Artificial intelligence-based characterization of multi-organ ultrasound congestion across the heart failure Spectrum

Aims: To investigate, using artificial intelligence (AI), the relationships between ultrasound (US)-defined systemic congestion and demographic, echocardiographic, and biohumoral parameters across the heart failure (HF) spectrum. Methods and results: A total of 1588 subjects (651 Stage A-B, 376 HF with reduced left ventricular ejection fraction [HFrEF, <50%], and 561 HF with preserved ejection fraction [HFpEF, ≥50%]) underwent comprehensive clinical evaluation, laboratory testing, echocardiography, and US assessment of congestion, including inferior vena cava (IVC), lung ultrasound (LUS), renal venous flow (RVF), portal venous flow (PVF), and hepatic venous flow (HVF). Assessment of IVC, LUS, and RVF was available in the entire cohort, whereas HVF and PVF were performed in 359 and 289 patients, respectively. Overall, 856 patients had no US signs of congestion, 458 had one US sign, and 274 had ≥2 US signs (multi-organ congestion). AI-based predictive models were developed for each site of congestion and for multi-organ congestion using a 3-item model (IVC, LUS, RVF). Congestion-related features clustered into four domains: medical history, biohumoral variables, left heart morphology and function, and right heart and pulmonary circulation. The 3-item model identified mitral annular systolic velocity, systolic and diastolic pulmonary artery pressure, triglycerides, left atrial volume index, diabetes, and treatment with furosemide or angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers as key predictors of multi-organ congestion (area under the curve = 0.79). Conclusion: AI-assisted integration of multi-organ US characterizes congestion as a multidimensional phenotype beyond conventional clinical assessment and biomarkers across the HF spectrum.