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) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography–mass spectrometry and solid-phase microextraction volatilomics 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 5 min, 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 LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume 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 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

“…Similarly, AI-based biomarker discovery using deep phenotyping with multiomics and analysis of digital electrocardiogram and data from wearable devices shows promising results in HF ( 50 ), left ventricular systolic dysfunction ( 51 , 52 ), and arrhythmia ( 53 ). Although reviewing specific applications of AI in cardiovascular contexts exceeds the scope of this review, several interesting review articles address this area ( 54 – 58 ). Giordano and colleagues discuss the efficacy and application of machine learning and AI in clinical decision making when developing personalized models of patient care ( 59 ).…”

Effects of canagliflozin on NT-proBNP stratified by left ventricular diastolic function in patients with type 2 diabetes and chronic heart failure: a sub analysis of the CANDLE trial

“…10 Department of Cardiovascular Medicine, Fukushima Medical University, Fukushima, Japan. 11 Department of Cardiovascular Medicine, Nagoya City University East Medical Center, Nagoya, Japan. 12 Department of Community Medicine for Cardiology, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Japan.…”

“…Recently, several researchers reported that advanced imaging can identify subtypes in which treatment was effective [8,9]. An advancing area in this research field is the application of artificial intelligence to carry out detailed phenotyping using multi-omics data, which enables stratification of early-stage diseases and assessment of prognosis [10,11]. Identification of effective subtypes of HF treatment is therefore an essential research topic for optimizing treatment of HF [12].…”

Application of Metabolomics to Identify Potential Biomarkers for the Early Diagnosis of Coronary Heart Disease

“…Metabolomics refer to global analyses of small molecule metabolites in a biological system ( Nicholson and Lindon, 2008 ). High-throughput metabolomics-based methods have been widely employed for screening novel biomarkers and elucidating the multiple targets and metabolic pathways of heart disease ( Jiang et al, 2020 ; Deidda et al, 2021 ; Gladding et al, 2021 ). Further, metabolic profiling provides integrative information on physiological as well as pathological changes ( Mamas et al, 2011 ; Johnson and Gonzalez, 2012 ).…”