Background: The fraction of cardiac arrest patients presenting with pulseless electrical activity is increasing, and it is likely that many of these patients have pseudo-electromechanical dissociation (P-EMD), a state in which there is residual cardiac contraction without a palpable pulse. The efficacy of cardiopulmonary resuscitation (CPR) with external chest compression synchronized with the P-EMD cardiac systole and diastole has not been fully evaluated.
Hypothesis: During external chest compression in P-EMD, the coronary perfusion pressure (CPP) will be greater with systolic synchronization compared with diastolic phase synchronization.
Methods: A porcine model of P-EMD induced by progressive hypoxia with peak aortic pressures targeted to 50 mmHg was used. CPR chest compressions were performed by either load distributing band or vest devices. Paired 10s intervals of systolic and diastolic synchronization were performed randomly during P-EMD, and aortic, right atrial and CPP were compared.
Results: Stable P-EMD was achieved in 8 animals, with 2.6±0.5 matched synchronization pairs per animal. Systolic synchronization was association with increases in relaxation phase aortic pressure (41.7±8.9 mmHg vs. 36.9±8.2 mmHg), and coronary perfusion pressure (37.6±11.7 mmHg vs. 30.2±9.6 mmHg). Diastolic synchronization was associated with an increased right atrial pressure (6.7±4.1 mmHg vs. 4.1±5.7 mmHg).
Conclusion: During P-EMD, synchronization of external chest compression with residual cardiac systole was associated with higher CPP compared to synchronization with diastole.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.resuscitation.2012.02.016 | DOI Listing |
Eur Radiol
January 2025
Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Objective: This study aimed to develop an open-source multimodal large language model (CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists.
Materials And Methods: For training, we collected 592,580 publicly available CXRs, of which 374,881 had labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided free-text radiology reports (Dataset 2). After pre-training a vision transformer with Dataset 1, we integrated it with an LLM influenced by the LLaVA network.
Heliyon
January 2025
Chest Clinical College of Tianjin Medical University, Tianjin, 300270, China.
Backgroud: Fluid volume abnormalities are a major cause of exacerbations in heart failure patients. However, there is few efficient, rapid, or cost-effective clinical approach for determining volume status, resulting in inadequate or unsatisfactory treatment. The aim was to develop an early fluid volume detection model for heart failure patients utilizing a machine learning stratification.
View Article and Find Full Text PDFInt J Crit Illn Inj Sci
December 2024
Department of Trauma and Emergency, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India.
Background: Train collision accidents are tragic events associated with high mortality. The study aimed to comprehensively describe the clinical-epidemiological profile, disaster emergency response, and management following a train collision accident in Odisha, India.
Methods: This observational study was conducted by a tertiary care hospital in eastern India.
Background And Aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.
Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain.
Ophthalmol Sci
November 2024
Liverpool Ocular Oncology Research Group, Department of Eye and Vision Science, Institute of Life Course and Medical Sciences (ILCaMS), University of Liverpool, Liverpool, United Kingdom.
Purpose: Testing the validity of a self-supervised deep learning (DL) model, RETFound, for use on posterior uveal (choroidal) melanoma (UM) and nevus differentiation.
Design: Case-control study.
Subjects: Ultrawidefield fundoscopy images, both color and autofluorescence, were used for this study, obtained from 4255 patients seen at the Liverpool Ocular Oncology Center between 1995 and 2020.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!