Machine Learning Unlocks Blood Test Secrets for Spinal Cord Injury

According to a study from the University of Waterloo, daily blood samples that are ingested daily at any hospital and are tracked over time may help predict the severity of the injury and even provide insight into mortality after spinal cord injury.

Using advanced analytics and machine learning, a type of artificial intelligence, the research team evaluated whether routine blood tests serve as early warning signs of outcomes in patients with spinal cord injury.

According to the World Health Organization, in 2019, more than 20 million people were affected by spinal cord injuries, with 930,000 new cases each year. Traumatic spinal cord injuries often require intensive care, and a variety of clinical symptoms and recovery trajectories are characterized, particularly in the emergency department and intensive care unit, complicating diagnosis and prognosis.

Regular blood tests may provide your doctor with important and affordable information that will help you predict the risk of death, the presence of injuries, and how serious it is. ”

Dr. Abel Torres Espin, Professor, Department of Public Health, University of Waterloo

The researchers sampled hospital data from more than 2,600 patients in the United States. They used machine learning to analyze millions of data points and discovered hidden patterns of common blood measurements, such as electrolytes and immune cells ingested in the first three weeks after spinal cord injury.

They found that these patterns can help predict recovery and injury severity without early neurological examinations, which are not always reliable, as they depend on patient reactivity.

“While a single biomarker measured at a single time point can have predictive power, the broader story lies in the changes that are shown over time with multiple biomarkers,” said Dr. Margie Mussabi Rij, a postdoctoral scholar at Torres Espin’s lab in Waterloo.

Models that did not rely on early neurological assessments were accurate in predicting mortality and injury severity 1-3 days after hospital admission, compared to standard non-specific severity measures often performed on the first day of arrival in the intensive care unit.

The survey also found that accuracy increases over time as blood tests increase. Other measurements such as MRI and Fluid Omics-based biomarkers can also provide objective data, but they are not always readily accessible across healthcare settings. On the other hand, daily blood tests are economical, easy to obtain and are available in all hospitals.

“Predicting the severity of injuries over the first few days is clinically relevant to decision-making, but neurological assessment alone is a challenging task,” Torres Espin said. “It shows the possibility of predicting whether the injury is completely motion-type or incomplete, indicating incomplete regular blood data early after injury or an increase in predictive performance over time.

“This basic study can open up new possibilities in clinical practice and allows for more detailed decisions regarding treatment priorities and resource allocation in critical care settings for many physical injuries.”