Impact of Computational Modeling on Transcatheter Left Atrial Appendage Closure Efficiency and Outcomes
Bibliographic record
Abstract
BACKGROUND: When performing transcatheter left atrial appendage (LAA) closure, peridevice leaks and device-related thrombus (DRT) have been associated with worse clinical outcomes-hence, their risk should be mitigated. OBJECTIVES: The authors sought to assess whether use of preprocedural computational modeling impacts procedural efficiency and outcomes of transcatheter LAA closure. METHODS: The PREDICT-LAA trial (NCT04180605) is a prospective, multicenter, randomized trial in which 200 patients were 1:1 randomized to standard planning vs cardiac computed tomography (CT) simulation-based planning of LAA closure with Amplatzer Amulet. The artificial intelligence-enabled CT-based anatomical analyses and computer simulations were provided by FEops (Belgium). RESULTS: All patients had a preprocedural cardiac CT, 197 patients underwent LAA closure, and 181 of these patients had a postprocedural CT scan (standard, n = 91; CT + simulation, n = 90). The composite primary endpoint, defined as contrast leakage distal of the Amulet lobe and/or presence of DRT, was observed in 41.8% in the standard group vs 28.9% in the CT + simulation group (relative risk [RR]: 0.69; 95% CI: 0.46-1.04; P = 0.08). Complete LAA closure with no residual leak and no disc retraction into the LAA was observed in 44.0% vs 61.1%, respectively (RR: 1.44; 95% CI: 1.05-1.98; P = 0.03). In addition, use of computer simulations resulted in improved procedural efficiency with use of fewer Amulet devices (103 vs 118; P < 0.001) and fewer device repositionings (104 vs 195; P < 0.001) in the CT + simulation group. CONCLUSIONS: The PREDICT-LAA trial demonstrates the possible added value of artificial intelligence-enabled, CT-based computational modeling when planning for transcatheter LAA closure, leading to improved procedural efficiency and a trend toward better procedural outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.009 |
| 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) | 0.000 | 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 teacher head, 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".