AI Article Synopsis

  • Effective chest compressions are crucial for successful CPR, but they can sometimes cause serious injuries.
  • A case is presented involving an 80-year-old man who experienced major complications after receiving CPR.
  • He was later diagnosed with a deep epicardial laceration, highlighting the potential risks associated with effective chest compressions.

Article Abstract

Effective chest compressions have been proven to be a key element in a successful cardiopulmonary resuscitation (CPR). However, unintended injuries have been described in the medical literature for decades, including major intrathoracic injuries. We present a case of an 80-year-old man after a successful CPR who was later diagnosed with deep epicardial laceration as a result of effective chest compressions.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8549835PMC
http://dx.doi.org/10.5041/RMMJ.10455DOI Listing

Publication Analysis

Top Keywords

deep epicardial
8
epicardial laceration
8
cardiopulmonary resuscitation
8
effective chest
8
chest compressions
8
laceration cardiopulmonary
4
resuscitation case
4
case report
4
report effective
4
compressions proven
4

Similar Publications

Objectives: Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT.

Methods: Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included.

View Article and Find Full Text PDF

In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI-driven techniques leverage large datasets to develop and train algorithms capable of making precise evaluations and predictions. The realm of cardiac CT is expanding day by day and multiple tools are offered to answer different questions.

View Article and Find Full Text PDF

Leveraging calcium score CT radiomics for heart failure risk prediction.

Sci Rep

November 2024

Center for Computational and Precision Health (C3PH), DeBakey Heart and Vascular Center, Houston Methodist, Houston, TX, 77030, USA.

Article Synopsis
  • Researchers aimed to find a screening method using computed tomography calcium scoring (CTCS) to assess the risk of heart failure (HF) in patients, particularly focusing on those with type 2 diabetes.
  • They analyzed CTCS scans from nearly 2,000 patients and applied deep learning to create models that predict HF risk based on radiomic features of epicardial adipose tissue (EAT) and calcifications.
  • The study found that CTCS-based models, especially those using fat-omics for non-diabetic patients and calcium-omics for diabetic patients, significantly outperformed traditional clinical prediction methods in forecasting incident HF.
View Article and Find Full Text PDF

An enhanced deep learning method for the quantification of epicardial adipose tissue.

Sci Rep

October 2024

Department of Radiology, the Second Xiangya Hospital, Central South University, No. 139 Middle Renmin Road, Furong District, Changsha, 410000, China.

Article Synopsis
  • - Epicardial adipose tissue (EAT) is important in cardiovascular disease progression, but measuring its volume manually is tough and prone to errors.
  • - This study introduces a new deep learning method for EAT quantification using coronary computed tomography angiography (CCTA) that combines data-driven techniques with specific anatomical information.
  • - The automated method showed strong agreement with traditional manual measurements, achieving high accuracy for both 2D slices and 3D volumes, suggesting its potential value in clinical settings.
View Article and Find Full Text PDF
Article Synopsis
  • Recent studies have focused on measuring epicardial adipose tissue (EAT) to predict major cardiovascular events, but traditional metrics have shown limited effectiveness.
  • This study aimed to develop advanced EAT features called "fat-omics," which capture more detailed aspects of EAT's role in cardiovascular health, enhancing MACE prediction.
  • Through testing a cohort of 400 patients, the novel 15-feature fat-omics model significantly outperformed traditional measures, showing better risk stratification for cardiovascular events.
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!