Efforts to understand spinal cord injury (SCI) and other complex neurotrauma disorders at the pre-clinical level have shown progress in recent years. However, successful translation of basic research into clinical practice has been slow, partly because of the large, heterogeneous data sets involved. In this sense, translational neurological research represents a "big data" problem. In an effort to expedite translation of pre-clinical knowledge into standards of patient care for SCI, we describe the development of a novel database for translational neurotrauma research known as Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI). We present demographics, descriptive statistics, and translational syndromic outcomes derived from our ongoing efforts to build a multi-center, multi-species pre-clinical database for SCI models. We leveraged archived surgical records, postoperative care logs, behavioral outcome measures, and histopathology from approximately 3000 mice, rats, and monkeys from pre-clinical SCI studies published between 1993 and 2013. The majority of animals in the database have measures collected for health monitoring, such as weight loss/gain, heart rate, blood pressure, postoperative monitoring of bladder function and drug/fluid administration, behavioral outcome measures of locomotion, and tissue sparing postmortem. Attempts to align these variables with currently accepted common data elements highlighted the need for more translational outcomes to be identified as clinical endpoints for therapeutic testing. Last, we use syndromic analysis to identify conserved biological mechanisms of recovery after cervical SCI between rats and monkeys that will allow for more-efficient testing of therapeutics that will need to be translated toward future clinical trials.
Keywords: bioinformatics; monkeys; rodents; syndromics; translation.