Electrocardiogram

This pilot study is designed to evaluate the clinical performance and utility of the SmartECG-EF software in patients undergoing evaluations at Inha University Hospital. The primary objective is to assess how effectively the device assists clinicians in evaluating cardiac function and related clinical parameters. The study will prospectively enroll eligible participants to gather real-world clinical data, contributing to enhanced diagnostic accuracy and patient management.

The aim of this study is to develop, implement, and evaluate the effects of a virtual ECG rhythm intervention on the interpretation and competence of cardiology nurses in Finnish university hospitals. The virtual ECG rhythm intervention consists of a virtual animation component and a virtual simulation component, both incorporating cardiological patient cases with corresponding ECG rhythms. The virtual animation includes educational content on a total of 31 different ECG rhythms, as well as a virtual simulation containing 6-8 different cardiological patient cases with ECG rhythms (covering 11 distinct rhythms). The study employs a quasi-experimental design. Cardiology nurses are divided into two groups based on hospital: an intervention group and a control group. In this study, data will first be collected for the virtual animation component during 2026-2027, followed by data collection for the virtual simulation component during 2027-2028. A baseline measurement of ECG interpretation and competence will be conducted for both groups (n = 508) using an online survey (LimeSurvey) prior to the intervention. The same measurement schedule will be applied to the virtual simulation component during the 2027-2028 data collection period. In this study, three (3) reminder letters will be sent at weekly intervals to encourage participation in the study, its surveys, and the virtual interventions. These reminder messages will be distributed via email by unit managers or equivalent personnel in each hospital to the participants. The virtual intervention will be delivered online via a web service (GitHub) link. Research data on ECG interpretation and competence will be collected using an ECG interpretation measurement instrument administered through an electronic survey (LimeSurvey). The ECG interpretation instrument was previously developed by the researcher and includes ECG recordings from real patient cases, for which prior permission has been obtained in accordance with the practices of a cardiac care unit in a central hospital. The instrument has been partially updated in terms of ECG rhythms, with additional new ECG recordings included. Permission for the new ECG recordings was obtained in April 2023 from a Finnish wellbeing services county. The instrument also includes ECG recordings from a simulation monitor display. The ECG interpretation instrument begins with 14 background questions. It includes five questions assessing ECG competence and currently contains 31 ECG interpretation questions, compared to the previous 21 items. Additionally, the end of the instrument includes seven questions assessing the experiences of participants in the intervention group regarding the virtual intervention. These implementation methods aim to control for the potential influence of external variables, as the intervention and survey are identical for all participants. This consistency could not be guaranteed if the virtual ECG interpretation intervention were delivered, for example, as a traditional theory-based lecture. The results of the study will be analyzed using statistical methods. The findings can be utilized, for example, in the development of ECG rhythm interpretation and competence training programs for cardiology nurses.

This trial will prospectively evaluate the impact of integrating AI-ECG within the pay-for-performance program on improving the diagnosis, treatment, and clinical outcomes of occlusion myocardial infarction patients by promoting accurate and timely diagnoses through financial incentives.

Our study aimed to use an AF-predict AI-ECG alert system to help physicians identify patients who need to wear a continuous cardiac rhythm monitor for new diagnoses of atrial fibrillation (AF), atrial flutter (AFL), or atrial arrhythmia with high AF risk, including premature atrial complexes (PAC) ≥ 500/24hr, burst PACs \> 20 beats, non-sustained AF/AFL.

This study will evaluate the performance of specialist physicians in interpreting normal electrocardiograms (ECGs) with and without the assistance of an artificial intelligence (AI) neural network. The primary aim is to determine whether AI support affects the rate of false-positive interpretations of normal tracings. Secondary aims include evaluating the time required for interpretation, the sensitivity for detecting abnormalities, and the effect on false positives in ECGs with major abnormalities according to the Minnesota Code system. All ECGs in the sample will be reviewed by a panel of three specialists, to determine the reference classification.

Atrial fibrillation (AF) is an irregular and often very fast heart rhythm, is considered the most common sustained cardiac arrhythmia in adults worldwide, and its incidence and prevalence are increasing. Currently, the estimated prevalence of AF in adults is 2-4%, and is projected to increase 2.3-fold. AF is associated with increased morbimortality and other comorbidities (hypertension diabetes etc.) which places a significant burden on the patient himself, social health and also on health and social care expenditure. The European Society of Cardiology proposes an integrated ABC model (A: Anticoagulation, B: main symptom management, C: optimization of comorbidities and cardiovascular) and within this model, catheter ablation (B) is considered one of the main treatments to control AF symptoms; physical activity (C) is considered one of the modifiable health risk factors and is considered within a lifestyle intervention together with weight loss. Catheter ablation of AF is currently the treatment of choice for paroxysmal AF. It uses small burns or frostbite to cause some scarring inside the heart to help interrupt the electrical signals that cause the irregular heartbeat. It is a safe procedure that has been shown to be more effective than treatment with antiarrhythmic drugs in reducing the arrhythmic burden and, therefore, the morbidity and mortality associated with the pathology. Many studies have demonstrated the beneficial effects of moderate physical activity and physical exercise on cardiovascular health. However, there is still controversy as to whether physical activity is associated with an increased risk of AF in the general population; while some studies report a decreased risk of AF, others suggest an increase or that there is no evidence of an association between AF and physical activity. Few studies have yet focused on the effects of physical activity in those subjects who have undergone catheter ablation. Studies that have evaluated physical activity with questionnaires associate it, when of moderate or high intensity, with lower recurrence of AF and lower incidence of serious events. It is true that the practice of regular and controlled physical exercise is a recognized part of the comprehensive care of patients with coronary heart disease (patients whose heart has difficulty receiving blood), and exercise is systematically identified as a central element of their rehabilitation. However, to date there is no similar approach for AF ablation patients. Given the current situation of the subject of interest, the main objective of this project is to study the influence of a physical exercise program in patients undergoing catheter ablation of AF on different morphological and physiological variables of the heart, levels of physical activity and quality of life of patients. Investigators intend to recruit 120 participants, who will be randomly and equally distributed into a group that will perform a physical exercise intervention and a control group that will not perform any type of intervention. Participation in the study will not disrupt the normal practice of the health care system with these patients.

The study aims to identify new diagnostic and prognostic markers for CMP that can help predict disease progression. In particular, the study will focus on microRNAs (miRNAs) and spatial transcriptomics, which are emerging techniques that may provide insights into the underlying disease mechanisms. By understanding these markers, the investigators hope to improve the way the investigators diagnose and manage CMP, particularly in terms of predicting progression to heart failure or heart transplantation. The study will evaluate patients with hypertrophic cardiomyopathy (e.g., sarcomeric forms, Anderson-Fabry disease, AL, and TTR cardiac amyloidosis), dilated cardiomyopathy and arrhythmogenic cardiomyopathy. These patients will undergo clinical evaluations, including ECG, echocardiograms, CMR, biopsy analysis, and genetic testing, as well as molecular studies such as transcriptomics and miRNA analysis. This comprehensive approach aims to identify potential new biomarkers for diagnosing and predicting the disease course.

Electrical conduction in the heart produces a heartbeat; electrical signals are sent to the heart muscle upon each contraction, causing the heart to pump blood. These electrical impulses are recorded by an electrocardiogram (ECG), which makes it possible to evaluate electrical conduction and cardiac rhythm. ECG is a non-invasive technique used to assess pacemaker function and perioperative heart health. Ten electrodes are used in the 12-lead ECG, the gold standard for diagnosing cardiac illness and anatomy, to record the electrical activity of the heart from various perspectives. With wireless data transmission to the cloud, this technology is now included into emergency and ambulance situations, streamlining processes and improving signal quality. The purpose of this observational study is to examine 12-lead electrocardiogram data for better clinical application and diagnosis.

The goal of this observationl study is to evaluate the possibility of building a Deep Learning (DL) model capable of analyzing electrocardiographic traces of athletes and providing information in the form of a probability stratification of cardiovascular disease. Researchers will enroll a training cohort of 455 participants, evaluated following standard clinical practice for eligibility in competitive sports. The response of the clinical evaluation and ECG traces will be recorded to build a DL model. Researchers will subsequently enroll a validation cohort of 76 participants. ECG traces will be analyzed to evaluate the accuracy of the model to discriminate participants cleared for sports eligibility versus participants who need further medical tests