Assessment of Radiation-Induced Malfunction in Cardiac Implantable Electronic Devices
Bibliographic record
Abstract
Background Radiation therapy (RT) is a standard cancer treatment modality, and an increasing number of patients with cardiac implantable electronic devices (CIEDs) are being referred for RT. The goals of this study were as follows: (i) to determine the incidence of CIED malfunction following RT; (ii) to characterize the various types of malfunctions that occur; and (iii) to identify risk factors associated with CIED malfunction following RT. Methods A retrospective study of patients with CIEDs who received RT between 2007 and 2018 at 4 Canadian centres (Sunnybrook Health Sciences Centre, Kingston General Hospital, Hamilton Health Sciences Centre, and University of Ottawa Heart Institute) was conducted. Patients underwent CIED interrogation after completion of RT, to assess for late damage to the CIEDs. Data on demographics, devices, and RT were compared for the primary outcome of device malfunction. Results Of 1041 patients with CIEDs who received RT, 811 patients with complete data were included. Device malfunctions occurred in 32 of 811 patients (4%). The most common device malfunctions were reduced ventricular/atrial sensing (in 13 of 32 [41%]), an increase in lead threshold (in 9 of 32 [22%]), lead noise (in 5 of 32 [16%]), and electrical reset (in 2 of 32 [6%]). Higher beam energy (≥ 10 MV) was associated with malfunction ( P < 0.0001). Radiation dose was not significantly different between the malfunction and non-malfunction groups (58.3 cGy vs 65 cGy, respectively, P = 0.71). Conclusions Although RT-induced CIED malfunctions are rare (occurring in 4% of patients with a CIED who undergo RT), collaborative efforts between radiation oncologists and cardiac rhythm device clinics to optimize CIED monitoring are needed, to detect and manage CIED malfunctions. Malfunctions are more common in patients receiving higher–beam energy ( ≥ 10 MV \) RT.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".