Venneri, Lucia;
Aimo, Alberto;
Porcari, Aldostefano;
Sezer, Irem;
Ioannou, Adam;
Sheikh, Awais;
Mansell, Josephine;
... Fontana, Marianna; + view all
(2025)
Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis.
European Journal of Heart Failure
, Article ejhf.70073. 10.1002/ejhf.70073.
(In press).
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Abstract
Aims: In transthyretin amyloid cardiomyopathy (ATTR-CM), reduced stroke volume (SV) portends a poor prognosis. Artificial intelligence (AI) enables rapid, standardized assessment of left ventricular outflow tract velocity-time integral (LVOT-VTI), which is a reliable surrogate for SV. We investigated longitudinal changes in AI-derived LVOT-VTI as outcome predictors in ATTR-CM. / / Methods and results: Consecutive patients with ATTR-CM underwent baseline and 12 ± 1 month transthoracic echocardiography between 2007 and 2021. Scans were processed by an AI platform for fully automated measurements including LVOT-VTI. Changes in echocardiographic variables were related to all-cause mortality in a landmark analysis using multivariable Cox models adjusting for clinical covariates (age, sex, TTR genotype, atrial fibrillation status, New York Heart Association class and National Amyloidosis Centre stage). Time-dependent receiver-operating characteristic analysis identified the optimal threshold of LVOT-VTI change. A total of 752 patients (74 ± 9 years; 88% men; 66% wild-type) were followed for a median of 3.3 years (interquartile range 2.1–5.0 years), during which 334 (44.4%) died. Among changes in echocardiographic parameters over 12 months, only LVOT-VTI change remained independently prognostic (adjusted hazard ratio [HR] per 1% decrease 0.994, p = 0.025). A ≥5% decrease (n = 377 patients, 50%) independently predicted all-cause mortality (adjusted HR 1.41, 95% confidence interval 1.13–1.76; p = 0.003), and improved risk reclassification (integrated discrimination improvement = 0.012; continuous net reclassification improvement = 0.21, both p < 0.001). / / Conclusions: A ≥5% decrease of AI-derived LVOT-VTI over 12 months, a simple indicator of SV loss, is independently associated with worse outcome in ATTR-CM. Routine monitoring of this automated AI metric may guide earlier therapeutic escalation and is a possible endpoint for future trials.
| Type: | Article |
|---|---|
| Title: | Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/ejhf.70073 |
| Publisher version: | https://doi.org/10.1002/ejhf.70073 |
| Language: | English |
| Additional information: | © The Author(s), 2025. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | ATTR, Artificial intelligence, Cardiac amyloidosis, Disease progression, Echocardiography, Prognosis, Risk stratification, Stroke volume, Transthyretin amyloid cardiomyopathy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inflammation |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215712 |
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