Objective: Pulmonary embolism (PE) is a common complication of SARS-CoV-2 infection. We aimed to explore the short-term outcomes among patients with acute PE and COVID-19 and to further determine and compare the performance of the different prognostic scores (PESI, sPESI, BOVA, FAST and ESC scores) for risk-stratification in this scenario.
Methods: Retrospective single-centre study of 85 patients with SARS-CoV-2 infection and PE admitted to the Emergency Department (ED). The diagnostic accuracy of each above-mentioned prognostic score was calculated post hoc, and their discriminative power was evaluated through an AUC curve.
Results: Among the 85 patients, all-cause death occurred within 7 days for 6 patients (7.1%) and within 30 days for 14 patients (16.5%). Despite being older and having a higher percentage of altered mental status on presentation, non-survivors patients did not differ from survivors regarding comorbidities, traditional risk factors for venous thromboembolism and signs and symptoms at the ED presentation.Each risk stratification tool had modest discriminative power for 7-day mortality (AUC range, 0.601-0.730) with slightly lower discrimination for 30-day mortality (AUC range, 0.543-0.638). The pair-wise comparison of ROC curves showed that PESI had better predictive value for short-term mortality than ESC score (z test = 3.92, p = 0.001) and sPESI (z test = 2.43, p = 0.015); there is no significant difference between PESI and BOVA score (z test = 1.05, p = 0.295) and FAST score (z test = 0.986, p = 0.324).
Conclusion: The most common risk-stratification tools for PE had modest discriminative power to predict short-term mortality in patients with acute PE and COVID-19.
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http://dx.doi.org/10.1016/j.ijcha.2022.100984 | DOI Listing |
Alzheimers Dement
December 2024
Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
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View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Pennsylvania, Philadelphia, PA, USA.
Background: Structural and functional heterogeneity in the brains of patients with Alzheimer's disease (AD) leads to diagnostic and prognostic uncertainty and confounds clinical treatment planning. Normative modelling, where individual-level deviations in brain measures from a reference sample are computed to infer personalized effects of disease, allows parsing of disease heterogeneity. In this study, GAN based normative modelling technique quantifies individual level neuroanatomical abnormality thereby facilitating measurement of personalized disease related effects in AD patients.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Cumulus Neuroscience, Dublin, Ireland.
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View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Republic of Korea.
Unlabelled: This study aims to create a fatigue recognition system that utilizes electroencephalogram (EEG) signals to assess a driver's physiological and mental state, with the goal of minimizing the risk of road accidents by detecting driver fatigue regardless of physical cues or vehicle attributes. A fatigue state recognition system was developed using transfer learning applied to partial ensemble averaged EEG power spectral density (PSD). The study utilized layer-wise relevance propagation (LRP) analysis to identify critical cortical regions and frequency bands for effective fatigue discrimination.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Electronics and Communication Engineering, IFET College of Engineering, Villupuram, Tamilnadu India.
Unlabelled: Breast cancer (BC) remains a significant global health issue, necessitating innovative methodologies to improve early detection and diagnosis. Despite the existence of intelligent deep learning models, their efficacy is often limited due to the oversight of small-sized masses, leading to false positive and false negative outcomes. This research introduces a novel segmentation-guided classification model developed to increase BC detection accuracy.
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