Hepatic T1 Mapping in Takotsubo Syndrome: A Preliminary Imaging Insight into the Cardiohepatic Axis
Bibliographic record
Abstract
Background: Takotsubo syndrome (TS) is an acute heart failure characterized by transient systolic dysfunction of the left ventricle (LV). Given the complex cardiohepatic interactions in heart failure, the purpose of this study was to examine the role of hepatic T1 mapping in TS patients as an imaging biomarker of the cardiohepatic axis and to explore its correlation with demographics, laboratory data, and cardiovascular magnetic resonance (CMR) findings. Methods: In this retrospective pilot study, CMR was performed in 62 consecutive patients with TS (54 females, 73.47 ± 9.88 years). Additionally, 24 age- and sex-matched control subjects were included (20 females, 69.67 ± 6.88 years). A dedicated CMR software (CV42 6.0, CVI42, Circle Cardiovascular Imaging Inc., Calgary, AB, Canada) was used to assess atrial and ventricular strain parameters, as well as parametric mapping, including hepatic T1 mapping. Results: TS patients exhibited significantly higher hepatic T1 mapping values compared with the age-, sex-, and cardiovascular risk factor-matched control group (499.80 ± 141.86 vs. 425.26 ± 51.91, p = 0.017). In multivariable analysis, hepatic T1 mapping was independently associated with right ventricular (RV) longitudinal strain (β coefficient = 2.936, p = 0.007) and N-terminal pro-B-type natriuretic peptide (β coefficient = 2.395, p = 0.024). Conclusions: In this pilot study, hepatic T1 mapping was elevated in TS patients, suggesting its potential role as an imaging biomarker of cardiohepatic interaction. Hepatic T1 also showed independent associations with RV longitudinal strain and N-terminal pro-B-type natriuretic peptide, both well-known markers of adverse outcomes in TS. These preliminary findings warrant validation in larger studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".