SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

Epidemiological Situation of High-Prevalence Non-Communicable Diseases in Spain: A Systematic Review

High-prevalence non-communicable diseases (HNCDs) are an ongoing global public health problem, posing a risk to the continuity of the 2030 Agenda for Sustainable Development. The aim of this study is to describe the current situation in Spain regarding certain HNCDs, namely, ischaemic heart disease, type 2 diabetes mellitus and colorectal cancer, including their prevalence and incidence in recent years. A systematic review was conducted between October 2022 and February 2023 using the MEDLINE, ProQuest and Scopus databases. After an exhaustive search, a total of thirty-four articles were included, comprising fourteen articles on colorectal cancer, seven on ischaemic heart disease and thirteen on diabetes mellitus type 2. The main topics included risk factors, lifestyles, mortality and incidence, the importance of screening and patient empowerment. On analysing each disease, it can be gleaned that risk factors and lifestyle impact the incidence, prevalence and mortality of the diseases studied. In addition, responsible human behaviour, associated with lifestyle factors, is related to the occurrence of these three diseases.

[Pericardial and myocardial involvement after SARS-CoV-2 infection: a cross-sectional descriptive study in healthcare workers]

Introduction and objectives The cardiac sequelae of SARS-CoV-2 infection are still poorly documented. We conducted a cross-sectional study in healthcare workers to report evidence of pericardial and myocardial involvement after SARS-CoV-2 infection.Methods We studied 139 healthcare workers with confirmed past SARS-CoV-2 infection. Participants underwent clinical assessment, electrocardiography, and laboratory tests, including immune cell profiling and cardiac magnetic resonance (CMR). Clinically suspected pericarditis was diagnosed when classic criteria were present and clinically suspected myocarditis was based on the combination of at least 2 CMR criteria.Results Median age was 52 (41-57) years, 71.9% were women, and 16.5% were previously hospitalized for COVID-19 pneumonia. On examination (10.4 [9.3-11.0] weeks after infection-like symptoms), participants showed hemodynamic stability. Chest pain, dyspnea or palpitations were present in 41.7% participants, electrocardiographic abnormalities in 49.6%, NT-proBNP elevation in 7.9%, troponin in 0.7%, and CMR abnormalities in 60.4%. A total of 30.9% participants met criteria for either pericarditis and/or myocarditis: isolated pericarditis was diagnosed in 5.8%, myopericarditis in 7.9%, and isolated myocarditis in 17.3%. Most participants (73.2%) showed altered immune cell counts in blood, particularly decreased eosinophil (27.3%; P < .001) and increased cytotoxic T cell numbers (17.3%; P < .001). Clinically suspected pericarditis was associated (P < .005) with particularly elevated cytotoxic T cells and decreased eosinophil counts, while participants diagnosed with clinically suspected myopericarditis or myocarditis had lower (P < .05) neutrophil counts, natural killer-cells, and plasma cells.Conclusions Pericardial and myocardial involvement with clinical stability are frequent after SARS-CoV-2 infection and are associated with specific immune cell profiles.

Pericardial and myocardial involvement after SARS-CoV-2 infection: a cross-sectional descriptive study in healthcare workers

Introduction and objectives

The cardiac sequelae of SARS-CoV-2 infection are still poorly documented. We conducted a cross-sectional study in healthcare workers to report evidence of pericardial and myocardial involvement after SARS-CoV-2 infection.

Methods

We studied 139 healthcare workers with confirmed past SARS-CoV-2 infection. Participants underwent clinical assessment, electrocardiography, and laboratory tests, including immune cell profiling and cardiac magnetic resonance (CMR). Clinically suspected pericarditis was diagnosed when classic criteria were present and clinically suspected myocarditis was based on the combination of at least 2 CMR criteria.

Results

Median age was 52 (41-57) years, 71.9% were women, and 16.5% were previously hospitalized for COVID-19 pneumonia. On examination (10.4 [9.3-11.0] weeks after infection-like symptoms), participants showed hemodynamic stability. Chest pain, dyspnea or palpitations were present in 41.7% participants, electrocardiographic abnormalities in 49.6%, NT-proBNP elevation in 7.9%, troponin in 0.7%, and CMR abnormalities in 60.4%. A total of 30.9% participants met criteria for either pericarditis and/or myocarditis: isolated pericarditis was diagnosed in 5.8%, myopericarditis in 7.9%, and isolated myocarditis in 17.3%. Most participants (73.2%) showed altered immune cell counts in blood, particularly decreased eosinophil (27.3%; P < .001) and increased cytotoxic T cell numbers (17.3%; P < .001). Clinically suspected pericarditis was associated (P < .005) with particularly elevated cytotoxic T cells and decreased eosinophil counts, while participants diagnosed with clinically suspected myopericarditis or myocarditis had lower (P < .05) neutrophil counts, natural killer-cells, and plasma cells.

Conclusions

Pericardial and myocardial involvement with clinical stability are frequent after SARS-CoV-2 infection and are associated with specific immune cell profiles.

The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology

In recent years, artificial intelligence (AI) has found numerous applications in cardiology due in part to large digitized datasets and the evolution of high performance computing. In the discipline of cardiac electrophysiology (EP), a number of clinical, imaging, and electrical waveform data are considered in the diagnosis, prognostication and management of arrhythmias, which lend themselves well to automation through AI. But equally relevant, AI offers a unique opportunity to discover novel EP concepts and improve clinical care through its inherent, hierarchical tenets of self-learning. This review will focus on the application of AI in clinical EP and summarize state-of-the art, large, clinical studies in the following key domains: (1) ECG-based arrhythmia and disease classification, (2) atrial fibrillation source detection, (3) substrate and risk assessment for atrial fibrillation and ventricular tachyarrhythmias, and (4) predicting outcomes after cardiac resynchronization therapy. Many are small, single-center, proof-of-concept investigations, but they still demonstrate groundbreaking performance of deep learning, a subdomain of AI, which surpasses traditional statistical analysis. Larger studies, for instance classifying arrhythmias from ECG recordings, have further provided external validation of their high accuracy. Ultimately, the performance of AI is dependent on the quality of the input data and the rigor of algorithm development. The field is still nascent and several barriers will need to be overcome, including prospective validation in large, well-labelled datasets and more seamless information technology-based data collection/integration, before AI can be adopted into broader clinical EP practice. This review will conclude with a discussion of these challenges and future work.

Deep learning and the electrocardiogram: review of the current state-of-the-art

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

Applications of Artificial Intelligence in Cardiology. The Future is Already Here

There is currently no other hot topic like the ability of current technology to develop capabilities similar to those of human beings, even in medicine. This ability to simulate the processes of human intelligence with computer systems is known as artificial intelligence (AI). This article aims to clarify the various terms that still sound foreign to us, such as AI, machine learning (ML), deep learning (DL), and big data. It also provides an in-depth description of the concept of AI and its types; the learning techniques and technology used by ML; cardiac imaging analysis with DL; and the contribution of this technological revolution to classical statistics, as well as its current limitations, legal aspects, and initial applications in cardiology. To do this, we conducted a detailed PubMed search on the evolution of original contributions on AI to the various areas of application in cardiology in the last 5 years and identified 673 research articles. We provide 19 detailed examples from distinct areas of cardiology that, by using AI, have shown diagnostic and therapeutic improvements, and which will aid understanding of ML and DL methodology.

A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, we developed a model of neurological outcome prediction for OHCA in Chicago, Illinois.

METHODS

Rescue workflow data of 2639 patients with witnessed OHCA were retrieved from Chicago's CARES. An Embedded Fully Convolutional Network (EFCN) classification model was selected to predict the patient outcome (survival with good neurological outcomes or not) based on 27 input features with the objective of maximizing the average class sensitivity. Using this model, sensitivity analysis of intervention variables such as bystander cardiopulmonary resuscitation (CPR), targeted temperature management, and coronary angiography was conducted.

RESULTS

The EFCN classification model has an average class sensitivity of 0.825. Sensitivity analysis of patient outcome shows that an additional 33 patients would have survived with good neurological outcome if they had received lay person CPR in addition to CPR by emergency medical services and 88 additional patients would have survived if they had received the coronary angiography intervention.

CONCLUSIONS

ML modeling of the complex Chicago OHCA rescue system can predict neurologic outcomes with a reasonable level of accuracy and can be used to support intervention decisions such as CPR or coronary angiography. The discriminative ability of this ML model requires validation in external cohorts to establish generalizability.