Prospective Cohort of Acute Cardiology Referrals in an Ambulatory Day-Hospital Setting (CESAR)

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.