Publications by authors named "K Hamzi"
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
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- The study focused on the use of a machine learning model using initial transthoracic echocardiography (TTE) to predict in-hospital major adverse events (MAEs) in patients admitted to intensive cardiac care units (ICCU).
- A total of 1,499 patients were evaluated, and the model showed significant accuracy, highlighting five key TTE parameters that contributed to its predictions.
- The machine learning model outperformed traditional scoring methods, indicating it could serve as a better tool for risk stratification in heart patients.
Background: Intensive cardiac care units (ICCUs) were created to manage ventricular arrhythmias after acute coronary syndromes, but have diversified to include a more heterogeneous population, the characteristics of which are not well depicted by conventional methods.
Aims: To identify ICCU patient subgroups by phenotypic unsupervised clustering integrating clinical, biological, and echocardiographic data to reveal pathophysiological differences.
Methods: During 7-22 April 2021, we recruited all consecutive patients admitted to ICCUs in 39 centers.
Article Synopsis
- Intensive cardiac care units (ICCU) in France have diversified from monitoring arrhythmias post-heart attack to treating various cardiac conditions, with acute coronary syndromes (ACS) being the most common cause for admission.
- A study of 1499 patients revealed that many have additional health concerns, with a significant portion requiring advanced therapies but a majority not needing invasive procedures.
- Overall, patients tend to have brief hospital stays with a low rate of major adverse events, although acute heart failure (AHF) patients face higher risks and resource use.
Aim: To evaluate the perspectives of parents and children on the impact of early childhood caries (ECC) on the oral health-related quality of life (OHQoF).
Materials And Methods: About 400 children aged 3-5 years were recruited for the study. About 200 children who were caries-free were the controls for the study.