu AM, Bisignano C, James SL, et al. Global, regional, and national burden of bone fractures in 204 countries and territories, 1990\u20132019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet Healthy Longev. 2021;2(9):e580\u201392. https:\/\/doi.org\/10.1016\/S2666-7568(21)00172-0.","journal-title":"Lancet Healthy Longev."},{"issue":"1","key":"2648_CR2","doi-asserted-by":"publisher","first-page":"11696","DOI":"10.1038\/s41598-025-94871-9","volume":"15","author":"HG Niu","year":"2025","unstructured":"Niu HG, Hu Y, Gong YK, Hu GK, Ye GQ, Gao WS. Trends in prevalence of spine fractures and risk factors in spine fractures among US adults, 1999\u20132018. Sci Rep. 2025;15(1):11696. https:\/\/doi.org\/10.1038\/s41598-025-94871-9.","journal-title":"Sci Rep"},{"issue":"5","key":"2648_CR3","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1111\/jgs.14669","volume":"65","author":"RS Jawa","year":"2017","unstructured":"Jawa RS, Singer AJ, Rutigliano DN, et al. Spinal fractures in older adult patients admitted after low-level falls: 10-year incidence and outcomes. J Am Geriatr Soc. 2017;65(5):909\u201315. https:\/\/doi.org\/10.1111\/jgs.14669.","journal-title":"J Am Geriatr Soc"},{"issue":"10","key":"2648_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0164828","volume":"11","author":"J Allyn","year":"2016","unstructured":"Allyn J, Ferdynus C, Bohrer M, Dalban C, Valance D, Allou N. Simplified acute physiology score II as predictor of mortality in intensive care units: a decision curve analysis. PLoS ONE. 2016;11(10):e0164828. https:\/\/doi.org\/10.1371\/journal.pone.0164828.","journal-title":"PLoS ONE"},{"issue":"1","key":"2648_CR5","doi-asserted-by":"publisher","first-page":"9076739","DOI":"10.1155\/2020\/9076739","volume":"2020","author":"YT Hsu","year":"2020","unstructured":"Hsu YT, He YT, Ting CK, Tsou MY, Tang GJ, Pu C. 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Kinon MD: Globus Medical Inc. consultant, Sanara MedTech consultant, Dilon Technologies consultant; John V. Wainwright MD: Globus Medical Inc. consultant; All other authors have no conflicts of interest","order":1,"name":"Ethics","label":"Conflicts of Interest","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The electronic Intensive Care Unit (eICU) database is exempt from institutional review board approval due to the retrospective design, lack of direct patient intervention, and the security schema, for which the reidentification risk was certified as meeting safe harbor standards by an independent privacy expert (Privacert, Cambridge, MA) (Health Insurance Portability and Accountability Act Certification no. 1031219-2). The Medical Information Mart for Intensive Care (MIMIC-IV) database was reviewed by the Institutional Review Board at the Beth Israel Deaconess Medical Center and received a waiver of informed consent and approved the data sharing initiative. For this type of study, formal consent is not required.","order":2,"name":"Ethics","label":"Ethical Approval \/ Informed Consent","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Artificial intelligence (AI) was not used in the preparation of this manuscript","order":3,"name":"Ethics","label":"Artificial Intelligence","group":{"name":"EthicsHeading","label":"Ethics"}}]}}