Abstract 11497: Focal Hibernating Myocardium -A Novel Method to Assess Ventricular Arrhythmogencity Using Molecular Imaging
Bibliographic record
Abstract
Introduction: Hibernating myocardium is associated with increased ventricular tachyarrhythmias and sudden cardiac death. Current algorithms aim to detect only large areas of left ventricular hibernation, likely to respond to revascularization and improve LV EF. However, no current tools allow the determination of small and focal hibernating areas within the ischemic substrate, which could act as VT trigger and substrate modulators, and initiate or maintain ventricular arrhythmias. Methods: Custom-made Matlab-based software was developed to perform quantitative segmental analysis. Patients with ischemic cardiomyopathy undergoing VT ablation underwent pre-procedural FDG and Rubidium-PET/Technicum SPECT to determine the metabolic and perfusion characteristics of the LV myocardium. After co-registering the voxel-based tracer intensity information was transferred into a 36 x 21 +1 matrix (757 segments analysis). After normalization comparative analysis identified functional categories of LV myocardium (normal:>75% uptake perfusion[p]/metabolism[M]; hibernation: P and MP+20% or P>50% but P+20%; matched scar P and M<50%). Results: Software was evaluated on metabolism/perfusion scans of 8 patients undergoing VT ablation. While all patients had reported scar, only 2/8 patients (25%) had clinically identified area of hibernation using the currently clinically employed nuclear medicine algorithm. All DICOM files were successfully uploaded into the software module, transformed and analyzed using the pre-specified functional categories of LV myocardium. Post-analysis polar plots of all patients demonstrated matched scar as seen during the clinical read. However, focal areas of hibernation were detected in all patients using the 757 algorithm analytical tool (100% vs. 25% for 757 segmental vs. standard clinical analysis, P<0.001) often adjacent to pre-specified scar category. ConclusioN: The novel quantitative 757 segmental analysis is able to detect and localize focal areas of hibernation in all patients with ischemic VT substrate. This allows the use of molecular imaging techniques to identify potentially proarrhythmic VT trigger and modulators and design novel diagnostic and therapeutic strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".