Acute Coronary Syndromes, Arrhythmia, Heart Failure, HTN-Hypertension, Pulmonary Embolism, Deep Vein Thrombosis
Conditions
Keywords
Cardiovascular emergencies, Multimodality Imaging, Acute care, Prospective cohort, Biomarkers
Brief summary
Suspected acute or subacute cardiovascular diseases-including chest pain, dyspnea, and palpitations-are among the most common reasons for unscheduled emergency department visits and pre-hospital referrals. Despite this high clinical burden, the diagnostic yield is often limited, with a frequent mismatch between initial clinical suspicion and final diagnosis, contributing to substantial healthcare utilization and hospitalization rates. Current evidence is largely focused on specific conditions such as acute coronary syndromes, heart failure, arrhythmias, or pulmonary embolism, and rarely integrates the full spectrum of clinical, biological, and imaging data obtained during initial evaluation. To address this gap, we will establish a prospective cohort of all consecutive patients referred to the ambulatory day-hospital cardiology unit at Lariboisière University Hospital. This unit acts as a specialized downstream referral structure within the emergency care pathway, receiving patients after triage by emergency physicians, pre-hospital regulation services (SAMU), mobile intensive care units (SMUR), or emergency departments. Although it does not capture all suspected cardiovascular emergencies, it represents a selected real-world population deemed to require specialized acute cardiology assessment. The primary objective is to assess the frequency of cardiac conditions diagnosed in this cohort. Secondary objectives include characterization of patient profiles and diagnostic pathways; evaluation of the diagnostic and prognostic performance of clinical, biological, imaging, and multimodal parameters using final Heart Team diagnosis as reference; analysis of prior health history and healthcare utilization; and assessment of the medico-economic burden of suspected acute cardiovascular disease. The study will further support the development of a dedicated biobank and the validation of next-generation biomarkers, including AI-driven and voice-based markers, as well as decision-support algorithms for binary classification of cardiac involvement. Through integration of multimodal data and linkage with national health records, this approach aims to improve diagnostic accuracy, risk stratification, and understanding of the healthcare impact of acute cardiovascular presentations in a real-world setting.
Interventions
None listed
Sponsors
Assistance Publique - Hôpitaux de Paris
MIRACL.ai
INSERM UMR-S 942 MASCOT
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
\- The study will include all consecutive patients referred to ambulatory day-hospital unit for suspicion of acute or sub-acute cardiovascular disease at the Cardiology Department of Lariboisière University Hospital.
Exclusion criteria
* Patient unable to provide informed consent * Patient not affiliated to French social security
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Proportion of patients with confirmed cardiovascular diagnosis among consecutive patients consulting for suspicion of acute or sub-acute cardiovascular disease at the ambulatory day-hospital unit. | From enrollement to six months of follow-up. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Proportion referred to cardiac computed tomography (CCT) | From enrollement to six months of follow-up. | — |
| Proportion referred to cardiovascular magnetic resonance (CMR) | From enrollement to six months of follow-up. | — |
| Proportion referred to stress tests. | From enrollement to six months of follow-up. | — |
| Proportion referred to Holter monitoring or implantable loop recorders | From enrollement to six months of follow-up. | — |
| Proportion with introduction of heart failure (HF) therapy | From enrollement to six months of follow-up. | — |
| Proportion with introduction of anti-platelet therapy | From enrollement to six months of follow-up. | — |
| Proportion with introduction of anti-coagulant therapy | From enrollement to six months of follow-up. | — |
| Proportion referred to percutaneous coronary intervention (PCI) | From enrollement to six months of follow-up. | — |
| Proportion referred to cardiac surgery | From enrollement to six months of follow-up. | — |
| Proportion referred to electrical cardioversion | From enrollement to six months of follow-up. | — |
| Proportion referred to catheter ablation | From enrollement to six months of follow-up. | — |
| Proportion referred to pacemaker implantation | From enrollement to six months of follow-up. | — |
| Proportion referred to defibrillator implantation | From enrollement to six months of follow-up. | — |
| Diagnostic performance of each initial parameter with: sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve. | From enrollement to six months of follow-up. | — |
| Cost of care pathways initial and up to 2-5 years | From enrollement to five years of follow-up. | — |
| Prognostic value of baseline features to predict medical events annually up to 10 years of follow-up | From enrollement to ten years of follow-up. | The occurrence of: 1. All-cause mortality 2. Cardiovascular mortality 3. Sudden cardiac death 4. Hospitalization for any cardiovascular reason and duration of hospitalization 5. Hospitalization for HF 6. Myocardial infarction (MI) 7. Stroke 8. Incident atrial fibrillation (AF) 9. Cardiac syncope 10. Incident ventricular arrhythmia 11. Cardiac surgery 12. Coronary revascularization including PCI and coronary artery bypass grafting (CABG) m. Valvular percutaneous intervention (TAVI, TMVI, mitral or tricuspid clips...) n. Electrophysiological studies (catheter ablation, pacemaker / defibrillator implantation...) |
| Cost-effectiveness of the CESAR care pathway | From enrollement to five years of follow-up. | Cost-effectiveness of the CESAR care pathway compared with a propensity score-matched control population extracted from the French national health claims database. |
| F1 score of the artificial intelligence model for detecting cardiac involvement | From enrollement to six months of follow-up. | — |
| Prognostic value of voice-derived acoustic and speech parameters | At enrollement (D0). | The occurrence of: a. All-cause mortality b. Cardiovascular mortality c. Sudden cardiac death d. Hospitalization for any cardiovascular reason and duration of hospitalization |
| Number of sick leave | From enrollement to five years of follow-up. | — |
| Five-year event-free survival | From enrollement to five years of follow-up. | Event-free survival in patients managed through the CESAR care pathway compared with a propensity score-matched control population extracted from the French national health claims database. |
| Area under the precision-recall curve (PR-AUC) of the artificial intelligence model for detecting cardiac involvement | From enrollement to six months of follow-up. | — |
| Area under the receiver operating characteristic curve (ROC-AUC) of the artificial intelligence model for detecting cardiac involvement | From enrollement to six months of follow-up. | — |
| Duration of sick leave | From enrollement to five years of follow-up. | Cumulative duration of sick leave during follow-up in days. |
Contacts
CONTACTThéo PEZEL, MD, PhD
CONTACTJulien HUDELO, MD
Outcome results
None listed