Integration of longitudinal and circumferential strain predicts volumetric change across the cardiac cycle and differentiates patients along the heart failure continuum
Bibliographic record
Abstract
Left ventricular (LV) circumferential and longitudinal strain provide important insight into LV mechanics and function, each contributing to volumetric changes throughout the cardiac cycle. We sought to explore this strain-volume relationship in more detail, by mathematically integrating circumferential and longitudinal strain and strain rate to predict LV volume and volumetric rates of change. Cardiac magnetic resonance (CMR) imaging from 229 participants from the Alberta HEART Study (46 healthy controls, 77 individuals at risk for developing heart failure [HF], 70 patients with diagnosed HF with preserved ejection fraction [HFpEF], and 36 patients with diagnosed HF with reduced ejection fraction [HFrEF]) were evaluated. LV volume was assessed by the method of disks and strain/strain rate were assessed by CMR feature tracking. Integrating endocardial circumferential and longitudinal strain provided a close approximation of LV ejection fraction (EF Strain ), when compared to gold-standard volumetric assessment (EF Volume : r = 0.94, P < 0.0001). Likewise, integrating circumferential and longitudinal strain rate provided a close approximation of peak ejection and peak filling rates (PER Strain and PFR Strain , respectively) compared to their gold-standard volume-time equivalents (PER Volume , r = 0.73, P < 0.0001 and PFR Volume , r = 0.78, P < 0.0001, respectively). Moreover, each integrated strain measure differentiated patients across the HF continuum (all P < 0.01), with the HFrEF group having worse EF Strain , PER Strain , and PFR Strain compared to all other groups, and HFpEF having less favorable EF Strain and PFR Strain compared to both at-risk and control groups. The data herein establish the theoretical framework for integrating discrete strain components into volumetric measurements across the cardiac cycle, and highlight the potential benefit of this approach for differentiating patients along the heart failure continuum.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".