Insights from Novel Noninvasive CT and ECG Imaging Modalities on Electromechanical Myocardial Activation in a Canine Model of Ischemic Dyssynchronous Heart Failure
Bibliographic record
Abstract
INTRODUCTION: The interplay between electrical activation and mechanical contraction patterns is hypothesized to be central to reduced effectiveness of cardiac resynchronization therapy (CRT). Furthermore, complex scar substrates render CRT less effective. We used novel cardiac computed tomography (CT) and noninvasive electrocardiographic imaging (ECGI) techniques in an ischemic dyssynchronous heart failure (DHF) animal model to evaluate electrical and mechanical coupling of cardiac function, tissue viability, and venous accessibility of target pacing regions. METHODS AND RESULTS: Ischemic DHF was induced in 6 dogs using coronary occlusion, left bundle ablation and tachy RV pacing. Full body ECG was recorded during native rhythm followed by volumetric first-pass and delayed enhancement CT. Regional electrical activation were computed and overlaid with segmented venous anatomy and scar regions. Reconstructed electrical activation maps show consistency with LBBB starting on the RV and spreading in a "U-shaped" pattern to the LV. Previously reported lines of slow conduction are seen parallel to anterior or inferior interventricular grooves. Mechanical contraction showed large septal to lateral wall delay (80 ± 38 milliseconds vs. 123 ± 31 milliseconds, P = 0.0001). All animals showed electromechanical correlation except dog 5 with largest scar burden. Electromechanical decoupling was largest in basal lateral LV segments. CONCLUSION: We demonstrated a promising application of CT in combination with ECGI to gain insight into electromechanical function in ischemic dyssynchronous heart failure that can provide useful information to study regional substrate of CRT candidates.
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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 | 0.000 |
| 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".