Clinical feasibility of targeted cardiac resynchronization lead delivery using a 3D MRI cardiac model
Bibliographic record
Abstract
Cardiac resynchronization therapy (CRT) aims to reduce dyssynchronous contraction through simultaneous pacing of the right ventricular (RV) septum and left ventricular (LV) lateral wall. Up to 40% of patients do not respond, largely attributed to lack of dysynchrony and/or transmural scar at pacing sites. In this pilot study we tested the feasibility of guiding LV and RV leads to “optimal” segmental targets using a MRI-based 3D surface rendered cardiac model. Ten consecutive patients planned for CRT were recruited. All patients underwent cardiac MRI inclusive of cine and delayed enhancement (DE) imaging using a 3T scanner. A blinded interpreter determined the time to maximal radial wall thickening (TmWT) and myocardial scar burden for each of 16 segments. All potential LV lead targets were ranked according to scar burden (lowest first) and then sub-ranked by TmWT (highest first). All potential RV lead targets were ranked according to scar burden (lowest first). These rankings were encoded onto a surface rendered cardiac model, displayed in standard fluoroscopic views (Figure 1A ) and used to direct lead placement by fluoroscopy. A cardiac gated CT was then performed at a 1-month follow-up visit (Figure 1B ) to assess procedural success for target achievement. Figure 1 Mean age and LVEF was 68.5 ±6.7 years and 25.6 ±10.8%, with a mean NYHA class of 2.9 ± 0.6. “Optimal” LV and RV pacing sites differed from conventional pacing sites (basal posterolateral segment and RV apex) in 9 and 4 patients, respectively. All patients successfully underwent CRT device implantation with a mean fluoroscopy time of 23 ± 10.9 minutes. Post-device CT imaging showed LV and RV leads to be successfully delivered to the “optimal” or immediately adjacent segment in 90% and 90% of patients, respectively. The only documented complication was a small pericardial effusion not requiring drainage in a patient that was prescribed conventional lead positioning by the model. In this pilot study we demonstrate that a targeted approach to CRT lead placement using an MRI-based cardiac model is clinically feasible. A prospective clinical trial evaluating the clinical benefit of this approach is planned.
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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 | 0.000 | 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 | 0.001 |
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".