15 Predictors of right ventricular remodelling in reperfused inferior myocardial infarctions: cmr voxel feature tracking based feasibility study
Bibliographic record
Abstract
Background RV function after STEMI has important prognostic implications. However, changes in RV function over time after inferior-STEMI and the incidence of RV remodelling remain unclear. We aimed to investigate which parameters of RV function after inferior-STEMI influence RV remodelling at 3 months. Methods Twenty-one patients underwent CMR at 3T (Achieva CV, Philips Healthcare, Best, The Netherlands) within 3-days and 90-days following reperfused inferior/posterior STEMI. The CMR protocol included: cines and LGE imaging (0.1 mmol/kg gadolinium DTPA). Infarct location was determined from LGE images. Indexed RV end-diastolic volume (RVEDVi), end-systolic volume (RVESVi) and ejection fraction were derived from the short-axis stack cines for day-3 and day-90 scans. Offline strain analysis was performed for day-3 scans by voxel feature tracking (FT) for the RV and RA in the 4-chamber cines using commercially available software (cvi42 v5.1, Circle Cardiovascular Imaging Inc., Calgary, Canada). Results Mean age of our population was 57 ± 12 years-old. 86% patients were male. RV EF improved significantly from day-3 scan to day-90 scans (40 ± 12.6% vs. 49 ± 10.9%, p < 0.001). Day-3 RV EF demonstrated correlation to RV PLS (p = 0.03), RV PRS (p = 0.03) and RA TTP LSR (0.018). On multivariate stepwise analysis, RV PLS showed the strongest correlation (R=0.44; p = 0.04). Day-90 RV EF was most strongly correlated to TTP of LSR of RA (R=0.48, p = 0.048). Relative change in RV EF was also correlated to RV PLS (p = 0.03). Conclusion Voxel FT derived RV functional parameters, mainly PLS, correlates well with Day 3 RV EF and with relative change of RV EF at day-90. Interestingly, day-90 RV EF showed the strongest correlation to time to peak longitudinal strain rate (TTP LSR) of the RA. This may be because TTP SR parameters reflect mechanical dyssynchrony after the acute ischaemic event. This concept needs further clarification 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".