Journal of neurotrauma最新文献

With the aging population, symptomatic chronic subdural hematoma (CSDH) is becoming increasingly prevalent in neurosurgical practice. While burr-hole drainage remains the mainstay treatment, the optimal drilling site remains controversial. This single-center, randomized controlled noninferiority trial aimed to compare frontal versus parietal burr-hole approaches in patients aged ≥18 years requiring surgical drainage for CSDH. Participants were randomized (1:1) via computer-generated allocation to frontal or parietal burr-hole groups, with blinding maintained for patients and staff except operating neurosurgeons. All patients received postoperative atorvastatin combination therapy. Primary outcomes included 6-month recurrence rates (noninferiority margin: 5.0%), with secondary outcomes assessing functional status (modified Rankin Scale [mRS] 4-6), mortality, and complications. From July 2020 to December 2022, 135 of 147 screened patients (92%) were enrolled (frontal: n = 67; parietal: n = 68), comprising 79% males (n = 107) and 21% females (n = 28). At 6-month follow-up (completed June 2023), recurrence rates were 1.5% (1/67) in the frontal group versus 4.4% (3/68) in the parietal group (difference: -2.9%; 95% confidence interval [CI]: -8.6 to 2.8; p = 0.31), meeting noninferiority criteria. Functional outcomes (mRS 4-6: 3.0% vs. 4.4%, p = 0.66) and mortality (3.0% vs. 1.5%, p = 0.55) showed no significant intergroup differences. Notably, postoperative pneumocephalus volume was significantly lower in the frontal group (11.6 ± 14.8 mL vs. 20.7 ± 20.4 mL; p = 0.038). Adverse event rates were comparable between groups, with pneumonia being most frequent (53.7% vs. 55.9%) and surgical complications similarly distributed (6.0% vs. 5.9%). These findings establish noninferiority of frontal burr-hole while demonstrating reduced postoperative pneumocephalus, supporting its clinical preference and warranting future superiority trials. (Trial registration: chictr.org.cn, ChiCTR2000033967).

Recent advancements in machine learning have increased studies predicting neurological outcomes following spinal cord injury (SCI). However, there is limited research on predictive models for bladder and bowel dysfunction outcomes postinjury. This study aims to develop predictive models for bladder and bowel dysfunction outcomes in patients with traumatic SCI and integrate the models into a web application. This study utilized data from 4181 patients with traumatic SCI, registered in the Japan Association of Rehabilitation Database between 1991 and 2015, to develop and validate predictive models. The explanatory variables were categorized into three groups: neurological findings at admission (such as American Spinal Injury Association scores and Functional Independence Measure scores), patient background (including demographics, comorbidities, and insurance status), and SCI pathology (including injury mechanism, vertebral fractures, surgical history, presence of ossification of the posterior longitudinal ligament/OLF, and time to admission). Feature selection was performed using Boruta, excluding features with more than 25% missing values. The target variables were the bladder and bowel functions at discharge, classified into a binary outcome of whether natural urination and defecation were possible. Machine learning models were implemented using PyCaret, and model performance was evaluated using the area under the curve (AUC). Shapley Additive Explanation (SHAP) values assessed the contribution of individual features. A total of 3,949 cases were analyzed, with an average age of 50.3 years. The model with the highest accuracy for predicting bladder function was the gradient boosting model, achieving an AUC of 0.9064 on the test data. For predicting bowel function, the gradient boosting model showed the highest accuracy with an AUC of 0.8714. The top three key predictive factors identified using SHAP values included L3 motor function, time from injury to admission, and the Functional Independence Measure bowel management score, which were common predictors for both bladder and bowel function. The web application of the predictive models can be found at https://takakikitamura-bladder-prediction.hf.space/ and https://takakikitamura-bowel-prediction.hf.space. In conclusion, we developed a predictive model for bladder and bowel dysfunction outcomes after traumatic SCI using machine learning, confirming its high predictive accuracy. Critical predictors included L3 motor function, time from injury to admission, and the degree of bowel dysfunction, all of which were relevant for predicting both bladder and bowel function. These models were made publicly available as a web application.

Secondary loss of initially spared white and grey matter is a major driver of morbidity after spinal cord injury (SCI). Current treatments have not substantially changed in decades and are limited to surgical decompression and blood pressure management. White matter atrophy after SCI is primarily caused by secondary axonal degeneration (SAD), which is triggered by maladaptive axonal uptake of sodium and calcium through a multitude of ion channels and transporters. While specific inhibitors have been studied, none have been translated into clinical use, in part due to the diverse array of involved channels. Here, we studied whether amiodarone, an FDA-approved antiarrhythmic drug that exerts pleotropic inhibition of multiple sodium and calcium channels, might be neuro- and axonoprotective after SCI precisely because of its broad inhibitory profile. Mice were submitted to off-midline thoracic SCI versus sham surgery and treated with amiodarone versus vehicle control within 15 min and after 4 h of injury. We found that amiodarone treatment after SCI improved locomotor function, which was longitudinally measured over 28 days with the Basso mouse scale, accelerating rotarod, and inclined plane tests. Amiodarone treatment reduced spinal cord atrophy and white matter loss at 28 days after injury, assessed by spinal cord wet weights and by volumetric measurements of grey and white matter in serial coronal sections of spinal cords stained with luxol fast blue and cresyl violet. Amiodarone was directly axonoprotective after SCI, with reduced losses of neurofilament heavy positive axons at 28 days. Interestingly, long-term amiodarone-mediated axonoprotection was accompanied by a reduction of SAD at early time points, measured by counting axonal spheroids 24 h after SCI in fluorescently labeled corticospinal tract axons imaged with light sheet imaging. Overall, these data identify amiodarone as a potentially axonoprotective agent that could be repurposed to treat secondary injury after SCI.

After a traumatic brain injury, around 12% of patients require surgical interventions during their index hospitalization due to delayed or progressive intracranial hemorrhage or complications such as elevated intracranial pressure (ICP)¹. Compiling data from four harmonized studies with 288 patients that have high-frequency physiological measurements, including ICP, we aimed to determine factors associated with those surgeries and whether longitudinal physiological measurements could be used to predict the need for craniectomy or craniotomy at least 1 h before the surgery occurred. The outcome was the occurrence of the first cranial surgery 6-120 h post-injury with 2:1 matched controls for those without surgery. Covariates included baseline characteristics and dynamic physiological measurements. Univariate associations were assessed, and the area under the receiving operating characteristic curve (AUC) was used to compare various machine learning and multivariable statistical models for the prediction of surgery. It was found that means, medians, and transgressions of both ICP and mean arterial pressure, as well as the linear regression slope of ICP by time in the 6 h prior to surgery, were significantly and independently related to whether a patient had cranial surgery or not. The best-performing model was found using random forests supervised learning algorithm (AUC = 0.75, 95% confidence interval 0.61-0.88). This model may assist clinicians in predicting when they may need to perform an emergent neurosurgical procedure, thus preventing more damage from elevated ICPs.

In 2023, the American Congress of Rehabilitation Medicine Brain Injury Interdisciplinary Special Interest Group (ACRM BI-ISIG) Mild Traumatic Brain Injury (TBI) Task Force published updated diagnostic criteria for mild TBI. These criteria were developed in collaboration with a panel of 32 subject matter experts in mild TBI using the Delphi method. The 2023 ACRM diagnostic criteria marked the first update since 1993, incorporating three decades of research advancements in our understanding of mild TBI. To facilitate the consistent use of the new diagnostic criteria, the ACRM BI-ISIG Mild TBI Task Force initiated a special project in September 2023 to develop a structured interview to apply the ACRM diagnostic criteria for mild TBI in clinical and research settings. The purpose of this article is to describe the development of the ACRM Structured TBI Interview and the accompanying documents. The ACRM Structured TBI Interview was developed in four phases: (1) initial development of a draft interview by two project leads, (2) review and revision over three rounds by 17 members of the ACRM BI-ISIG Mild TBI Task Force, (3) external review by 19 subject matter experts in mild TBI, and (4) field testing of the ACRM Structured TBI Interview by 11 interviewers who completed 25 diagnostic interviews. In addition to the ACRM Structured TBI Interview, three other documents were developed to help facilitate the administration of the interview (Administration Guide) and to apply the diagnostic criteria (Diagnostic Coding Form and Diagnostic Flow Diagram). A Short Form was also developed for use in contexts where administering the full structured interview is not feasible due to time constraints.

The increasing use of artificial intelligence-driven chatbots for medical queries requires a systematic evaluation of their accuracy, reliability, and potential role in patient education. This study assesses the performance of three widely used chatbots-ChatGPT, Google Gemini, and Microsoft CoPilot-in answering patient-oriented questions related to traumatic brain injury (TBI). A standardized set of questions related to TBI was developed, divided into eight subtopics, and presented to each chatbot using unified prompts. The responses were evaluated together with reference answers prepared by experts from a group of specialists in the fields of neurology, neurosurgery, and neurorehabilitation, and subsequently assessed in a survey of patients undergoing rehabilitation for TBI. Performance was evaluated using a modified scoring framework in five key dimensions of quality. Statistical analysis included multivariate analysis of variance to compare chatbot performance and logistic regression analysis to determine the likelihood of chatbot responses being considered an adequate substitute for expert advice. Significant differences between the chatbots were found in several quality dimensions, with ChatGPT scoring higher than Gemini and CoPilot on reliability, responsiveness, and perceived trustworthiness (p < 0.05). No chatbot consistently demonstrated an advantage in conveying empathy. Logistic regression analysis revealed that responses from ChatGPT were significantly more likely to be rated as an adequate substitute for expert input (p < 0.0001, OR = 4.3, 95% CI: 2.4-7.6). AI-driven chatbots vary in their ability to provide high-quality medical information, with significant differences in reliability and responsiveness. While ChatGPT outperformed other models in providing structured information, further improvements in context awareness and empathy are needed before broader clinical integration can be considered.

Neurogenic lower urinary tract dysfunction (NLUTD) is a major cause of morbidity and reduced quality of life after spinal cord injury (SCI). In pre-clinical research, small and large animal models such as rats, dogs, and minipigs have been used to investigate NLUTD through urodynamic studies (UDS) such as conventional filling cystometry. Although filling cystometry is currently considered the gold standard for bladder monitoring in pre-clinical research, this approach has several well-recognized limitations. The aim of this study was to develop and evaluate the feasibility of an implantable, radiotelemetric system for monitoring bladder pressure in a Yucatan minipig model of SCI. The transmitter was surgically implanted in the dome of the bladder and several UDS experiments were conducted to evaluate the system's effectiveness at measuring pressure compared to conventional UDS equipment. We observed a strong correlation and agreement between the transmural telemetry sensor and the UDS system. There was no significant difference between bladder compliance and baseline bladder pressure between the two sensor systems. However, the telemetry system recorded significantly lower voiding and non-voiding contraction pressure amplitudes as well as lower voiding threshold pressures and detrusor after-contraction measured with the telemetry system. The telemetry system appeared to be a reliable and accurate method for assessing bladder pressure and allowed for an evaluation of urodynamics in a pig model of SCI for several months. The application of this method could enable a more detailed in vivo evaluation of NLUTD after SCI and a better understanding of micturition behavior during natural-filling, ambulatory urodynamics.