Evidence map›Paper›PMID 42695072›Full record

ArticleEuropean heart journal. Imaging methods and practice2026

The FINESSE (Artificial Intelligence Stress Echo) study to develop and validate machine learning-based model to improve risk prediction in patients undergoing stress echocardiography for the assessment of inducible myocardial ischaemia (FINESSE Protocol).

Ugochukwu Ihekwaba, Mohamed Bennasar, Nicholas Johnson, Nerea Sanfeliu Garces, Hanry West, Blaine Price, Jeffrey Khoo, Iain Squire, Kenneth Chan, Attila Kardos

Registry-linked trialAbstract read
In one paragraph

Article in European heart journal. Imaging methods and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07432620 (Risk Prediction Model in Patients With Suspected Coronary Artery Disease Based on Contemporary Stress Echocardiography Data Using Artificial Intelligence), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT07432620 active not recruitingnot on this map

Risk Prediction Model in Patients With Suspected Coronary Artery Disease Based on Contemporary Stress Echocardiography Data Using Artificial Intelligence

TypeobservationalSponsorMilton Keynes University Hospital NHS Foundation TrustRan2019 to 2028Enrolled2,281ConditionsCoronary Artery Disease, Chest Pain, Myocardial Ischemia, Angina PectorisArmsDobutamine Stress Echocardiography
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Ugochukwu IhekwabaDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom.
Mohamed BennasarSchool of Computing and Communications, The Open University, Milton Keynes, United Kingdom.
Nicholas JohnsonDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom.
Nerea Sanfeliu GarcesDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom.
Hanry WestDivision of Cardiovascular Medicine, Radcliffe Department of Medicine University of Oxford, Acute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation, Centre of Research Excellence, Oxford, United Kingdom.
Blaine PriceSchool of Computing and Communications, The Open University, Milton Keynes, United Kingdom.
Jeffrey KhooNIHR Cardiovascular Research Centre, Glenfield Hospital, and Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom.
Iain SquireNIHR Cardiovascular Research Centre, Glenfield Hospital, and Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom.
Kenneth ChanDivision of Cardiovascular Medicine, Radcliffe Department of Medicine University of Oxford, Acute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation, Centre of Research Excellence, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-5571-7549
Attila KardosDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, United Kingdom.ORCID https://orcid.org/0000-0002-0231-7605

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, necessitating accurate diagnostic strategies and robust risk stratification. The FINESSE (Artificial Intelligence Stress Echo) study aims to develop and validate machine learning-based models to improve risk prediction in patients undergoing stress echocardiography (SE) for the assessment of inducible myocardial ischaemia, and to implement a structured AI-driven risk reclassification framework to support clinical decision-making. Methods and analysis: This is a retrospective observational study on prospectively recruited patients referred for SE with suspected CAD. Clinical, demographic, haemodynamic, and echocardiographic data will be extracted from institutional electronic health records and linked with longitudinal national datasets. The study employs a three-stage ML framework: (1) baseline risk estimation using QRISK3, (2) development and optimization of supervised ML models (including ensemble methods, support vector machines, and neural networks), and (3) longitudinal risk prediction using linked NHS England data (such as all-cause mortality and major adverse cardiovascular events, including cardiovascular death, non-fatal myocardial infarction, stroke, unplanned revascularization). Model performance will be evaluated using discrimination (AUC), calibration, and reclassification metrics, and will be externally validated using independent multicentre SE datasets. Conclusion: The FINESSE study will evaluate the incremental value of machine learning in SE by integrating multimodal data into a clinically actionable risk reclassification framework. This approach has the potential to improve personalized risk stratification, optimize diagnostic pathways, and support precision cardiovascular care. Protocol registration: Ethical approval has been obtained from the UK Health Research Authority and relevant research ethics committees, with appropriate data governance approvals for the use of routinely collected healthcare data. The FINESSE study is registered on ClinicalTrials.gov (Identifier: NCT07432620), ensuring transparency and public accessibility of the study protocol.

Indexed as

artificial intelligenceMACEoutcomeprotocolstress echocardiography

Identifiers

PMID42695072
PMCPMC13541104

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.