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. 2024 Mar 13;5(3):295-302.
doi: 10.1093/ehjdh/ztae022. eCollection 2024 May.

The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

Milagros Pereyra Pietri  1 , Juan M Farina  1 , Ahmed K Mahmoud  1 , Isabel G Scalia  1 , Francesca Galasso  1 , Michael E Killian  1 , Mustafa Suppah  1 , Courtney R Kenyon  1 , Laura M Koepke  1 , Ratnasari Padang  2 , Chieh-Ju Chao  2 , John P Sweeney  1 , F David Fortuin  1 , Mackram F Eleid  2 , Kristen A Sell-Dottin  3 , David E Steidley  1 , Luis R Scott  1 , Rafael Fonseca  1 , Francisco Lopez-Jimenez  2 , Zachi I Attia  2 , Angela Dispenzieri  4 , Martha Grogan  2 , Julie L Rosenthal  1 , Reza Arsanjani  1 , Chadi Ayoub  1
Affiliations

The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

Milagros Pereyra Pietri et al. Eur Heart J Digit Health. .

Abstract

Aims: Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients.

Methods and results: In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction.

Conclusion: Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Keywords: Artificial intelligence; Cardiac amyloidosis; Transcatheter aortic valve replacement.

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Conflict of interest statement

Conflict of interest: none declared.

Figures

Graphical Abstract
Graphical Abstract
Figure 1
Figure 1
Artificial intelligence applied to electrocardiograms. Panel A demonstrates 12-lead electrocardiogram of a patient flagged as high probability for cardiac amyloidosis by the artificial intelligence algorithm as demonstrated in Panel 1B, with probability for cardiac amyloidosis predicted at 98.9% (red dot). In the contrast, with normal electrocardiogram (C) and low probability for cardiac amyloidosis at 0.03% predicted by the electrocardiogram artificial intelligence (purple dot) in D.
Figure 2
Figure 2
All-cause mortality in transcatheter aortic valve replacement patients with high vs. low probability for cardiac amyloidosis. ECG, electrocardiogram; HR, hazard ratio.
Figure 3
Figure 3
Major adverse cardiovascular events in transcatheter aortic valve replacement patients with high vs. low probability for cardiac amyloidosis. ECG, electrocardiogram; HR, hazard ratio.
Figure 4
Figure 4
Heart failure hospitalizations in transcatheter aortic valve replacement patients with high vs. low probability for cardiac amyloidosis. ECG, electrocardiogram; HR, hazard ratio.
See this image and copyright information in PMC

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