Objective: Spinal epidural abscesses (SEA's) are a challenge to diagnose, particularly if there are non-contiguous (skip) lesions. There is also limited data to predict which patients can be treated with antibiotics alone and which require surgery. We sought to assess which demographics, clinical and laboratory findings can guide both diagnosis and management of SEA's.
Methods: All patients with SEA (ICD9 324.1, ICD10 G06.1) between April 2011-May 2019 at a single tertiary center were included. A retrospective EMR review was completed. Patient and disease characteristics were compared using appropriate statistical tests.
Results: 108 patients underwent initial surgical treatment versus 105 that were treated medically initially; 22 (21 %) of those failed medical management. Patients who failed medical management had significantly higher CRP, longer symptom duration, and had higher rates of concurrent non-spinal infections. 9% of patients had skip lesions. Patients with skip lesions had significantly higher WBC, ESR, as well as higher rates of bacteremia and concurrent non-spinal infections. Demographic characteristics and proportion with IVDU, smoking, malignancy, and immunosuppression were similar among the three treatment groups.
Conclusions: 21 % of SEA patients failed initial medical management; they had significantly greater CRP, longer symptom duration, more commonly had neurologic deficits, and concurrent non-spinal infections. 9% of patients had skip lesions; they had significantly higher WBC, ESR, rates of bacteremia and infections outside the spine. These variables may guide diagnostic imaging, and identify those at risk of failing of medical management, and therefore require more involved clinical evaluation, and consideration for surgical intervention.
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http://dx.doi.org/10.1016/j.clineuro.2020.106185 | DOI Listing |
World J Gastroenterol
December 2024
School of Computer Science Technology, Changchun University, Changchun 130022, Jilin Province, China.
Background: Wireless capsule endoscopy (WCE) has become an important noninvasive and portable tool for diagnosing digestive tract diseases and has been propelled by advancements in medical imaging technology. However, the complexity of the digestive tract structure, and the diversity of lesion types, results in different sites and types of lesions distinctly appearing in the images, posing a challenge for the accurate identification of digestive tract diseases.
Aim: To propose a deep learning-based lesion detection model to automatically identify and accurately label digestive tract lesions, thereby improving the diagnostic efficiency of doctors, and creating significant clinical application value.
Infect Dis (Lond)
December 2024
Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Background: Vertebral osteomyelitis (VO) is an infection of the spine with increasing prevalence due to improved diagnostics and aging populations. Multiple pathogens, including , spp., and pyogenic bacteria, can cause VO, making differential diagnosis complex, especially in regions with endemic brucellosis and tuberculosis.
View Article and Find Full Text PDFAcad Radiol
November 2024
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan Key Laboratory of Computer Technologies Application, Kunming, China (F.S., S.T., X.W., Y.L., L.W.). Electronic address:
Crit Rev Biomed Eng
November 2024
School of Telecommunications, Shenzhen Polytechnic, Shenzhen, Guangdong, China.
Ultrasound imaging technology plays a vital role in medical imaging. Ovarian ultrasound image segmentation is challenging due to the wide variation in lesion sizes caused by the cancer detection period and individual differences, as well as the noise from reflected wave interference. To address these challenges, we propose an innovative algorithm for ovarian ultrasound image segmentation that incorporates multi-scale features.
View Article and Find Full Text PDFPLoS One
November 2024
Telecommunications Research Centre (TRC), University of Limerick, Limerick, Ireland.
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