Reducing Radiation Exposure During CRT Implant Procedures: Single‐Center Experience With Low‐Dose Fluoroscopy Settings and a Sensor‐Based Navigation System (MediGuide)
Bibliographic record
Abstract
Sensor‐Based Navigation and CRT Implantation Introduction Cardiac resynchronization therapy (CRT) implant procedures are often complex and prolonged, resulting in substantial ionizing radiation (IR) exposure to the patient and operator. We assessed the impact of lower‐dose fluoroscopy settings and a sensor‐based electromagnetic tracking system (MediGuide™, MDG) on reducing IR exposure during CRT implantation. Methods A single‐center 2‐group cohort study was conducted on 348 consecutive patients, age 66.4 ± 11.0 years, 80.4% male, with CRT implant procedures from 2013 to 2015. Patients were arbitrarily assigned to MDG (N = 239) versus no MDG (N = 109) guidance. Lower‐dose fluoroscopy settings were adopted in January 2015 (3 instead of 6 fps; 23 instead of 40 nGy/pulse; N = 101). Results Overall, MDG was associated with an 82.1% reduction in IR exposure (393 μGray·m 2 vs. 2191 μGray·m 2 , P < 0.001). Lower‐dose fluoroscopy resulted in a 59.5% reduction in IR‐exposure without MDG (1055 μGray·m 2 vs. 2608 μGray·m 2 , P < 0.001) and 81.8% reduction with MDG (108 μGray·m 2 vs. 595 μGray·m 2 , P < 0.001). Low‐dose fluoroscopy combined with MDG was associated with a 95.9% lower exposure to IR when compared to standard fluoroscopy without MDG (108 μGray·m 2 vs. 2608 μGray·m 2 , P < 0.001). Procedures with MDG were shorter (96 minutes vs. 123 minutes, P < 0.001) and associated with a trend towards a higher success rate (94.6% vs. 89.0%, P = 0.062), with fewer coronary sinus cannulation failures (2.1% vs. 6.4%, P = 0.040). Conclusion Low‐dose fluoroscopy settings are highly effective (>50%) in reducing IR exposure during CRT implant procedures. When combined with MDG, >95% reduction in IR exposure is achieved. Moreover, MDG shortens procedural duration and may improve acute 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".