Optimizing Ultrasound‐Guided Placement of Cardiac Implantable Electronic Devices: Current Uses and Challenges
Bibliographic record
Abstract
Cardiac implantable electronic devices are an effective treatment for heart rhythm disorders. Currently, x-ray imaging-especially fluoroscopy-is the standard method used to guide the implantation process and assess its effectiveness. However, due to risks associated with radiation exposure, ultrasound is being explored as an alternative to traditional imaging modality. The growing interest in adopting ultrasound as the primary imaging modality during implantation emphasizes the need for practitioners, particularly cardiac electrophysiologists, to be aware of its advantages as well as its limitations. This article discusses the existing and possible future uses of ultrasound in CIED implantation, explores new US-based technologies that could support its usage, and discusses the potential drawbacks associated with it. Additionally, it offers recommendations for enhancing the proficiency of ultrasound-guided implantation and mitigating its limitations. SUMMARY: Question: What is the scope of use of ultrasound in cardiac device implantation? FINDINGS: Studies show that ultrasound is safe for vascular access to reduce complications; aid in lead implantation on the ventricular septum, preventing cardiac perforation; and adjust slack to prevent lead-related tricuspid regurgitation. CLINICAL RELEVANCE STATEMENT: The clinical relevance of these findings is to help physicians utilize ultrasound during routine cardiac device implantation to enhance clinical outcomes and minimize complications, especially in the era of left bundle branch area pacing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".