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Prediction of Cardiovascular Events Using Fully Automated Global Longitudinal and Circumferential Strain in Patients Undergoing Stress CMR

2025· article· en· W4414168535 on OpenAlexaff
Andreea Sorina Afana, Jérôme Garot, Suzanne Duhamel, Thomas Hovasse, Stéphane Champagne, Thierry Unterseeh, Philippe Garot, Mariama Akodad, Teodora Chițiboi, Puneet Sharma, Athira Jacob, Trecy Gonçalves, J. Florence, Alexandre Unger, Francesca Sanguineti, Sebastian Militaru, Théo Pezel, Solenn Toupin

Bibliographic record

VenueCirculation Cardiovascular Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsCutoffMaceStress testing (software)Strain (injury)Stress EchocardiographyHeart failureCardiac magnetic resonance

Abstract

fetched live from OpenAlex

BACKGROUND: Stress perfusion cardiovascular magnetic resonance (CMR) is widely used to detect myocardial ischemia, mostly through visual assessment. Recent studies suggest that strain imaging at rest and during stress can also help in prognostic stratification. However, the additional prognostic value of combining both rest and stress strain imaging has not been fully established. This study examined the incremental benefit of combining these strain measures with traditional risk prognosticators and CMR findings to predict major adverse clinical events (MACE) in a cohort of consecutive patients referred for stress CMR. METHODS: This retrospective, single-center observational study included all consecutive patients with known or suspected coronary artery disease referred for stress CMR between 2016 and 2018. Fully automated machine learning was used to obtain global longitudinal strain at rest (rest-GLS) and global circumferential strain at stress (stress-GCS). The primary outcome was MACE, including cardiovascular death or hospitalization for heart failure. Cox models were used to assess the incremental prognostic value of combining these strain features with traditional prognosticators. RESULTS: Of 2778 patients (age 65±12 years, 68% men), 96% had feasible, fully automated rest-GLS and stress-GCS measurements. After a median follow-up of 5.2 (4.8–5.5) years, 316 (11.1%) patients experienced MACE. After adjustment for traditional prognosticators, both rest-GLS (hazard ratio, 1.09 [95% CI, 1.05–1.13]; P <0.001) and stress-GCS (hazard ratio, 1.08 [95% CI, 1.03–1.12]; P <0.001) were independently associated with MACE. The best cutoffs for MACE prediction were >–10% for rest-GLS and stress-GCS, with a C-index improvement of 0.02, continuous net reclassification improvement of 15.6%, and integrative discrimination index of 2.2% (all P <0.001). CONCLUSIONS: The combination of rest-GLS and stress-GCS, with a cutoff of >–10% provided an incremental prognostic value over and above traditional prognosticators, including CMR parameters, for predicting MACE in patients undergoing stress CMR.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.259
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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