Abstract 10348: Contemporary Management of Cardiac Implantable Electronic Device Infection: A Survey of American College of Cardiology Members and Primary Care Physicians (COGNITO Study)
Bibliographic record
Abstract
Introduction: Cardiac implantable electronic devices (CIED) infection remains a serious complication associated with CIEDs. Complete extraction is a Class I recommendation for CIED infections, but a recent US Medicare analysis has demonstrated that there is a lack of guideline adherence with only 1 in 5 patients with a CIED infection undergoing extraction. Objective: To assess the current practice of US-based physicians related to the diagnosis and management of CIED infections and explore potential barriers to extraction. Methods: ACC performed a survey, based on a previous multinational EHRA/ESC needs assessment, to collect data from a mix of US physicians that may encounter CIED patients with complications. The survey timeframe was Feb 11 - Mar 10, 2022. Sampling techniques and screener questions were used to determine eligibility. The survey included questions based on knowledge and experience with CIED infection patients and multiple case scenarios. Results: The response rate was 20%, with a total of 387 physicians completing the survey. Less than half of clinicians polled (49%) indicated that they were familiar with the current HRS/AHA/EHRA/ESC guidelines regarding CIED infection. EPs (91%) were more familiar with these guidelines, compared to non-EP cardiologists (29%) and PCPs (23%). All EPs surveyed have had experience managing patients with CIEDs and nearly all (98%) have implanted these devices. Only two-thirds of non-EP cardiologists have experience with these patients. Only 30% of physicians specified that their institution had guideline-based protocols in place for managing patients with CIED infection. When presented with pocket infection cases, close to 50% of non-EP cardiologists and 70% of PCPs did not recommend guideline directed treatment. Conclusions: There are gaps in familiarity of guidelines as well as practical management of CIED infection with several non-extracting physician groups. Additionally, there is a lack of care pathways and other mechanisms in place for the management of CIED infection patients at most institutions. Addressing discrepancies, including guideline education and streamlining care/referral pathways, will be a key factor to bridging the gap and improving CIED infection patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".