Background: Controversy continues as to the most safe and reliable method for clearing the cervical spine (C-spine) in a trauma patient who is rendered unable to participate in a clinical examination. Although magnetic resonance imaging (MRI) is the most sensitive test to detect soft-tissue injuries, it is impractical for routine use in every patient largely because of its cost and time of acquiescence. Recent studies have advocated the sole use of multidetector computed tomographic (MDCT) scans of the C-spine to decide if cervical collar immobilization can be discontinued. The current investigation retrospectively reviewed a series of MDCT scans obtained after an acute traumatic event that were used to direct treatment in the emergency department (ED) or intensive care unit.
Methods: Seven-hundred and eight trauma patients consecutively admitted to the ED between June 2001 and July 2006 underwent a computed tomographic scan of their C-spine as part of an institutional protocol. We identified 91 patients with MDCT scans that were officially recorded as adequate and negative by an attending ED radiologist who had also undergone an MRI during the same trauma admission period. Retrospectively, two fellowship-trained spine surgeons independently reviewed these MDCT studies to address the following questions: (1) Is the study adequate? (2) Is it suggestive of an acute injury? (3) Is there sufficient information to safely recommend collar removal? Institutional Review Board approval was obtained before the images were reviewed. Neither clinical examination findings nor MRI readings were made available to the surgeon evaluators.
Results: Both spine surgeons agreed that 76 of the 91 studies (84%) were adequate to evaluate for possible C-spine injuries. Seven of 91 MDCT scans (8%) were deemed inadequate by both surgeons (95% confidence interval, 2.3-13.1). Reasons for inadequacy included motion artifact, insufficient visualization of the cervical-thoracic or occipital-cervical junctions, incomplete reconstructive views, or poor quality. Three of the adequate MDCT scans had fractures that were identified by both of the spine surgeons; 4 additional fractures and 15 findings suspicious for instability were identified by at least one of the surgeons. Ultimately, 22 of 91 MDCT scans read as adequate and normal by attending radiologists were deemed suspicious for abnormality by the spine surgeons. Of these 22 cases, the official MRI reading was positive for a trauma-related abnormality in 17 cases.
Conclusions: C-spine clearance of patients without the ability participate in a clinical examination remains difficult. A multidisciplinary, algorithmic approach generally yields the most consistent results. However, our data highlight that reliance on a single imaging modality may lead to missed diagnosis of C-spine injuries. These data suggest that early involvement of the spine service for radiographic clearance may help identify occult injuries or suspicious findings necessitating further evaluation.
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http://dx.doi.org/10.1097/TA.0b013e3181b021da | DOI Listing |
Osteoporos Int
December 2024
Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts Der Isar, TUM School of Medicine and Health, Technical University of Munich, Ismaninger Str 22, 81675, Munich, Germany.
Unlabelled: This study aimed to validate the correlation between volumetric bone mineral density in the cervicothoracic and lumbar spine using measurements from opportunistic CT scans. The bone density assessment proved feasible, allowing us to propose optimal cut-off values for diagnosing osteoporosis and predicting vertebral fractures in the cervical and thoracic spine.
Objectives: To investigate the performance of cervicothoracic volumetric bone mineral density (vBMD), obtained through opportunistic quantitative computed tomography (QCT), in discriminating patients with/without osteoporosis and with/without vertebral fractures (VFs), using lumbar vBMD as the reference.
Eur Heart J Open
September 2024
Kore University, Faculty of Medicine, Piazza dell'Università, 94100 Enna EN, Italy.
Aims: Subclinical thrombosis may represent an early stage of prosthesis structural disease. Most of the available evidence on the incidence, location, predictors, and consequences of thrombosis comes from studies that have employed balloon-expandable valves. We aimed to describe the different localisations of valvular and perivalvular thrombosis and analyse prosthesis-host multi-detector computed tomography predictors in the context of self-expandable prosthesis.
View Article and Find Full Text PDFForensic Sci Res
September 2023
Department of Oral and Maxillofacial Radiology-Forensic Odontology, Faculty of Dentistry, Universitas Padjadjaran, Bandung, Indonesia.
Int J Legal Med
December 2024
Centre for Forensic Anthropology, School of Social Sciences, University of Western Australia, Crawley, WA, 6009, Australia.
It is imperative in a forensic investigation to determine the identity of an unidentified corpse, for which a crucial starting point is to establish population affinity as part of the biological profile supplied by the forensic anthropologist. The present study investigates the feasibility of using multidetector computed tomography (MDCT) images to quantify craniometric variation between Japanese and Malay populations relative to the estimation of population affinity in a forensic context. The Japanese and Malay samples comprise MDCT scans of 252 (122 female; 130 male) and 182 (84 female; 98 male) adult individuals, respectively.
View Article and Find Full Text PDFCirc J
November 2024
Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University.
Background: Accurate prediction of short-term mortality in patients with acute pulmonary embolism (PE) is critical for optimizing treatment strategies and improving patient outcomes. The Pulmonary Embolism Severity Index (PESI) is the current reference score used for this purpose, but it has limitations regarding predictive accuracy. Our aim was to develop a new short-term mortality prediction model for PE patients based on deep learning (DL) with multimodal data, including imaging and clinical/demographic data.
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