Multiomics, virtual reality and artificial intelligence in heart failure.

Abstract

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) andreduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometryand solid-phase microextractionvolatilomics in plasma and urine. HFrEF was defined using left ventricular (LV)global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve =0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r=-0.77, p<0.0001), while Echo AI-generated measurements correlated well with manually measured LVend diastolic volumer=0.77, LVend systolic volume r=0.8, LVEF r=0.71, indexed left atriumvolume r=0.71 and indexed LV mass r=0.6, p<0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF =0.88;95% CI: -0.03 to0.15;p=0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha2 of RR interval variability, p=1×10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.