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Gladding PA, Loader S, Smith K, Zarate E, Green S, Villas-Boas S, Shepherd P, Kakadiya P, Hewitt W, Thorstensen E, Keven C, Coe M, Nakisa B, Vuong T, Rastgoo MN, Jüllig M, Starc V, Schlegel TT. Multiomics, virtual reality and artificial intelligence in heart failure. Future Cardiol 2021; 17:1335-1347. [PMID: 34008412 DOI: 10.2217/fca-2020-0225] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
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.
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Affiliation(s)
- Patrick A Gladding
- Department of Cardiology, Waitemata District Health Board, Auckland 0620, New Zealand
| | - Suzanne Loader
- Department of Cardiology, Waitemata District Health Board, Auckland 0620, New Zealand
| | - Kevin Smith
- Clinical Laboratory, Waitemata District Health Board, Auckland 0620, New Zealand
| | - Erica Zarate
- School of Biological Science, University of Auckland, Auckland 1010, New Zealand
| | - Saras Green
- School of Biological Science, University of Auckland, Auckland 1010, New Zealand
| | - Silas Villas-Boas
- School of Biological Science, University of Auckland, Auckland 1010, New Zealand
| | - Phillip Shepherd
- Grafton Genomics Ltd, Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Purvi Kakadiya
- Grafton Genomics Ltd, Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Will Hewitt
- Auckland Bioengineering Institute, University of Auckland, Auckland 1010, New Zealand
| | - Eric Thorstensen
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Christine Keven
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Margaret Coe
- Liggins Institute, University of Auckland, Auckland 1023, New Zealand
| | - Bahareh Nakisa
- School of Information Technology, Deakin University, Victoria 3125, Australia
| | - Tan Vuong
- School of Information Technology, Deakin University, Victoria 3125, Australia
| | - Mohammad Naim Rastgoo
- School of Electrical Engineering & Computer Science, Queensland University of Technology, Brisbane, QLD 4072, Australia
| | - Mia Jüllig
- Paper Dog Limited, Waiheke Island, Auckland 1081, New Zealand
| | - Vito Starc
- Faculty of Medicine, University of Ljubljana, Ljubljana 1000, Slovenia
| | - Todd T Schlegel
- Karolinska Institutet, Stockholm, Sweden 171 77, Switzerland.,Nicollier-Schlegel Sàrl, Trélex, Karolinaka 1270, Switzerland
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