Chest Pain

This is a randomised controlled trial to determine the effectiveness of Vericiguat to improve stress myocardial blood flow (MBF) and myocardial perfusion reserve as measured by cardiac magnetic resonance (CMR) imaging.

PAnDA-One is a prospective, multicenter, interventional study (10 centers, France) aimed at developing and validating a diagnostic support algorithm based on the ADx-One medical device, which non-invasively acquires thoracic vibrations using airborne ultrasound. The study will enroll 2,500 patients presenting to the emergency department with acute dyspnea or non-traumatic chest pain, divided into a development cohort (N = 1,500) and an independent test cohort (N = 1,000). The deep learning algorithm will be trained to discriminate cardiovascular from non-cardiovascular origins of symptoms, and its performance will be assessed by AUROC, sensitivity, and specificity against a final diagnosis established by an expert adjudication committee. Patient management will not be modified by study participation.

The objective of this study is to assess the safety and efficacy of the Freesolve resorbable magnesium scaffold (RMS) in the treatment of subjects with up to two de novo lesions in native coronary arteries compared to the Xience coronary drug-eluting stent (DES) system

The goal of the proposed project is to evaluate a mechanical intervention (sports bras designed specifically for full busted women) to alleviate neck, shoulder, arm, and back pain in full-busted women and investigate the contribution of non-mechanical pathways associated with this type of pain in women. Specifically, the investigators will investigate how sex-hormones, inflammation, and remapping of specific regions of the brain contribute to the manifestation of neck, shoulder, arm, and back pain in full-busted women across the lifespan.

This study investigates a new way to diagnose severe heart attacks in patients who have a specific electrical heart pattern called a Left Bundle Branch Block (LBBB). When patients present to the emergency department with chest pain, doctors routinely perform an electrocardiogram (ECG) to check for a heart attack. However, the presence of an LBBB can alter the heart's electrical signals on the ECG, effectively masking or hiding the typical signs of an ongoing acute coronary occlusion (a completely blocked artery). This making it highly challenging for emergency physicians to make an accurate and rapid diagnosis. The primary purpose of this prospective and observational research is to develop and evaluate an artificial intelligence/machine learning (ML) model that can analyze digital 12-lead ECG signals to accurately predict a true blocked coronary artery in patients with LBBB. The machine learning model will analyze raw digital ECG waveforms to detect subtle, microscopic patterns that might be missed by the human eye. To confirm the accuracy of the model, its predictions will be compared directly with invasive coronary angiography results, which is the gold standard reference method used to visualize blocked vessels. Additionally, the study aims to evaluate if the model can differentiate between a true heart attack caused by a blocked artery (Type 1 MI) and other non-occlusive conditions that cause elevated heart enzymes (Type 2 MI). Ultimately, the investigators intend to determine whether integrating this machine learning tool into emergency care can safely reduce the rate of unnecessary emergency invasive procedures for patients who do not have a true coronary blockage.

Improving Telephone Triage in Emergency Calls with AI The Coordinating Centre for Urgencies and Emergencies in Andalusia (CCUE) handles thousands of calls every day. Each call needs to be assessed based on the information given over the phone to determine how serious the case is. The reasons for calling range from minor health issues to life-threatening emergencies like cardiac arrest (CPA). This project focuses on improving telephone triage for four key emergency situations that often indicate severe or life-threatening conditions: Unconsciousness / Cardiac arrest Difficulty breathing Chest pain (non-traumatic, possible heart-related issues) Stroke symptoms Our goal is to make telephone triage more accurate and efficient by using advanced Artificial Intelligence (AI) techniques, including Machine Learning (ML) and Natural Language Processing (NLP). These tools will help CCUE operators make better and faster decisions, ensuring that patients receive the right care as quickly as possible. How it will be done: The investigators will analyze anonymized historical call data from the emergency coordination system (CCR) and digital clinical records (HCDM). This includes: Structured data: Predefined fields, such as answers to standard triage questions. Unstructured data: Free-text notes and other information recorded during the call. A hybrid AI approach will be used, combining: Traditional AI methods (supervised learning and deep learning) to classify cases. Generative AI techniques (advanced language models) to extract useful insights from free-text data. Building the Best Prediction Model To find the most effective AI model, we will test different machine learning techniques, including: Decision Trees Random Forests Support Vector Machines (SVM) XGBoost Ensemble methods Neural Networks We will also analyze which questions and variables are the most important in predicting the severity of a case. Based on this, we will suggest improvements to the current triage questions to enhance accuracy. Measuring Success We will evaluate the AI model using key performance metrics, including: Accuracy (overall correctness) Sensitivity (ability to detect real emergencies) Specificity (ability to avoid false alarms) False Positive \& False Negative Rates (how often the system makes mistakes) Likelihood Ratios (how well the system distinguishes between urgent and non-urgent cases) F1-Score \& ROC Curve (overall performance indicators) Why This Matters This project will assess how effective the current telephone triage system is and develop a new AI-powered model to improve it. The goal is to help emergency operators quickly identify the most serious cases, reducing response times and improving patient outcomes. In the future, the investigators aim to integrate this improved AI model into the CCUE system to enhance emergency response across Andalusia.

Chest pain is a commonly encountered presentation in the emergency department (ED), with an incidence ranging from 5 to 12%. Chest pain has a wide range of aetiologies from the mildest to the most severe. In benign forms these pains can be musculoskeletal, psychogenic or even indeterminate (\>60% depending on the place of recruitment). Coronary aetiology represents 12 to 25% of severe forms. The management of chest pain in the emergency department is part of the daily life of nurses. According to the french 2020 Nurse Reception Organizer (IOA) guidelines, a nurse who has been trained for this role is required to "triage" (severity grade and orientation) patients presenting for this reason. The presence of specialized nurses in the management of chest pain allows faster initial management. To date, no study has been conducted to assess the diagnostic and treatment performance of a nursing pathway for the management of patients presenting with acute chest pain regardless of the suspected diagnosis and not only acute coronary syndrome.

The goal of this prospective multicenter observational study is to learn whether an artificial intelligence model based on electrocardiograms (ECGs) can help diagnose acute type A aortic dissection (TAAD) in adults who come to the emergency department with chest pain or related symptoms. The main question it aims to answer is: Can the AI-ECG model accurately distinguish TAAD from other causes of chest pain in a real-world emergency setting? Researchers will compare the AI model's ECG-based predictions with the final diagnosis confirmed by computed tomographic angiography (CTA), which is the reference standard. Participants will undergo routine emergency ECG testing and subsequent diagnostic evaluation as part of standard care. Clinical and ECG data will be collected from five tertiary hospitals, and the model's diagnostic performance will be assessed across centers.

The goal of this study is to determine if rapid outpatient evaluation vs hospitalization management is the best strategy (based on patient-centered measures and safe, equitable, and efficient resource use) for evaluating patients with acute chest pain who are at moderate risk for acute coronary syndrome (ACS). Patients will be randomized in the Emergency Department to either an outpatient evaluation (CARE-CP) or hospitalization evaluation for their symptoms.

This study aims to investigate the guiding value of coronary CTA combined with CT-FFR in diagnostic and treatment decision-making for emergency chest pain patients at moderate risk, as well as its impact on clinical outcomes. Through a prospective multicenter randomized controlled trial, this research compares the preventive effects of early application of this technology versus standard care on major adverse cardiovascular and cerebrovascular events (MACCE), with the goal of optimizing the diagnostic and treatment processes for emergency chest pain patients.

The objective of this observational, prospective study is to determine the prevalence of Cardiac Amyloidosis (CA) in males over the age of 65 who experience chest pain but show no signs of coronary artery disease (CAD). Prior to inclusion, all patients will have undergone a CT coronary angiogram or an Rb-PET scan to rule out the possibility of CAD. Participants will be subject to several examinations, including blood tests, urine samples, ECG, echocardiography, and bone scintigraphy. An endomyocardial biopsy may be conducted if necessary.

The purpose of this study is to determine if virtual reality (VR) will improve symptoms in non-cardiac chest pain (NCCP).

Acute non-traumatic chest pain is one of the common causes of presentation in emergency patients, but the causes of acute non-traumatic chest pain are complex, the severity of the condition varies greatly, and the specificity of symptoms is not high. Machine learning and intelligent auxiliary models can greatly shorten the time of clinical decision-making, and improve the accuracy of etiological diagnosis in patients with chest pain, reduce the rate of misdiagnosis and missed diagnosis, and provide a clear direction for further treatment.

Acute chest pain is a prevalent medical emergency in primary emergency care settings. Triage of chest pain prior to hospital admission presents significant challenges due to the absence of sufficiently sensitive diagnostic tools. Clinical signs, symptoms, risk assessment scores, or a normal electrocardiogram (ECG) can reliably exclude acute myocardial infarction (MI). This diagnostic uncertainty has resulted in chest pain being the second most common cause for acute hospital referrals from Norwegian emergency primary care, even though chest pain is frequently non-cardiac in origin. In acute MI events, cardiac troponins are released into the bloodstream from the damaged myocardium, where low values are used to exclude MI. Until recently, such testing has necessitated using high-sensitivity cardiac troponin (hs-cTn) assays, which have been limited to hospital laboratories. However, recent technological advancements in point-of-care (POC) testing allow access to whole-blood assays that meet high-sensitivity criteria. In this upcoming project, the investigators will evaluate the implementation of a whole-blood POC assay (QuidelOrtho TriageTrue hs-cTnI) across six Norwegian emergency primary care clinics. The study plans to enrol 2,500 patients over a period of 1.5 years. The clinical performance of the novel strategy will be investigated, as well as its impact on healthcare utilization and hospital referrals compared to standard care. Additionally, the investigators will assess the prevalence of persistent chest pain and its effects on quality of life, alongside psychological stress and anxiety, through validated questionnaires. This project aims to offer better and more comprehensive management of the large group of emergency primary care patients with acute chest pain, contributing to reduced hospital referrals, improved quality of life, and more sustainable use of healthcare services.

" This prospective, pragmatic, randomized controlled trial is designed to evaluate the impact of an artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) interpretation tool (Angiomics) on emergency physicians' diagnostic performance and clinical decision-making in patients presenting with acute chest pain. CCTA is a critical diagnostic modality for suspected acute coronary syndrome (ACS) in the emergency department (ED). Accurate interpretation often requires experienced radiologists, who may not always be available, particularly during off-hours. The introduction of AI-based interpretation tools into clinical workflow has the potential to enhance diagnostic accuracy, increase physician confidence, reduce delays in decision-making, and improve efficiency of resource utilization. However, evidence regarding the real-world effectiveness of such AI tools in the ED setting remains limited. Eligible participants will include adults aged 18 years or older presenting to the ED with chest pain and classified as intermediate risk (HEART score 4-6). Participants will be randomized into two groups: (1) AI-assisted CCTA interpretation, in which emergency physicians interpret scans with access to AI results; and (2) standard interpretation, in which emergency physicians interpret CCTA without AI support. In both groups, physicians will document the presence of stenosis in the four major coronary arteries (LM, LAD, LCX, RCA) and report diagnostic confidence on a 5-point Likert scale. The primary outcome is the negative predictive value (NPV) of CCTA interpretation at the patient level, comparing AI-assisted versus standard interpretations against the reference standard of blinded consensus readings by board-certified radiologists. Secondary outcomes include sensitivity, specificity, positive predictive value (PPV), accuracy, diagnostic confidence, vessel-level diagnostic performance, and agreement with radiologist consensus using Cohen's Kappa. The study aims to enroll approximately 530 participants (276 in the control arm and 254 in the intervention arm, accounting for an expected 10% dropout). Enrollment and follow-up will be conducted at Severance Hospital and Gangnam Severance Hospital over a 24-month period following IRB approval. The results are expected to provide evidence for the clinical utility and effectiveness of AI-based CCTA interpretation in the ED and to guide integration of AI into emergency care in order to optimize patient outcomes and healthcare efficiency.

The primary objective of this study is to determine whether a reduced radiation protocol (RRP) in which angiograms are acquired at ultralow radiation doses and then processed using spatiotemporal enhancement software can produce similar quality angiographic images as compared with standard techniques.

Magnetocardiography (MCG) is a promising noninvasive and accurate method for detecting myocardial infarction. Although progress has been made in this area, there is a lack of studies using up-to-date examination instruments for the calibration of MCG analysis. This is a prospective single-center study aiming to build accurate analytical models of MCG to detect myocardial infarction. Myocardial infarction are diagnosed by electrocardiogram, biomarkers (high-sensitivity cardiac troponin, etc), or non-invasive imaging (cardiac magnetic resonance or single-photon-emission tomography). Myocardial infarction is also quantified by cardiac magnetic resonance or single-photon-emission tomography. Healthy volunteers and chest pain patients who will receive electrocardiogram, biomarkers (high-sensitivity cardiac troponin, etc), or non-invasive imaging (cardiac magnetic resonance or single-photon-emission tomography) examination will be enrolled in this study.

Chest pain represents a common reason for consultation to emergency room. This symptom can be explained by a broad spectrum of conditions, from benign musculoskeletal or esophageal pain to life-threatening disease such as aortic dissection, pulmonary embolism, and myocardial infarction. There are already different diagnostic tools to quickly identify most dangerous diseases, for example electrocardiogram, blood samples with specific markers of cardiac injury, chest X-ray and echography. In case a doubt of disease is raised after the first clinical evaluation, it is possible to proceed with more complex, expensive and invasive examinations, namely a computed tomography (CT) scan or an invasive coronary angiography (ICA). CT scan allows the diagnosis of various conditions such as pleural, pulmonary, pericardial and vascular disease such as pneumonia, pneumothorax, pleural and pericardial fluid, pulmonary embolism (PE), acute aortic dissection (AOD). In order to see the pulmonary, aortic and coronary arteries, a contrast injection is needed. Moreover, since the heart and the aortic root are continuously moving, specific technical measures to obtain good quality images are needed. Recently, a new CT scan system has been developed. It allows to obtain good quality images of the heart and aortic root using an estimated heart rhythm, without ECG-gating. This allows to perform a CT scan of the heart in a reduced amount of time, and without need for controlling heart rate. Moreover, it is possible to obtain information on both aortic, coronary, and pulmonary artery with the same contrast injection. This may be of great interest in the context of patients presenting at the emergency room with chest pain and with a suspicion of pulmonary embolism, myocardial infarction, or aortic dissection, since with a single fast exam it is possible to rule out all these conditions. Coronary arteries are very small vessels, and the accuracy of this new technique in identifying a significant obstruction is still to be proved. At present, patients with chest pain and a suspicion of myocardial infarction undergo an invasive coronary angiography. If this new tool proves to be reliable, it will be possible to reduce the number of useless invasive examination in patients in which the presence of coronary artery disease (CAD) is ruled-out. In addition, the CT scan can help quickly and effectively plan treatment when worrying abnormalities are detected in the coronary arteries that are associated with a higher risk. Therefore, this clinical trial (further on referred to as "trial") will evaluate the investigational medicinal product (IMP), ECG-less Revolution Apex Elite system (GE Healthcare, Waukesha, WI -USA) for the diagnosis of coronary artery disease. The purpose of this trial is to learn about: the accuracy of this new CT system compared to the gold standard invasive coronary angiography in diagnosing coronary artery disease. The number of patients receiving an alternative diagnosis such as pulmonary embolism, aortic dissection, pulmonary, pleural or pericardial disease will be evaluated. Finally, the prognostic predictive value of the CT compared with ICA, in predicting myocardial infarction, coronary revascularization, and cardiac death at a follow-up of 18 months will be assessed. Patient will undergo a computer tomography examination with this new technique, evaluating both pulmonary, aortic, and coronary arteries. Then, as indicated by current guidelines, they will undergo an invasive coronary angiography.

A retrospective, observational study consisting of patients who presents with typical/atypical chest pain and have an ensuing negative ischemic evaluation

Patients with angina and non-obstructive CAD are common within clinical practice, but remain a challenge with regard to diagnosis and treatment. When these patients undergo a comprehensive evaluation at the time of invasive coronary angiography, occult coronary abnormalities are frequently found. We hope to learn the overall prevalence and presentation of these occult coronary abnormalities and its long term outcome in this patient population.

This prospective study aims to compare functional abnormalities detected using myocardial perfusion SPECT imaging (MPI SPECT) with the extent and severity of anatomical findings on coronary computed tomography angiography (coronary CTA). Additionally, the investigators aim to enhance the diagnostic value of MPI SPECT by quantifying myocardial blood flow and utilizing myocardial flow reserve calculated from dynamic SPECT images. 50 patients with suspected coronary artery disease are anticipated to be enrolled. Pharmacological stress and rest-phase dynamic and static MPI SPECT following an additional coronary CTA scan are to be performed. The obtained multimodality imaging data (functional and anatomical parameters) are planned to be compared and subjected to statistical analysis. The results of this study are expected to improve risk assessment for patients with moderate cardiovascular risk and enhance the diagnostic performance of MPI SPECT.

The lifetime prevalence of chest pain in the general population is 20-40%. The etiologies to be evoked from the outset of management are those of cardiovascular origin, such as acute coronary syndrome (ACS) and pulmonary embolism. ACS is responsible for almost 20% of deaths. Delay in treatment is a major prognostic factor, given the importance of coronary reperfusion. In France, one of the first contacts with the healthcare system is the medical regulation assistant (MRA) at the Centre 15. His or her role is to prioritize the call according to the identification of immediate signs of seriousness, and if necessary, to decide autonomously to send a rescue team before medical regulation. Depending on the reason for the call and any signs of seriousness, it prioritizes the call according to the expected response time. In line with current recommendations, all calls for chest pain should be answered by an emergency medical dispatcher (EMR) within 5 minutes. However, 60-90% of chest pain calls are not of cardiovascular origin. Their prioritization could therefore be re-qualified for longer response times. Given the frequency of this type of call, a more efficient MRA referral strategy is needed. To achieve this, decision-support tools would be essential. The performance of the HAR (History, Age and Risk Factors) score has been recently explored, derived from the HEART score, in a previous single-center prospective study in 2019. It stratifies the risk of a major cardiovascular event (MCE) into low (0 or 1 point), intermediate (2 or 3 points) or high (4, 5 or 6 points). Investigator's hypothesis is that the HAR score could be entrusted to MRA, to enable them to optimize the prioritization of patients calling with non-traumatic chest pain, by qualifying low-risk chest pain calls on the one hand, which could be prioritized in P2 SNP, and high-risk calls on the other, making it possible to anticipate the dispatch of an emergency service.

Cardiovascular disease is the most severe cause of death among all non-infectious diseases and accountable for 17.5 million deaths in the world. Heart mortality in patients with DM is frequently associated with prevalence silent ischemia, which is triggered by autonomic neuropathy, thereby decreasing the chest pain felt by them

This is a prospective observational study carried out in the emergency department of Emile Roux Hospital. Its aim is to compare the medical, psychological and social characteristics of patients followed up by a general practitioner and those without regular follow-up, using an anonymous questionnaire completed by the general practitioner in the emergency department, for patients consulting for the reason "chest pain".

Chest pain accounts for 10-20 percent of all emergency department visits. The stratification of chest pain is always a challenge. Electrocardiograms (ECG) have been used in clinical practice for 100 years, which is too important to be replaced due to its advantages of non-invasive, simple, rapid and inexpensive. ECG contains numerous signals derived from depolarization and repolarization of cardiomyocytes. However, the interpretation of ECG hasn't improved much in a hundred years. Based on determine-learning, Cong W's team developed an technique called "cardiodynamicsgram (CDG)", which is an outstanding method to identify myocardial ischemia. This study will further investigate the accuracy of CDG in stratification of patients with chest pain in Emergency department.