Lateral Corticospinal Tract and Dorsal Column Damage: Predictive Relationships With Motor and Sensory Scores at Discharge From Acute Rehabilitation After Spinal Cord Injury

ObjectiveTo determine if lateral corticospinal tract (LCST) integrity demonstrates a significant predictive relationship with future ipsilateral lower extremity motor function (LEMS) and if dorsal column (DC) integrity demonstrates a significant predictive relationship with future light touch (LT) sensory function post spinal cord injury (SCI) at time of discharge from inpatient rehabilitation.DesignRetrospective analyses of imaging and clinical outcomes.SettingUniversity and academic hospital.ParticipantsA total of 151 participants (N=151) with SCI.InterventionsInpatient rehabilitation.Main Outcome MeasuresLEMS and LT scores at discharge from inpatient rehabilitation.ResultsIn 151 participants, right LCST spared tissue demonstrated a significant predictive relationship with right LEMS percentage recovered (β=0.56; 95% confidence interval [CI], 0.37-0.73; R=0.43; P<.001). Left LCST spared tissue demonstrated a significant predictive relationship with left LEMS percentage recovered (β=0.66; 95% CI, 0.50-0.82; R=0.51; P<.001). DC spared tissue demonstrated a significant predictive relationship with LT percentage recovered (β=0.69; 95% CI, 0.52-0.87; R=0.55; P<.001). When subgrouping the participants into motor complete vs incomplete SCI, motor relationships were no longer significant, but the sensory relationship remained significant. Those who had no voluntary motor function but recovered some also had significantly greater LCST spared tissue than those who did not recover motor function.ConclusionsLCST demonstrated significant moderate predictive relationships with lower extremity motor function at the time of discharge from inpatient rehabilitation, in an ipsilesional manner. DC integrity demonstrated a significant moderate predictive relationship with recovered function of LT. With further development, these neuroimaging methods might be used to predict potential deficits after SCI and to provide corresponding targeted interventions.     

短时程脊髓电刺激治疗爆发痛合并触诱发痛的急性期带状疱疹的临床研究

目的探讨短时程脊髓电刺激(temporary spinal cord stimulation, tSCS)治疗爆发痛合并触诱发痛的急性期带状疱疹的临床疗效。方法回顾性地分析同济大学附属第十人民医院疼痛科2020年1月—2020年12月收治的52例接受tSCS治疗的爆发痛合并触诱发痛的急性期带状疱疹患者的临床资料，评估在治疗前、治疗后3d、7d、14d、3个月、6个月的总体疼痛情况(numerical rating scale, NRS)评分、(simple McGill scores, McGill)评分、爆发痛情况(发生率、NRS评分、次数以及持续时间)、触诱发痛情况(发生率、分级)、术后不良反应等；评估在治疗前、治疗后7d、3个月、6个月的睡眠时长、睡眠中醒来次数、疼痛障碍指数(pain disorder index, PDI)、功能状态评分(Karnofsky score, KPS)、抑郁症筛查量表(patient health questionnaire depression module scale, PHQ-9)和焦虑症筛查量表(generalized anxiety disorder-7 scale, GAD-7)等。结果与治疗前相比，治疗后3d、7d、14d、3个月、6个月的总体疼痛NRS评分、总体疼痛MCGILL评分、静息痛NRS评分明显降低(均P<0.001)；与治疗前相比，治疗后3d、7d、14d、3个月、6个月的的爆发痛NRS评分明显降低(均P<0.05)，治疗后14d、3个月、6个月时的爆发痛次数以及持续时间都明显降低(均P<0.05)；与治疗前比较，患者治疗后7d、14d、3个月、6个月时的触诱发痛的分级都明显降低，差异均有统计学意义(均P<0.05)；与治疗前相比，治疗后14d、3个月、6个月的PDI评分明显降低(P<0.05)；与治疗前相比，治疗后14d、3个月、6个月的PHQ-9评分和GAD-7评分都明显减少(P<0.05)，与术前的药物使用情况相比，治疗后各镇痛药使用人数普遍呈下降趋势；术中及整个随访期间未观察到严重不良事件。结论短时程脊髓电刺激对爆发痛合并触诱发痛的急性期带状疱疹具有较好的临床疗效。     

Giant intracranial arteriovenous malformation as a possibility of epileptic seizures in a case of drowning

A male in his late 50s had been complaining of headaches and dizziness for 25 years. He also had episodes of losing consciousness, but had not sought treatment because of financial hardship. He was found in the ocean. Autopsy revealed foamy liquid leaking from his nose and mouth, and pleural effusions. The trachea and bronchi contained the same foamy liquid. The lungs were swollen and edematous, and leaked a large amount of foamy liquid. His cause of death was diagnosed as drowning. In the brain, the veins on the frontal lobe and the temporal pole, each on the right cerebral hemisphere, were dilated. A vascular lesion measuring 5 × 5 × 8 cm was found on the bottom of the right frontal lobe, and was located between the right middle cerebral artery and those veins. This vascular lesion extended to the brain parenchyma, and the basal ganglia of the right cerebrum was displaced outward and upward. The vascular lesions in the brain showed blood vessels of various sizes and shapes, and some of the vessel walls were thickened. The vascular lesion on the right frontal lobe was diagnosed as an arteriovenous malformation (AVM). According to the police investigation, the harbor where his body was found was a place he often came for fishing and walking. The possibility of suicide cannot be ruled out. Moreover, it was considered that his AVM might have rendered him unconscious, causing him to fall into the ocean.     

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

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.