Longitudinal Trends and Prevalence of Bowel Management in Individuals With Spinal Cord Injury

Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review

Background

Machine learning has been applied to improve diagnosis and prognostication of acute traumatic spinal cord injury. We investigate potential for clinical integration of machine learning in this patient population to navigate variability in injury and recovery.

Materials and methods

We performed a systematic review using PRISMA guidelines through PubMed database to identify studies that use machine learning algorithms for clinical application toward improvements in diagnosis, management, and predictive modeling.

Results

Of the 132 records identified, a total of 13 articles met inclusion criteria and were included in final analysis. Of the 13 articles, 5 focused on diagnostic accuracy and 8 were related to prognostication or management of traumatic spinal cord injury. Across studies, 1983 patients with spinal cord injury were evaluated with most classifying as ASIA C or D. Retrospective designs were used in 10 of 13 studies and 3 were prospective. Studies focused on MRI evaluation and segmentation for diagnostic accuracy and prognostication, investigation of mean arterial pressure in acute care and intraoperative settings, prediction of ambulatory and functional ability, chronic complication prevention, and psychological quality of life assessments. Decision tree, random forests (RF), support vector machines (SVM), hierarchical cluster tree analysis (HCTA), artificial neural networks (ANN), convolutional neural networks (CNN) machine learning subtypes were used.

Conclusions

Machine learning represents a platform technology with clinical application in traumatic spinal cord injury diagnosis, prognostication, management, rehabilitation, and risk prevention of chronic complications and mental illness. SVM models showed improved accuracy when compared to other ML subtypes surveyed. Inherent variability across patients with SCI offers unique opportunity for ML and personalized medicine to drive desired outcomes and assess risks in this patient population.

Neurogenic Bowel and Management after Spinal Cord Injury: A Narrative Review

People with spinal cord injury (SCI) suffer from the sequela of neurogenic bowel and its disabling complications primarily constipation, fecal incontinence, and gastrointestinal (GI) symptoms. Neurogenic bowel is a functional bowel disorder with a spectrum of defecatory disorders as well as colonic and gastrointestinal motility dysfunction. This manuscript will review the anatomy and physiology of gastrointestinal innervation, as well as the pathophysiology associated with SCI. It will provide essential information on the recent guidelines for neurogenic bowel assessment and medical management. This will allow medical providers to partner with their patients to develop an individualized bowel plan utilizing a combination of various pharmacological, mechanical and surgical interventions that prevent complications and ensure successful management and compliance. For people with SCI and neurogenic bowel dysfunction, the fundamental goal is to maintain health and well-being, promote a good quality of life and support active, fulfilled lives in their homes and communities.