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.
Study Type
OBSERVATIONAL
Enrollment
25,000
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.
Time frame: From enrollement to six months of follow-up.
Proportion referred to cardiac computed tomography (CCT)
Time frame: From enrollement to six months of follow-up.
Proportion referred to cardiovascular magnetic resonance (CMR)
Time frame: From enrollement to six months of follow-up.
Proportion referred to stress tests.
Time frame: From enrollement to six months of follow-up.
Proportion referred to Holter monitoring or implantable loop recorders
Time frame: From enrollement to six months of follow-up.
Proportion with introduction of heart failure (HF) therapy
Time frame: From enrollement to six months of follow-up.
Proportion with introduction of anti-platelet therapy
Time frame: From enrollement to six months of follow-up.
Proportion with introduction of anti-coagulant therapy
Time frame: From enrollement to six months of follow-up.
Proportion referred to percutaneous coronary intervention (PCI)
Time frame: From enrollement to six months of follow-up.
Proportion referred to cardiac surgery
Time frame: From enrollement to six months of follow-up.
Proportion referred to electrical cardioversion
Time frame: From enrollement to six months of follow-up.
Proportion referred to catheter ablation
Time frame: From enrollement to six months of follow-up.
Proportion referred to pacemaker implantation
Time frame: From enrollement to six months of follow-up.
Proportion referred to defibrillator implantation
Time frame: 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.
Time frame: From enrollement to six months of follow-up.
Cost of care pathways initial and up to 2-5 years
Time frame: 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
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...)
Time frame: From enrollement to ten years of follow-up.
Cost-effectiveness of the CESAR care pathway
Cost-effectiveness of the CESAR care pathway compared with a propensity score-matched control population extracted from the French national health claims database.
Time frame: From enrollement to five years of follow-up.
F1 score of the artificial intelligence model for detecting cardiac involvement
Time frame: From enrollement to six months of follow-up.
Prognostic value of voice-derived acoustic and speech parameters
The occurrence of: a. All-cause mortality b. Cardiovascular mortality c. Sudden cardiac death d. Hospitalization for any cardiovascular reason and duration of hospitalization
Time frame: At enrollement (D0).
Number of sick leave
Time frame: From enrollement to five years of follow-up.
Five-year event-free survival
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.
Time frame: From enrollement to five years of follow-up.
Area under the precision-recall curve (PR-AUC) of the artificial intelligence model for detecting cardiac involvement
Time frame: 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
Time frame: From enrollement to six months of follow-up.
Duration of sick leave
Cumulative duration of sick leave during follow-up in days.
Time frame: From enrollement to five years of follow-up.
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