Value of FEops HEARTguide patient-specific computational simulations in the planning of left atrial appendage closure with the Amplatzer Amulet closure device: rationale and design of the PREDICT-LAA study
Bibliographic record
Abstract
BACKGROUND: Optimal preprocedural planning is essential to ensure successful device closure of the left atrial appendage (LAA). DESIGN: The PREDICT-LAA study is a prospective, international, multicentre, randomised controlled trial (ClinicalTrials.gov NCT04180605). Two hundred patients eligible for LAA closure with an Amplatzer Amulet device (Abbott, USA) will be enrolled in the study. Patients will be allocated to a computational simulation arm (experimental) or standard treatment arm (control) using a 1:1 randomisation. For patients randomised to the computational simulation arm, preprocedural planning will be based on the analysis of cardiac computed tomography (CCT)-based patient-specific computational simulations (FEops HEARTguide, Ghent, Belgium) in order to predict optimal device size and position. For patients in the control arm, preprocedural planning will be based on local practice including CCT analysis. The LAA closure procedure and postprocedural antithrombotic therapy will follow local practice in both arms. The primary endpoint of the study is incomplete LAA closure and device-related thrombus as assessed at 3 months postprocedural CCT. Secondary endpoints encompass procedural efficiency (number of devices used, number of repositioning, procedural time, radiation exposure, contrast dye), procedure-related complications within 7 days postprocedure and a composite of all-cause death and thromboembolic events at 12 months. CONCLUSION: The objective of the PREDICT-LAA study is to test the hypothesis that a preprocedural planning for LAA closure with the Amplatzer Amulet device based on patient-specific computational simulations can result in a more efficient procedure, optimised procedural outcomes and better clinical outcomes as compared with a standard preprocedural planning. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04180605).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".