Routine Blood Samples as Prognostic Tools for Spinal Cord Injury
A recent study conducted by the University of Waterloo suggests that routine blood samples, commonly taken in hospitals, could play a significant role in predicting the severity of spinal cord injuries and understanding mortality risks associated with such injuries.
Research Overview
The research team employed advanced analytics and machine learning, a form of artificial intelligence, to determine if routine blood tests could be utilized as early indications of patient outcomes in spinal cord injury cases.
Global Impact of Spinal Cord Injury
According to the World Health Organization, more than 20 million people were affected by spinal cord injuries globally in 2019, with approximately 930,000 new cases each year. Traumatic spinal cord injuries often necessitate intensive care and demonstrate a wide range of clinical presentations and recovery trajectories. This variability complicates diagnosis and prognosis, particularly in emergency departments and intensive care units.
Insights from the Research Team
Dr. Abel Torres Espín, a professor at Waterloo’s School of Public Health Sciences, noted, “Routine blood tests could provide doctors with vital and cost-effective information to help predict the risk of death, the presence of an injury, and its severity.”
Data Collection and Analytical Methods
The researchers analyzed data from over 2,600 patients in the U.S., applying machine learning techniques to examine millions of data points and identify hidden patterns in common blood measurements, such as electrolytes and immune cell counts, collected during the first three weeks following a spinal cord injury.
Findings on Predictive Accuracy
Results indicated that these patterns could enhance the ability to forecast recovery and injury severity, even in the absence of early neurological examinations, which can sometimes be unreliable due to patient responsiveness.
Dr. Marzieh Mussavi Rizi, a postdoctoral scholar in Torres Espín’s lab, emphasized, “While a single biomarker measured at a single time point can have predictive power, the broader narrative lies in multiple biomarkers and the changes they exhibit over time.”
Clinical Relevance of Blood Test Models
The developed models, which do not depend on early neurological assessments, demonstrated accuracy in predicting mortality and injury severity as early as one to three days post-admission. This contrasts with standard, non-specific severity measures typically employed during the first day in intensive care.
Accessibility and Practicality of Routine Tests
Further findings revealed that accuracy improved over time as more blood tests became available. While other diagnostic methods, such as MRI and fluid omics-based biomarkers, can provide objective data, they are not always readily accessible in all medical environments. Conversely, routine blood tests are economical, easy to obtain, and universally available across hospitals.
Conclusions and Future Implications
Dr. Torres Espín remarked, “The ability to predict injury severity within the first days is clinically relevant for decision-making; however, it is a complex task when relying solely on neurological assessment. Our study demonstrates the potential to discern whether an injury is motor complete or incomplete using routine blood data shortly after injury, with enhanced prediction performance over time.”
This foundational research could pave the way for improved clinical practices, enabling better-informed decisions regarding treatment priorities and resource allocation in critical care settings for various physical injuries.
The findings from this study, titled “Modeling trajectories of routine blood tests as dynamic biomarkers for outcomes in spinal cord injury,” were published in NPJ Digital Medicine by Nature.
Key Health Takeaway
Routine blood tests have the potential to significantly enhance the prediction of injury severity and mortality risks in spinal cord injury patients, offering a practical and accessible tool for healthcare providers in critical care settings.
