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. 2025 Oct 16.
doi: 10.1002/ejhf.70073. Online ahead of print.

Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis

Lucia Venneri #  1 , Alberto Aimo #  2   3 , Aldostefano Porcari  1 , Irem Sezer  1 , Adam Ioannou  1 , Awais Sheikh  1 , Josephine Mansell  1 , Yousuf Razvi  1 , Surabhi Bhaskar Iyer  1 , Ana Martinez-Naharro  1 , Francesco Bandera  4   5 , Sze Chi Lim  6 , Matthew Frost  6 , Justin Ezekowitz  7 , Carolyn S P Lam  8 , William Moody  9   10 , Carol Whelan  1 , Helen Lachmann  1 , Ashutosh Wechelakar  1 , Michele Emdin  2   3 , Philip N Hawkins  1 , Scott David Solomon  11 , Julian D Gillmore #  1 , Marianna Fontana #  1
Affiliations

Artificial intelligence-based echocardiographic assessment for monitoring disease progression in transthyretin cardiac amyloidosis

Lucia Venneri et al. Eur J Heart Fail. .

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.

Keywords: ATTR; Artificial intelligence; Cardiac amyloidosis; Disease progression; Echocardiography; Prognosis; Risk stratification; Stroke volume; Transthyretin amyloid cardiomyopathy.

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References

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