Clinical Research Directory

Virtual Reality for Non-cardiac Chest Pain

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

AI-ECG Detection of Occlusion Myocardial Infarction at Emergency Department Triage (TRIACS)

Chest pain is among the most common reasons people attend an emergency department, and the electrocardiogram (ECG) recorded at triage is the first test used to decide who needs urgent treatment for a blocked coronary artery. Recognising the ECG patterns that indicate an acutely blocked artery is difficult. Many such patterns do not produce the classic ST-segment elevation, and identifying them reliably requires a level of ECG expertise that is not always available at the point of triage - particularly at night and among clinicians who are not cardiologists. Artificial intelligence (AI) models that read the 12-lead ECG have been shown in several retrospective studies, mainly from Western Europe and the United States, to identify acute coronary occlusion more sensitively than conventional interpretation. These models are now in clinical use in a number of health systems. Their performance has not been assessed in a prospectively enrolled, unselected Slovenian emergency department population, and diagnostic accuracy commonly declines when a model is applied outside the setting in which it was developed. TRIACS is a prospective, single-centre, observational external validation study in the Emergency Department of University Medical Centre Maribor. All consecutive adults presenting with symptoms suggestive of acute coronary syndrome who have a 12-lead ECG recorded at triage as part of routine care will be enrolled. Each triage ECG is analysed by the AI model. The model's output is withheld from all treating clinicians and plays no part in patient care; every patient receives standard guideline-directed management. The main aim is to measure how accurately the AI model identifies angiographically confirmed occlusion myocardial infarction in this population, and to compare its accuracy with the triage decision actually made by the clinical team. The study is therefore designed to inform an adoption decision in either direction, rather than to demonstrate a predetermined conclusion.

Improving the Accuracy of Referrals of Patients With Chest Pain

This is a multicenter, prospective, investigator-initiated, randomized controlled trial aiming to reduce the percentage of non-cardiac chest pain (NCCP) patients admitted to the cardiac emergency department (ED) by performing the modified HEART score by emergency medical transport (EMT) personnel.

AI-based Decision Support in Acute Chest Pain

The AIDE-CP1 trial will test whether a decision support tool based on artificial intelligence (AI) can improve care for patients who come to an emergency department with chest pain. The AI tool uses the patient's age, sex, first blood tests and ECG to estimate the risk of a heart attack within 30 days and suggests what to do next. About 4,500 patients at five Swedish emergency departments will be included. The study will examine whether the AI tool can shorten time spent in the emergency department while maintaining patient safety. It will also study healthcare use, costs, and how AI affects doctors' decisions.

CARE-CP (Testing a Cardiovascular Ambulatory Rapid Evaluation for Patients With Chest Pain)

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.

Silent Deployment and Prospective Evaluation of Zhunxin Agent for Emergency Chest Pain Assessment

The goal of this prospective, multicenter observational study is to evaluate how well Zhunxin Agent, an artificial intelligence system for chest pain assessment, performs in real-world emergency department settings. The study will include people who receive routine care for acute chest pain at participating hospitals. The main questions this study aims to answer are: 1. How accurately can Zhunxin Agent identify serious causes of acute chest pain during routine emergency care? 2. How well can Zhunxin Agent distinguish people at different levels of clinical risk? Zhunxin Agent will run in silent mode using clinical information collected during routine care. Its predictions will not be shown to treating clinicians and will not affect clinical decisions, diagnostic tests, or treatment. Researchers will compare the predictions made by Zhunxin Agent with the final clinical diagnoses established through routine medical care. Participants will: 1. Receive usual emergency care according to local clinical practice 2. Have routinely collected clinical data analyzed by Zhunxin Agent in the background 3. Receive no additional treatment or clinical intervention based on the Agent's predictions

"Discovery2" Study of Using MCG at Emergency Departments

The purpose of this research is to collect the magnetic signals from the heart to identify features that may help Emergency Department (ED) doctors differentiate high and low risk patients for heart related chest pain.

Validation of the HAR Score for Prioritization of Patients Calling the Emergency Medical Service for Chest Pain by Emergency Call Dispatcher

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.

Safety and Clinical Performance of the Freesolve Resorbable Magnesium Scaffold (RMS) System in Subjects With Coronary Artery Lesions

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

SMARTwatch for the Diagnosis of ST-segment Elevation Myocardial Infarction in Patients With Chest Pain

The main symptom that initiates the diagnostic and therapeutic process for patients with suspected acute coronary syndrome is chest pain. Based on a standard 12-lead electrocardiogram (ECG), patients can be diagnosed with ST-segment elevation myocardial infarction (STEMI), which requires immediate medical treatment. Recently, wearable devices like smartwatches with ECG capabilities have opened new pathways for cardiac triage, but their diagnostic precision and technical viability in a real, unselected Emergency Department setting need to be confirmed. The primary objective of this prospective, observational study is to compare the diagnostic capacity of a 9-lead ECG obtained with a smartwatch against the standard 12-lead ECG for diagnosing STEMI in patients consulting for chest pain. During the study, patients evaluated by the on-call cardiologist for ischemic chest pain will undergo their standard care. In addition to the standard 12-lead ECG, a 9-lead ECG will be sequentially recorded using a smartwatch. This is an observational study, meaning the smartwatch recording will not cause any delay in clinical action or negatively impact the patient's standard treatment. Specifically, the study aims to: * Analyze the diagnostic agreement between expert cardiologists when blindly and independently classifying the smartwatch recordings versus the standard ECG recordings as "STEMI" or "non-STEMI". * Evaluate the technical feasibility of using the smartwatch in an Emergency Department setting by assessing the percentage of unreadable records due to artifacts.

The Application of Large Language Model in Emergency Chest Pain Triage

This study will evaluate the accuracy and efficiency of large language model in emergency triage.

Copeptin and Acute Coronary Syndrome Without ST-segment Elevation

The purpose of this study is to determine whether the combination of copeptine and troponin adds diagnosis information to that provided by troponin alone or the combination of troponin and myoglobin. Hypothesis : If the values of troponin and copeptin are not elevated, diagnosis of acute myocardial infarction can be ruled out without prolonged monitoring and serial blood sampling.

ER-VISION-AI Study

Prospective, multicenter, randomized, open-label, blinded-endpoint (PROBE-like) clinical trial evaluating whether physician-supervised Generative Pre-trained Transformer (GPT)-assisted multimodal diagnostic support improves diagnostic concordance in emergency department patients presenting with acute cardiopulmonary symptoms.

One-hoUr Troponin Using a High-sensitivity Point-Of-Care Assay in Emergency Primary Care

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.

Osteopathic Treatment in Adult Patients With Cystic Fibrosis

To study the contribution of osteopathy on the reduction of pain in adult patients with cystic fibrosis

Vericiguat in Patients With Coronary Microvascular Dysfunction Causing Stable Chest Pain (V-COM)

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.

Risk Factors, Costs, and Impacts of ED Boarding

The goal of this observational study is to learn about the risk factors, costs, and operational impacts of emergency department boarding (patients admitted to the hospital but remaining in the emergency department awaiting placement on an inpatient floor) The main questions it aims to answer is: 1. What characteristics of patients make them more likely to experience ED boarding? 2. What is the impact of ED boarding on costs of health care? 3. How do high-boarding environments affect the clinical care of all patients in the emergency department, including those that do not board themselves. Data will be secondary in nature, collected in the regular Participants already taking intervention A as part of their regular medical care for RA will answer online survey questions about their joint pain for 5 years.

Contactless Ultrasound Data Acquisition in Emergency Departments to Discriminate the Origin of Dyspnea and Chest Pain

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.

Machine Learning for Diagnosis of Occlusive MI in LBBB Patients

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.

Hormones, Outcomes, and Pain Pathways in Exercise Study

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.

Evaluation and Optimization of Telephone Triage Using Artificial Intelligence (AI) Models for the Detection of Demands for Time-dependent Pathology at the Emergency and Urgent Care Coordination Center (CCUE).

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.

Intraoperative Echocardiography in Low-Risk CABG Surgery

This goal of this study is to better understand when and where intraoperative transesophageal echocardiography (TEE) should (or should not) be used during coronary artery bypass graft (CABG) surgeries.

Chest Pain Nurse Track

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.

Triage and Recognition of Acute Aortic Dissection in Chest Pain by Electrocardiogram-Artificial Intelligence

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.