Targeting ventricular tachycardia originating from the anterolateral papillary muscle with stereotactic body radioablation therapy
Bibliographic record
Abstract
Abstract Background Ventricular Tachycardia (VT) is prevalent in the setting of non-ischaemic cardiomyopathy (NICM), a heterogeneous group of conditions characterized by myocardial dysfunction in the absence of coronary artery disease. Challenges of catheter ablation to treat VT in NICM patients are well known, and recurrence rates can be as high as 58%. Stereotactic body radioablation therapy (SBRT) involves precise delivery of radiation to arrhythmogenic substrates, and represents a novel treatment to reduce recurrent implantable cardioverter defibrillator (ICD) therapies in patients with refractory VT. Though SBRT has shown preliminary effectiveness in reducing VT episodes, its effectiveness in NICM patients has not been widely evaluated. Purpose To evaluate SBRT effectiveness in NICM patients with scars at the basal anterolateral papillary muscle at a tertiary care center between November-December 2022. Methods Eligible patients had their VT substrate mapped using a single-use ECGI vest and a 4D-CT simulation scan. In addition, MRI scans were used to aid anatomical identification of the anterolateral papillary muscle which could not be seen on the non-contrast diagnostic CTs. SBRT was delivered in a single fraction of 25 Gy under image guidance. Effectiveness of the treatment was evaluated as changes in frequencies of total antitachycardia pacing (ATP) and ICD shock episodes from 6 months pre-, and within to 3, 6, and 12 months follow up post SBRT. Results Participants were 3 males with NICM, aged 56, 60, and 73 with previous dual chamber ICD implantation, 2 of whom had a history of VT ablation/ VT storm and paroxysmal atrial fibrillation. All participants had basal lateral scar, and were receiving beta blockers, mexiletine, and amiodarone with refractory VT before SBRT treatment. Prior to SBRT, ICD device interrogation showed a total of 119 (77, 7, and 35 in patients 1-3 respectively) ATPs, and 27 (10, 1, and 16 in patients 1-3 respectively) ICD shocks delivered. At 12 month follow-up, none of the participants experienced any episodes of ATP or ICD shock. Ejection fraction remained consistent pre-post SBRT. Conclusion SBRT showed remarkable success in reducing ATP and ICD shock episodes, facilitating uncomplicated 1 year follow-up in 3 male patients with NICM and VT from the anterolateral papillary muscle.
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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 | 0.000 |
| 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) | 0.001 | 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".