nakisa-multiomicsvirtual-inpress-2021.pdf (2.34 MB)
Multiomics, virtual reality and artificial intelligence in heart failure
journal contribution
posted on 2021-11-01, 00:00 authored by P A Gladding, S Loader, K Smith, E Zarate, S Green, S Villas-Boas, P Shepherd, P Kakadiya, W Hewitt, E Thorstensen, C Keven, M Coe, Bahareh NakisaBahareh Nakisa, T Vuong, M N Rastgoo, M Jüllig, V Starc, T T SchlegelAim: 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.
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Journal
Future CardiologyVolume
17Issue
8Pagination
1335 - 1347Publisher
FUTURE MEDICINE LTDLocation
EnglandPublisher DOI
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1479-6678eISSN
1744-8298Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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