A data-driven approach to categorize patients with traumatic spinal cord injury: cluster analysis of a multicentre database

This study uses a method called cluster analysis to group patients with traumatic spinal cord injuries (tSCI) into subgroups based on their similarities in demographics and injury characteristics at the time of admission. The goal is to identify distinct groups of patients who might benefit from more personalized treatment approaches. The researchers analyzed data from a large registry, looking at factors like age, body mass index, injury severity, and location of the injury to create these subgroups. They then examined how these groups differed in terms of their outcomes at discharge, such as their functional independence and length of stay in the hospital. By identifying these subgroups, the study aims to improve communication between patients and healthcare providers, guide optimal management strategies, and inform the development of targeted therapies for tSCI patients, ultimately leading to better patient-centered care.

• 1
  The study identified five distinct subgroups of tSCI patients based on baseline variables such as age, BMI, injury severity (AIS grade), primary location of injury (PLI), and baseline FIM motor score.
• 2
  Significant inter-group differences were found in clinical outcomes, including FIM motor score at discharge and total length of stay, supporting the idea that patients within each subgroup had clinical similarities.
• 3
  The study found that primary location of injury (PLI) was among the primary factors that distinguished patient subgroups, classifying patients into cervical, mixed cervical/thoracic, thoracic, lower thoracic, and lumbar spine trauma groups.