A University of Waterloo study shows that routine hospital blood tests analyzed with machine learning can predict spinal cord injury severity and patient survival early after admission, with accuracy improving as more data becomes available.

These models can accurately predict injury severity and mortality within one to three days of hospital admission, often without relying on unreliable early neurological exams.

This approach offers a more reliable prognosis than traditional assessments and can serve as a non-invasive, cost-effective alternative to advanced diagnostics like MRI.

The study highlights that analyzing multiple biomarkers and their changes over time provides better predictive power than single measurements, offering a comprehensive understanding of injury progression.

Routine blood tests are economical, widely accessible, and easier to obtain than advanced imaging or biomarker tests, making them practical tools for early prognosis in diverse medical settings.

While methods like MRI and fluid omics are valuable, blood tests provide a simple, cost-effective, and accessible alternative for early injury assessment.

This machine learning approach can assist clinicians in making better-informed decisions regarding treatment priorities and resource allocation, especially in emergency and intensive care settings.

By providing objective and reliable insights into injury severity, this method helps improve clinical decision-making and resource management, particularly when neurological assessments are inconclusive.

The research analyzed data from over 2,600 U.S. patients, focusing on blood markers like electrolytes and immune cells collected within the first three weeks post-injury to identify patterns associated with injury severity and mortality.