Importance: Early outcome prediction after acute traumatic spinal cord injury (SCI) is challenging due to pathological complexities and population heterogeneity. Routinely collected data during standard medical practice, such as laboratory analytics, can be a surrogate of underlying pathophysiological processes and used as a biomarker. We hypothesized that distinct temporal trends of blood analytics could be modeled after SCI and that those would predict distinct outcome parameters. Objective: To test the hypothesis and develop machine learning models for predicting SCI outcomes. Design: We developed and validated the models using retrospective data from the MIMIC-III and MIMIC-IV datasets and the prospective TRACK-SCI study, covering the period from 2001 to 2020. Setting: Multi-center, involving data obtained from intensive care units across several different hospital settings in the United States. Participants: Patients 15 years and older with traumatic SCI or vertebral fractures, admitted to emergency facilities, were included, resulting in a final cohort of 2,615 patients for modeling. Exposure(s): NA Main Outcome(s) and Measure(s): Primary outcomes included in-hospital mortality, occurrence of SCI and vertebral fracture in spine trauma patients, and SCI severity measured by the ASIA Impairment Scale. Blood biomarker level trajectory memberships served as predictors. Results: Our study analyzed 2,752 patients, comprising 2,615 from the MIMIC dataset and 137 from the TRACK-SCI study. We identified multiple trajectory classes for 20 common blood markers that serve as dynamic predictors in machine learning classifiers. The in-hospital mortality model achieved an area under the Precision-Recall curve (PR-AUC) of 0.92 in the training set by leveraging trajectory data and baseline covariates from as early as day one post-injury. For SCI severity, the models distinguished between complete and incomplete motor outcomes with a PR-AUC of 0.78. The trajectory-based models showed significant improvement over traditional severity scores, such as Simplified Acute Physiology Score (SAPS) II, especially when combined with demographic information. Conclusions and Relevance: Real-world routinely obtained blood test data can be used to model dynamic changes after SCI with prediction validity for patient outcomes. This work establishes the basis for further development of dynamic biomarker data for outcome prediction in neurotrauma and other neurological conditions.

The authors have declared no competing interest.

This work has been funded by Wings for Life Foundation to A. Torres-Espin, U.S. Department of Defense (W81XWH-13-1-0297 and W81XWH-16-1-0497), and Craig H. Neilsen Foundation (University of California, San Francisco, Spinal Cord Injury Center of Excellence special project award) to M.S. Beattie. D. Fernandez is a Serra-Hunter Fellow, a member of the Centro de Investigacion Biomedica en Red de Salud Mental (Instituto de Salud Carlos III), and his work has been supported by the Ministerio de Ciencia e Innovacion y Universidades (Spain) [PID2023-148033OB-C21], and by grant 2021 SGR 01421 (GRBIO) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Office of research ethics of university of Waterloo gave ethical approval for this work. Data collection and extraction protocols for the TRACK-SCI study were approved by the Institutional Research Board (IRB) at the University of California, San Francisco.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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MIMIC-III and MIMIC-IV are available online under data use agreement through the PhysioNet project. TRACK-SCI are available through a collaboration agreement.