Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury.

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients. Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods. Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery. Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention. Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Spinal cord injury is a devastating condition that involves damage to the nerve fibers connecting the brain with the spinal cord, often leading to permanent changes in strength, sensation and body functions, and in severe cases paralysis. Scientists around the world work hard to find ways to treat or even repair spinal cord injuries but few patients with complete immediate paralysis recover fully. Immediate paralysis is caused by direct damage to neurons and their extension in the spinal cord. Previous research has shown that blood pressure regulation may be key in saving these damaged neurons, as spinal cord injuries can break the communication between nerves that is involved in controlling blood pressure. This can lead to a vicious cycle of dysregulation of blood pressure and limit the supply of blood and oxygen to the damaged spinal cord tissue, exacerbating the death of spinal neurons. Management of blood pressure is therefore a key target for spinal cord injury care, but so far, the precise thresholds to enable neurons to recover are poorly understood. To find out more, Torres-Espin, Haefeli et al. used machine learning software to analyze previously recorded blood pressure and heart rate data obtained from 118 patients that underwent spinal cord surgery after acute spinal cord injury. The analyses revealed that patients who suffered from either low or high blood pressure during surgery had poorer prospects of recovery. Statistical models confirming these findings showed that the optimal blood pressure range to ensure recovery lies between 76 to 104-117 mmHg. Any deviation from this narrow window would dramatically worsen the ability to recover. These findings suggests that dysregulated blood pressure during surgery affects to odds of recovery in patients with a spinal cord injury. Torres-Espin, Haefeli et al. provide specific information that could improve current clinical practice in trauma centers. In the future, such machine learning tools and models could help develop real-time models that could predict the likelihood of a patient's recovery following spinal cord injury and related neurological conditions.

Leveraging biomedical informatics for assessing plasticity and repair in primate spinal cord injury.

Recent preclinical advances highlight the therapeutic potential of treatments aimed at boosting regeneration and plasticity of spinal circuitry damaged by spinal cord injury (SCI). With several promising candidates being considered for translation into clinical trials, the SCI community has called for a non-human primate model as a crucial validation step to test efficacy and validity of these therapies prior to human testing. The present paper reviews the previous and ongoing efforts of the California Spinal Cord Consortium (CSCC), a multidisciplinary team of experts from 5 University of California medical and research centers, to develop this crucial translational SCI model. We focus on the growing volumes of high resolution data collected by the CSCC, and our efforts to develop a biomedical informatics framework aimed at leveraging multidimensional data to monitor plasticity and repair targeting recovery of hand and arm function. Although the main focus of many researchers is the restoration of voluntary motor control, we also describe our ongoing efforts to add assessments of sensory function, including pain, vital signs during surgery, and recovery of bladder and bowel function. By pooling our multidimensional data resources and building a unified database infrastructure for this clinically relevant translational model of SCI, we are now in a unique position to test promising therapeutic strategies' efficacy on the entire syndrome of SCI. We review analyses highlighting the intersection between motor, sensory, autonomic and pathological contributions to the overall restoration of function. This article is part of a Special Issue entitled SI: Spinal cord injury.