AI Article Synopsis

  • The study aimed to validate the Edinburgh diagnostic criteria for identifying cerebral amyloid angiopathy (CAA) in patients with non-traumatic intracerebral lobar hemorrhage (ICH) using CT and MRI scans, excluding genetic factors like APOE status.
  • Researchers included 102 patients and found that 36 had CAA, while 46 had non-CAA causes, and they achieved an AUC of 0.760 for the original Edinburgh model, which improved to 0.808 with the addition of cortical involvement as a feature.
  • The conclusion supports the use of modified Boston MRI criteria for diagnosing CAA and proposes a new three-variable prediction model to enhance diagnostic accuracy in urgent cases of spontaneous lobar ICH.

Article Abstract

Objective: Based on histopathology, Edinburgh diagnostic criteria were proposed to consider a nontraumatic intracerebral lobar hemorrhage (ICH) as related to cerebral amyloid angiopathy (CAA) using the initial computed tomography (CT) scan and the APOE genetic status. We aimed to externally validate the Edinburgh prediction model, excluding the APOE genotyping and based on the modified Boston criteria on the MRI for CAA diagnosis METHODS: We included patients admitted for spontaneous lobar ICH in the emergency department between 2016 and 2019 who underwent noncontrast CT scan and MRI. According to the MRI, patients were classified into the CAA group or into the non-CAA group in the case of other causes of ICH. Two neuroradiologists, blinded to the final retained diagnosis, rated each radiological feature on initial CT scan described in the Edinburgh study on initial CT scan RESULTS: A total of 102 patients were included, of whom 36 were classified in the CAA group, 46 in the non-CAA causes group and 20 of undetermined cause (excluded from the primary analysis). The Edinburgh prediction model, including finger-like projections and subarachnoid extension showed an area under receiver operating characteristic curves (AUC) of 0.760 (95% confidence interval, CI: 0.660-0.859) for the diagnosis of CAA. The AUC reached 0.808 (95% CI: 0.714-0.901) in a new prediction model integrating a third radiologic variable: the ICH cortical involvement.

Conclusion: Using the Boston MRI criteria as a final assessment, we provided a new external confirmation of the radiological Edinburgh CT criteria, which are directly applicable in acute settings of spontaneous lobar ICH and further proposed an original 3‑set model considering finger-like projections, subarachnoid extension, and cortical involvement that may achieve a high discrimination performance.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00062-022-01230-6DOI Listing

Publication Analysis

Top Keywords

prediction model
12
edinburgh criteria
8
cerebral amyloid
8
amyloid angiopathy
8
edinburgh prediction
8
spontaneous lobar
8
lobar ich
8
classified caa
8
caa group
8
non-caa group
8

Similar Publications

Who is coming in? Evaluation of physician performance within multi-physician emergency departments.

Am J Emerg Med

January 2025

Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.

Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.

Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.

View Article and Find Full Text PDF

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease, and it can be used as an important indicator of disease progression. However, many existing methods focus mainly on the image itself when processing brain imaging data, ignoring other non-imaging data (e.g.

View Article and Find Full Text PDF

Visibility, Physical Work Environment, and Stress in ICU Nurses.

J Nurs Adm

December 2024

Author Affiliations: Research Associate (Dr Keys), The Center for Health Design, Concord, California; National Senior Director (Dr Fineout-Overholt), Evidence-Based Practice and Implementation Science, at Ascension in St. Louis, MO.

Objective: Relationships among coworker and patient visibility, reactions to physical work environment, and work stress in ICU nurses are explored.

Background: Millions of dollars are invested annually in the building or remodeling of ICUs, yet there is a gap in understanding relationships between the physical layout of nursing units and work stress.

Methods: Using a cross-sectional, correlational, exploratory, predictive design, relationships among variables were studied in a diverse sample of ICU nurses.

View Article and Find Full Text PDF

Learning the language of antibody hypervariability.

Proc Natl Acad Sci U S A

January 2025

Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.

Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.

View Article and Find Full Text PDF

Geometrically modulated contact forces enable hula hoop levitation.

Proc Natl Acad Sci U S A

January 2025

Applied Mathematics Laboratory, Courant Institute of Mathematical Sciences, Department of Mathematics, New York University, New York, NY 10012.

Mechanical systems with moving points of contact-including rolling, sliding, and impacts-are common in engineering applications and everyday experiences. The challenges in analyzing such systems are compounded when an object dynamically explores the complex surface shape of a moving structure, as arises in familiar but poorly understood contexts such as hula hooping. We study this activity as a unique form of mechanical levitation against gravity and identify the conditions required for the stable suspension of an object rolling around a gyrating body.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!