Citations 2
Abstract 17227: An Artificial Intelligence Based Model for Heart Failure With Preserved Ejection Fraction Predicts Cardiac Mortality
Cassianni, Claire , Huntley, Geoffrey D. , Porumb, Mihaela, Chartsias, Agisilaos , Ryu, Alexander J. , Hawkes, William , Arystan, Ayana Z, Kane, Garvan C. , Lopez‐Jimenez, Francisco, Sarwar, Rizwan , Leeson, Paul , Woodward, Gary , Cassianni, Claire E , Akerman, Ashley Paul , Scott, Christopher G. , Ryu, Alexander J. , Hawkes, William, Pislaru, Sorin V. , López-Jiménez, Francisco , O’Driscoll, Jamie M. , Upton, Ross , Pellikka, Patricia A.
Circulation | December 19, 2023 | Volume: 148 | Issue: Suppl_1 | Pages: No page available
10.1161/circ.148.suppl_1.17227
Background: Diagnosis of heart failure with preserved ejection fraction (HFpEF) is challenging due to non-diagnostic imaging outputs and discordant clinical features. Using only a single videoclip from a standard transthoracic echocardiogram (TTE), we developed an artificial intelligence (AI) model to differentiate patients with HFpEF from those without HFpEF. In a separate test population, we assessed the model’s ability to predict cardiac mortality. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber videoclips to classify patients with HFpEF (cases) versus without HFpEF (controls). Overall and cardiac mortality were obtained from the National Death Index. Results: The model was independently tested in 646 cases and 638 controls from 8 centers in 4 states. Sensitivity (87.8%; 84.5-90.9%) and specificity (81.9%; 78.2-85.6%) were high and sustained in various subgroups, including increased left atrial volume index, average E/e’ ≥15, and pulmonary artery systolic pressure >35 mm Hg. During follow-up (median [IQR] 3.4 [1.7-6.5] years), 540 patients died. There were 135 cardiac deaths attributable to heart failure (47%), coronary artery disease (41%), valve disease (4%), arrhythmia (4%), and others (5%). Compared to a negative output, all-cause mortality was higher in patients with a positive (HR 2.9 [2.4-3.5]) or uncertain output (HR 1.8 [1.2-2.5]), and cardiac mortality (Figure) was also higher in patients with a positive (HR 7.7 [4.5-13.3]) or uncertain output (HR 3.4 [1.4-8.1]). Cardiac mortality differences according to AI model prediction remained after age adjustment (positive: HR 5.9 [3.3-10.4], p<0.001; uncertain: HR 2.8 [1.2-6.8], p=0.02). Conclusion: A novel AI model to detect HFpEF based on a single routinely acquired TTE videoclip also identifies those at increased risk of cardiac mortality.
Stress Echo 2030: The Novel ABCDE-(FGLPR) Protocol to Define the Future of Imaging
Pepi, Mauro , Ciampi, Quirino , Cortigiani, Lauro , Arruda‐Olson, Adelaide M. , Daros, Clarissa Borguezan , Pretto, José Luis de Castro e Silva , Bossone, Eduardo , Merli, Elisa , Kane, Garvan C. , Scali, Maria Chiara, Morrone, Doralisa , Simova, Iana , Samardjieva, Martina, Boshchenko, A , Ryabova, T. R. , Pálinkás, Eszter Dalma , Sepp, Róbert , Torres, Marco Antônio Rodrigues , Villarraga, Hector R. , Kovačević-Preradović, Tamara , Citro, Rodolfo , Amor, Miguel , Mosto, Hugo , Salamè, Michael, Leeson, Paul , Gaibazzi, Nicola , Tuttolomondo, Domenico , Prota, Costantina , Peteiro, Jesús , Heyning, Caroline M. Van De , D’Andrea, Antonello , Rigo, Fausto , Ostojić, Miodrag , Arbucci, Rosina , Haber, Diego M. Lowenstein, Merlo, Pablo , Wierzbowska‐Drabik, Karina , Kasprzak, Jarosław D. , Haberka, Maciej , Ratanasit, Nithima , Mori, Fabio , D’Alfonso, Maria Grazia , Tassetti, Luigi , Milazzo, Alessandra , Olivotto, Iacopo , Marchi, Alberto, Rodríguez‐Zanella, Hugo , Zagatina, A , Padang, Ratnasari , Dekleva, Milica , Djordievic-Dikic, Ana, Boskovic, N , Tešić, Milorad , Giga, Vojislav , Salvo, Giovanni Di , Lorenzoni, Valentina , Cameli, Matteo , Bombardini, Tonino, Caso, Pio, Čelutkienė, Jelena , Barbieri, Andréa , Benfari, Giovanni , Bartolacelli, Ylenia , Malagoli, Alessandro , Mantovani, Francesca , Villari, Bruno, Russo, Antonello , Nes, Michele De, Carpeggiani, Clara , Monte, Ines , Re, Federica , Cotrim, Carlos , Bilardo, G, Karužas, Arnas , Matuliauskas, D, Colonna, Paolo , Antonini‐Canterin, Francesco, Pellikka, Patricia A. , (SIECVI), The Stress Echo 2030 Study Group of the Italian Society of Echocardiography and Cardiovascular Imaging, Borguezan-Daros, Clarissa , de Castro E Silva Pretto, José Luis , Bossone, Eduardo , Morrone, Doralisa , Palinkas, Eszter Dalma , Preradović, Tamara Kovačević , Tuttolomondo, Domenico , Peteiro, Jesus , Van De Heyning, Caroline M , D'Andrea, Antonello , Arbucci, Rosina , Wierzbowska-Drabik, Karina , Tassetti, Luigi , Olivotto, Iacopo , Rodriguez-Zanella, Hugo , Tesic, Milorad , Di Salvo, Giovanni , Cameli, Matteo , Mandoli, Giulia Elena , Barbieri, Andrea , Benfari, Giovanni , Bartolacelli, Ylenia , Malagoli, Alessandro , Mantovani, Francesca , Monte, Ines , Cotrim, Carlos , Karuzas, Arnas , Colonna, Paolo , Pepi, Mauro , Pellikka, Patricia A , Ciampi, Quirino , Zagatina, Angela , Kasprzak, Jaroslaw D , Ryabova, Tamara , Gaibazzi, Nicola , Lorenzoni, Valentina , Citro, Rodolfo , Boshchenko, Alla , Celutkiene, Jelena , Cocchia, Rosangela , Varga, Albert , Ágoston, Gergely , Vrublevsky, A. V. , Pálinkás, Attila , Mangia, Cristina , Nikolic, Aleksandra, Lowenstein, Jorge , Camarozano, Ana Cristina , Marchi, Alberto, Beleslin, Branko , Bursi, Francesca , Saad, Ariel K. , (SIECVI), The Stress Echo 2030 Study Group of the Italian Society of Echocardiography and Cardiovascular Imaging
Journal of Clinical Medicine | August 17, 2021 | Volume: 10 | Issue: 16 | Pages: 3641
With stress echo (SE) 2020 study, a new standard of practice in stress imaging was developed and disseminated: the ABCDE protocol for functional testing within and beyond CAD. ABCDE protocol was the fruit of SE 2020, and is the seed of SE 2030, which is articulated in 12 projects: 1-SE in coronary artery disease (SECAD); 2-SE in diastolic heart failure (SEDIA); 3-SE in hypertrophic cardiomyopathy (SEHCA); 4-SE post-chest radiotherapy and chemotherapy (SERA); 5-Artificial intelligence SE evaluation (AI-SEE); 6-Environmental stress echocardiography and air pollution (ESTER); 7-SE in repaired Tetralogy of Fallot (SETOF); 8-SE in post-COVID-19 (SECOV); 9: Recovery by stress echo of conventionally unfit donor good hearts (RESURGE); 10-SE for mitral ischemic regurgitation (SEMIR); 11-SE in valvular heart disease (SEVA); 12-SE for coronary vasospasm (SESPASM). The study aims to recruit in the next 5 years (2021–2025) ≥10,000 patients followed for ≥5 years (up to 2030) from ≥20 quality-controlled laboratories from ≥10 countries. In this COVID-19 era of sustainable health care delivery, SE2030 will provide the evidence to finally recommend SE as the optimal and versatile imaging modality for functional testing anywhere, any time, and in any patient.