BackgroundThe epidemiology and clinical characteristics of spinal epidural lipomatosis (SEL) have been well-reported in the literature. However, few studies investigated the concomitant spinal pathologies that were present in patients with SEL. Therefore, we aimed to summarize the clinical and radiological characteristics of patients with SEL diagnosed on spinal imaging.MethodsPatients who were diagnosed with SEL on magnetic resonance imaging from January 2018 to October 2020 at our institution were included in the study. Clinical data was collected using a standardized data collection form. SEL was graded using a modified version of the Borré grading system. Factors associated with moderate or severe SEL were determined using multiple logistic regression.ResultsA total of 90 patients were included in the analysis. The mean (±SD) age was 59.3 (±17.1) years, and 62 patients (68.9%) were male. 61 patients (67.8%) had moderate or severe SEL. Most patients were overweight or obese (57 patients, 63.3%). The most common presenting symptoms was back pain (57 patients, 63.3%). SEL was diagnosed incidentally in 42 patients (46.7%). The lumbar spine was the most common site of SEL (35 patients, 38.9%). The most common concomitant spinal pathologies were disc bulge (83 patients, 92.2%) and flavum hypertrophy (60 patients, 66.7%). Moderate or severe SEL was associated with WHO Obesity Class, back pain or radicular leg pain at first presentation, and SEL that was worst at the lumbar or lumbosacral spinal level.ConclusionsModerate or severe SEL were independently associated with WHO Obesity Class, back pain, radicular leg pain, and SEL that was worst at the lumbar or lumbosacral spinal level. Future studies should prospectively evaluate whether weight loss therapy is warranted in patients with SEL. 相似文献
BackgroundMachine 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 methodsWe 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.ResultsOf 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.ConclusionsMachine 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. 相似文献
Objective: The purpose was to describe the prevalence and characteristics of healthcare utilization among individuals with spinal cord injury (SCI) from a Level I trauma center.
Design: Retrospective data analysis utilizing a local acute trauma registry for initial hospitalization and merged with the Dallas-Fort Worth Hospital Council registry to obtain subsequent health care utilization in the first post-injury year.
Setting: Dallas, TX, USA.
Participants: Six hundred and sixty four patients were admitted with an acute traumatic SCI from January 2003 through June 2014 to a Level I trauma center. Fifty five patients that expired during initial hospitalization and 18 patients with unspecified SCI (defined by ICD-9 with no etiology or level of injury specified) were not included in the analysis, leaving a final sample of 591.
Outcome Measures: Data included demographic and clinical characteristics, charges, and healthcare utilization.
Results: Mean age was 46.1?years (±18.9?years), the majority of patients were male (74%), and Caucasian (58%). Of the 591 patients, 345 (58%) had additional inpatient or emergency healthcare utilization accounting for 769 additional visits (median of 3 visits per person). Of the 769 encounters, 534 (69%) were inpatient and 235 (31%) were emergency visits not resulting in an admission. The most prevalent ICD-9 codes listed were pressure ulcer, neurogenic bowel, neurogenic bladder, urinary tract infection, fluid electrolyte imbalance, hypertension, and tobacco use.
Conclusion: Individuals with SCI experience high levels of healthcare utilization which are costly and may be preventable. Increasing our understanding of the prevalence and causes for healthcare utilization after acute SCI is important to target preventive strategies. 相似文献
The aim of the study was to evaluate the loss of truncal rotation over 54 hours after removing Chêneau brace.
Methods
The studied groups consisted of 39 girls aged 10–18 years old, diagnosed with adolescent idiopathic scoliosis (AIS) and treated with Chêneau brace (CAST) and 20 AIS girls aged 10–18 years old, not treated with bracing. Posterior-anterior radiographs were obtained from the clinical assessment of all subjects and were subsequently used to determine Cobb angles. The measurements of the angle of trunk rotation (ATR) were taken with the Scoliometer® and back-contour device during Adams forward bending test by the two evaluators. The changes in ATRs during 54 hours of observation were performed after the brace had been taken off (0, 2, 24, 30, 48 and 54 hours after debracing). This was described using VATR variable, defined as the change in the absolute Scoliometer® readings in the time intervals against the time interval Δt between the measurements. During back-contour assessment the differential factor (kra) has been used for the digital analysis. The changes in kra over 54 hours of observation were expressed as Vkra factor, defined as the difference in the absolute value of the amplitude differential factor (kra) in the time intervals against the time interval Δt between the measurements.
Results
The highest changes were observed in the thoracic as well as in lumbar spine in patients with Cobb angle ≥30°, axial rotation of the apical vertebrae within 5–15°, Risser sign 0–2. The biggest change in the trunk rotation after Chêneau brace had been taken off was noted within the first two hours of observation.
Conclusion
The patients should be advised to take the brace off for a minimum of two hours before the scheduled x-ray, to allow full relaxation of the trunk in order to obtain reliable radiological images of the deformation.