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Record W4414312078 · doi:10.1111/echo.70297

Association of Global Longitudinal Strain and Long‐Term Transplant‐Free Survival in Fontan Patients

2025· article· en· W4414312078 on OpenAlexaff
Assami Rösner, Simone Goa Diab, Mark K. Friedberg, George K. Lui

Bibliographic record

VenueEchocardiography · 2025
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsStrain (injury)Fontan procedureAssociation (psychology)Longitudinal studyLongitudinal dataSurvival analysis

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Patients with single-ventricle physiology undergoing Fontan operations face high morbidity and mortality risks. While classic-pattern dyssynchrony (CPD) and protein-losing enteropathy (PLE) are known predictors of adverse outcomes, the role of global longitudinal strain (GLS) as an independent predictor of heart failure remains unclear. METHODS: A retrospective cohort study of 135 Fontan-operated patients from 2014 to 2015 evaluated the predictive value of GLS alongside PLE and CPD on mortality and transplantation after 9 years. Echocardiographic data, including GLS, were analyzed using speckle tracking strain analysis in 132 patients. The primary endpoint was transplant-free survival. RESULTS: Among 132 Fontan patients, 15 had classic-pattern dyssynchrony, 29 had protein-losing enteropathy, 37 had moderately reduced global longitudinal strain (GLS ←8% ≥-16%), and 18 had severely reduced GLS (≥-8%). Cox regression analysis showed moderately reduced GLS increased mortality risk (HR 5.8, 95% CI 1.27-26.5, p = 0.023), with severely reduced GLS showing an HR of 10.3 (95% CI 2.18-48.6, p = 0.003). These results were comparable to CPD (HR 11.5, p = 0.002) and PLE (HR 14.9, p < 0.001). CONCLUSION: Global longitudinal strain emerged as the best independent factor for predicting long-term transplant-free survival in Fontan patients, highlighting the importance of GLS assessment in routine follow-up to identify high-risk individuals for early intervention.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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