Evidence map›Paper›PMID 41098006›Full record

ArticleEuropean journal of heart failure2025

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

Lucia Venneri, Alberto Aimo, Aldostefano Porcari, Irem Sezer, Adam Ioannou, Awais Sheikh, Josephine Mansell, Yousuf Razvi, Surabhi Bhaskar Iyer, Ana Martinez-Naharro and 14 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of heart failure, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07529860 (AI-based Echocardiography for Detection of Cardiac Amyloidosis in Patients Undergoing Transthoracic Echocardiography With Left Ventricular Hypertrophy), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07529860 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

AI-based Echocardiography for Detection of Cardiac Amyloidosis in Patients Undergoing Transthoracic Echocardiography With Left Ventricular Hypertrophy

TypeinterventionalSponsorGermans Trias i Pujol HospitalRan2026 to 2028Enrolled200ConditionsCardiac Amyloidosis, Heart Failure With Preserved Ejection Fraction (HFPEF), Left Ventricular HypertrophyArmsAI-based echocardiogram
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors.

Lucia Venneri *National Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.ORCID https://orcid.org/0009-0006-2551-7403
Alberto Aimo *Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy.
Aldostefano PorcariNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Irem SezerNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Adam IoannouNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Awais SheikhNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Josephine MansellNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Yousuf RazviNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Surabhi Bhaskar IyerNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Ana Martinez-NaharroNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Francesco BanderaCardiology Unit, IRCCS MultiMedica, Milan, Italy.
Sze Chi LimUs2.ai, Singapore, Singapore.
Matthew FrostUs2.ai, Singapore, Singapore.
Justin EzekowitzUniversity of Alberta, Edmonton, Canada.
Carolyn S P LamNational Heart Centre Singapore, Singapore.
William MoodyDepartment of Cardiology, Queen Elizabeth Hospital Birmingham, Birmingham, UK.
Carol WhelanNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Helen LachmannNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Ashutosh WechelakarNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Michele EmdinInstitute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy.
Philip N HawkinsNational Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Scott David SolomonCardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Julian D Gillmore *National Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.
Marianna Fontana *National Amyloidosis Centre, Royal Free Hospital, London, University College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsIn 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

resultsConsecutive 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).

conclusionsA ≥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.

Indexed as

Amyloid Neuropathies, FamilialArtificial IntelligenceCardiomyopathiesEchocardiographyStroke VolumeAgedAged, 80 and overDisease ProgressionFemaleFollow-Up StudiesHumansMalePrognosisArtificial intelligenceATTRCardiac amyloidosisDisease progressionEchocardiographyPrognosisRisk stratificationStroke volumeTransthyretin amyloid cardiomyopathy

Identifiers

PMID41098006
PMCPMC12803646

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.