Multiomics, virtual reality and artificial intelligence in heart failure
| Autor: | Eric B. Thorstensen, Suzanne Loader, Margaret Coe, Tan Vuong, Kevin Smith, Christine Keven, Todd T. Schlegel, Saras Green, Patrick Gladding, Silas G. Villas-Boas, Purvi M. Kakadiya, Erica Zarate, Mia Jüllig, Phillip Shepherd, Will Hewitt, Bahareh Nakisa, Mohammad Naim Rastgoo, Vito Starc |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Left atrium
030204 cardiovascular system & hematology Ventricular Function Left Muscle hypertrophy Ventricular Dysfunction Left 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Mental stress medicine Humans End-systolic volume 030304 developmental biology Heart Failure 0303 health sciences Ejection fraction business.industry Virtual Reality Area under the curve Stroke Volume Prognosis medicine.disease medicine.anatomical_structure Heart failure Molecular Medicine End-diastolic volume Artificial intelligence Cardiology and Cardiovascular Medicine business |
| Zdroj: | Future Cardiology. 17:1335-1347 |
| ISSN: | 1744-8298 1479-6678 |
| Popis: | 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 -4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF. |
| Databáze: | OpenAIRE |
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