Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
-
Vertebral end-plate changes and paraspinal muscles are recently getting much more attention, since they could be associated with intervertebral disc degeneration (IVDD) and low back pain (LBP). Even though obesity is known as a risk factor for LBP, the role of obesity in the process of LBP is still controversial. In this study, we aimed to identify whether increased body mass index (BMI) was associated with IVDD, vertebral end-plate changes and paraspinal muscle quality. ⋯ There was a higher trend of harboring Modic change at any lumbar level in obese patients, significantly in women (35.9% vs. 16.4%, p = 0.026). More severe fatty infiltration in the paraspinal muscles was seen at upper lumbar levels of the obese patients, particularly in women. Patients with higher BMI and suffering from LBP, had more fatty infiltration in the paraspinal muscles at the upper lumbar levels, more severe IVDD, and Modic changes at the lower lumbar levels; particularly women.
-
Impending respiratory failure is catastrophic neurological deterioration caused by repeated c of a brainstem cavernous malformation (BSCM). The benefit and outcome prediction of acute surgery for this fatal condition is rarely reported. In this study, the authors reported a case series of acute surgical treatment (≤3 weeks after the last hemorrhagic episode) for the BSCM with impending respiratory failure and reviewed literature over the past 20 years. ⋯ Repeated hemorrhagic BSCM with impending respiratory failure can benefit from acute surgical treatment. The ICH score, PPH score, and Lawton's BSCM grading are promisingly useful tools for fast and efficient surgical outcome prediction.
-
Perioperative blood transfusion has been associated with poor outcomes but the impacts of transfusion after fusion for lumbar stenosis have not been well-described. We assessed this effect in a large cohort of patients from 2012 to 2018 in the National Surgical Quality Improvement Program (NSQIP). We evaluated baseline characteristics including demographics, comorbidities, hematocrit, and operative characteristics. ⋯ However, after matching, no significant differences remained. In the matched cohorts, transfusion was associated with increased prolonged LOS (OR 1.66, 95% CI 1.45-1.91, p < 0.001), minor complication (OR 1.60, 95% CI 1.20-2.12, p = 0.001), major complication (OR 1.51, 95% CI 1.16-1.98, p = 0.003), any complication (OR 1.54, 95% CI 1.24-1.92, p < 0.001), discharge to facility (OR 1.70, 95% CI 1.48-1.95, p < 0.001), 30-day readmission (OR 1.56, 95% CI 1.23-1.99, p < 0.001), and 30-day reoperation (OR 1.85, 95% CI 1.35-2.53, p < 0.001). Although transfusion is performed based on perceived clinical need, this study contributes to growing evidence that it is important to balance the risks of perioperative blood transfusion with its benefits.
-
We describe a simple technique of securing surgically implanted leads for spinal cord (SCS), dorsal root ganglion (DRG) and occipital nerve stimulation (ONS), for both primary surgical implantation and correcting lead migration. This technique could also be adapted for securing percutaneously implanted leads. ⋯ There were no cases of primary or recurrent lead migration in any patient undergoing lead placement using mini-plate anchorage. The technique appears to offer a reliable means of preventing post-operative lead migration in a variety of spinal and extra-cranial neuromodulation implants.
-
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. ⋯ Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.