The incidence of community-onset bacteremia caused by extended-spectrum-β-lactamase (ESBL) producers is increasing. The adverse effects of ESBL production on patient outcome have been recognized and this antimicrobial resistance has significant implications in the delay of appropriate therapy. However, a simple scoring algorithm that can easily, inexpensively, and accurately be applied to clinical settings was lacking. Thus, we established a predictive scoring algorithm for identifying patients at the risk of ESBL-producer infections among patients with community-onset monomicrobial Enterobacteriaceae bacteremia (CoMEB).In a retrospective cohort, multicenter study, adults with CoMEB in the emergency department (ED) were recruited during January 2008 to December 2013. ESBL producers were determined based on ESBL phenotype. Clinical information was obtained from chart records.Of the total 1141 adults with CoMEB, 65 (5.7%) caused by ESBL producers were identified. Four independent multivariate predictors of ESBL-producer bacteremia with high odds ratios (ORs)-recent antimicrobial use (OR, 15.29), recent invasive procedures (OR, 12.33), nursing home residents (OR, 27.77), and frequent ED user (OR, 9.98)-were each assigned +1 point to obtain the CoMEB-ESBL score. Using the proposed scoring algorithm, a cut-off value of +2 yielded a high sensitivity (84.6%) and an acceptable specificity (92.5%); the area under the receiver operating characteristic curve was 0.92.In conclusion, this simple scoring algorithm can be used to identify CoMEB patients with a high ESBL-producer infection risk. Of note, frequent ED user was firstly demonstrated to be a crucial predictor in predicting ESBL-producer infections. ED clinicians should consider adequate empirical therapy with coverage of these pathogens for patients with risk factors.
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http://dx.doi.org/10.1097/MD.0000000000006648 | DOI Listing |
JMIR Form Res
January 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Background: To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of China.
In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped network with dual-attention, named DAU-Net, divided into encoder and decoder parts. Wherein, we replace the traditional convolutional layers with ConvNeXt Block and SnakeConv Block to strengthen its recognition ability for different forms of blood vessels while lightweight the model.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Information Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Anesthesiology, Daping Hospital, Army Medical University, Chongqing, China.
Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.
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