Objective: The Rotterdam scoring system (RSS) is useful for prognosis prediction in patients with severe traumatic brain injury (sTBI). It comprises basal cistern, midline shifting (MLS), epidural hematoma (EDH), and subarachnoid hemorrhage (SAH)/intraventricular hemorrhage (IVH) status. Brain computed tomography (CT) is important to assessing patients with sTBI; however, results often change over time. We aimed to determine whether RSS outcome prediction differs by initial brain CT scan time after the trauma in patients with sTBI. Methods: We used data from the second Korea Neurotrauma Data Bank, and analyzed 455 patients; RSS, Glasgow Outcome Scale Extended (GOSE) on 6-months, and the CT scan time were obtained. Unfavorable outcomes were defined as a GOSE score of 1–4. Participants were divided into 2 groups according to when brain CT scan was performed (> or ≤ 2 hours after trauma). The relationship between the prognosis of patients with sTBI and RSS score was examined by calculating the odds ratios. Univariate and multivariate analyses were performed. Results: In both univariate and multivariate analysis, the total RSS and basal cistern status were statistically correlated with prognosis in both groups. EDH and SAH/IVH showed statistically significant difference according to CT scan time. MLS was associated with prognosis in both groups in univariate analysis although not in multivariate analysis. Conclusion The total RSS score predicted prognosis 6 months after trauma in patients with sTBI, regardless of CT scan time. However, the prognostic predictive power of each item constituting the RSS varied according to CT scan time.

Shunt malfunction is the most common cause of ventriculoperitoneal shunt failure. In literature, occlusion of the tube with brain parenchyma, choroid plexus, blood, and proteinaceous debris has been suggested as a mechanism of obstruction. We herein report a case of shunt malfunction without any identifiable occlusion. Our case findings suggest that unapparent abdominal pathology, including inflammation and fibrosis, should be considered when treating shunt failures.

Traumatic retropharyngeal hematoma is a potentially life-threatening complication of cervical spine injury due to possible airway obstruction. Treatment by securing airway and subsequent conservative care is often adequate. However, a rapidly expanding large hematoma requires surgical evacuation. We present a case of 55-year-old man with a retropharyngeal hematoma secondary to cervical vascular injury without associated cervical fracture. The patient was successfully treated with endovascular arterial embolization and subsequent percutaneous drainage under fluoroscopic guidance without any sequelae.

Background: Assessing and improving the quality of trauma care is crucial in modern trauma systems and centers. In Korea, evaluations of regional trauma centers are conducted annually to assess and improve trauma management quality. This includes using the Trauma and Injury Severity Score (TRISS) method to calculate the W-score and mortality Observed-to-Expected ratio (O:E ratio), which are used to evaluate the quality of care. We analyzed the potential for overestimation of the probability of survival using TRISS method for patients with neurotrauma, as well as the potential for errors when evaluating and comparing regional trauma centers. Methods: We included patients who visited the regional trauma center between 2019 and 2021 and compared their probability of survival of the TRISS method, W-score, mortality O:E ratio, and misclassification rates. The patient groups were further subdivided into smaller subgroups based on age, Glasgow Coma Scale (GCS), and Injury Severity Score, and comparisons were made between the neurotrauma and non-neurotrauma groups within each subgroup. Results: A total of 4,045 patients were enrolled in the study, with 1,639 of them having neurotrauma. The neurotrauma patient group had a W-score of −0.68 and a mortality O:E ratio of 1.044. The misclassification rate was found to be 13.3%, and patients with a GCS of 8 or less had a higher misclassification rate of 37.4%. Conclusion The limitations of using the TRISS method for predicting outcomes in patients with severe neurotrauma are exposed in this study. The TRISS methodology demonstrated a high misclassification rate of approximately 40% in subgroups of patients with GCS less than 9, indicating that it may be less reliable in predicting outcomes for severely injured patients with low GCS. Clinicians and researchers should be cautious when using the TRISS method and consider alternative methods to evaluate patient outcomes and compare the quality of care provided by different trauma centers.

Objective: : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods: : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results: : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion : This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.