Purpose: This study aimed to evaluate the ability of ultrafast power Doppler (PD) to assess disease activity in rheumatoid arthritis (RA) by examining the correlations between variables from ultrafast PD perfusion imaging and clinical measures of disease activity.
Methods: Thirty-three RA patients underwent clinical assessments of disease activity and ultrasound scans of bilateral wrists using both ultrafast and conventional PD systems. A spatial singular value decomposition filter was applied to the ultrafast PD imaging. Singular vectors representing perfusion and fast flows were selected to produce perfusion images. All images were quantitatively analyzed with computer assistance and scored semiquantitatively (0-3) by a physician for synovial vascularity. The Pearson correlation coefficients between image variables and clinical indices were calculated.
Results: The correlation coefficients ranged from weakly to moderately positive between ultrafast PD variables and clinical indices (r=0.221-0.374, all P<0.05). The strongest correlations were observed for synovial PD brightness with the 28-joint Disease Activity Score based on C-Reactive Protein (DAS28-CRP) and the Simplified Disease Activity Index (SDAI). In patients within the deep clinical remission (dCR) subgroup, synovial PD brightness showed stronger correlations with DAS28-CRP, the Clinical Disease Activity Index, and SDAI (r=0.578-0.641, all P<0.001). The correlation coefficients between conventional PD variables and clinical indices were similar to those observed with ultrafast PD variables.
Conclusion: Ultrafast PD imaging effectively extracts capillary blood signals and generates perfusion images. In the RA population, ultrafast PD variables exhibit weak-to-moderate correlations with clinical indices, with these correlations being notably stronger in dCR patients.
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http://dx.doi.org/10.14366/usg.24095 | DOI Listing |
Int J Surg
January 2025
Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Nanjing, Jiangsu, China.
Background: Type A aortic dissection (TAAD) remains a significant challenge in cardiac surgery, presenting high risks of adverse outcomes such as permanent neurological dysfunction and mortality despite advances in medical technology and surgical techniques. This study investigates the use of quantitative electroencephalography (QEEG) to monitor and predict neurological outcomes during the perioperative period in TAAD patients.
Methods: This prospective observational study was conducted at the hospital, involving patients undergoing TAAD surgery from February 2022 to January 2023.
JAMA Intern Med
January 2025
Harvard Medical School, Boston, Massachusetts.
JAMA Intern Med
January 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Importance: There are no validated decision rules for terminating resuscitation during in-hospital cardiac arrest. Decision rules may guide termination and prevent inappropriate early termination of resuscitation.
Objective: To develop and validate termination of resuscitation rules for in-hospital cardiac arrest.
Clin Cancer Res
January 2025
Massachusetts General Hospital Cancer Center, Boston, MA, United States.
Background: Race/ethnicity may affect outcomes in metastatic breast cancer (MBC) due to biological and social determinants. We evaluated the impact of race/ethnicity on clinical, socioeconomic, and genomic characteristics, clinical trial participation, and receipt of genotype-matched therapy among patients with MBC.
Methods: A retrospective study of patients with MBC who underwent cell-free DNA testing (cfDNA, Guardant360â, 74 gene panel) between 11/2016 and 11/2020 was conducted.
ACS Sens
January 2025
Department of Engineering Physics, McMaster University, 1280 Main Street West, L8S 4L8 Hamilton, Ontario, Canada.
Current approaches for classifying biosensor data in diagnostics rely on fixed decision thresholds based on receiver operating characteristic (ROC) curves, which can be limited in accuracy for complex and variable signals. To address these limitations, we developed a framework that facilitates the application of machine learning (ML) to diagnostic data for the binary classification of clinical samples, when using real-time electrochemical measurements. The framework was applied to a real-time multimeric aptamer assay (RT-MAp) that captures single-frequency (12.
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