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Using MIMIC data (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20092615) for modeling and TRACK-SCI study data (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009137) for validation, we identified multiple trajectories for common blood markers. We developed machine learning models for the dynamic prediction of in-hospital mortality, SCI occurrence in spine trauma patients, and SCI severity (motor complete vs. incomplete). The in-hospital mortality model achieved an out-of-train ROC-AUC of 0.79 [0.77\u20130.81] day one post-injury, improving to 0.89 [0.88\u20130.89] by day 21. For detecting the presence of SCI after spine trauma, the highest ROC-AUC was 0.71 [0.69\u20130.72] achieved by day 21. By day seven, the ROC-AUC for SCI severity was 0.81 [0.77\u20130.85]. Our full models outperformed the severity score SAPS II following seven days of hospitalization.\n                  <\/jats:p>","DOI":"10.1038\/s41746-025-01782-0","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T05:17:09Z","timestamp":1753161429000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury"],"prefix":"10.1038","volume":"8","author":[{"given":"Marzieh","family":"Mussavi Rizi","sequence":"first","affiliation":[]},{"given":"Daniel","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[]},{"given":"John L. 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