Performance of an Automatic Capture Confirmation Algorithm in a Large Cohort of Pacemaker Patients with Left Bundle Branch Area Pacing
Bibliographic record
Abstract
Objectives Automatic capture confirmation (ACC) algorithms monitor the pacing capture threshold (PCT) and adjust energy output to deliver a tailored safety margin over the PCT, while providing a high-output backup safety pulse in the event of non-capture. Advantages of these algorithms include increased device longevity, enhanced patient safety, and improved remote monitoring capabilities. While such algorithms have been validated for conventional right ventricular pacing (RVP) locations, there is limited information on their performance for pacing in the increasingly utilized location of the left bundle branch area (LBBA). Our objective was to evaluate the longitudinal performance and stability of the Abbott AutoCapture™ algorithm in patients with left bundle branch area pacing (LBBAP). Methods De-identified remote device data were retrospectively analyzed from consecutive patients in our hospital who received AutoCapture enabled Abbott pacemakers with LBBAP from June 2021 to August 2023. Device stored AutoCapture PCT measurements were then evaluated incrementally over an approximate 2-year period, to evaluate longer-term trends and performance, and also compared with the original, manual PCT at the time of initial implant. Results A total of 619 patients with either single chamber (model 1272, n= 89) or dual-chamber Abbott devices (model 2272, n= 530) were identified. AutoCapture and manually measured PCTs at implant were within 0.25 V in 600/615 (97.6%) patients, with average PCTs of 0.76 V ± 0.28 and 0.80 V ± 0.26 respectively, at a pulse width of 0.5 ms. At 1, 3, 6, 12, and 24-month remote follow-up, average AutoCapture PCTs were 0.67 V ± 0.29 (n=594), 0.66 V ± 0.25 (n=560), 0.71 V ± 0.29 (n=543), 0.77 V ± 0.29 (n=447) and 0.81 V ± 0.28 (n=112), respectively. At the last remote follow-up, lead impedances were 536 ohms ±60, and sensed R-wave amplitude were 11 mV ± 3. AutoCapture was found to be effective in assessing PCT and was activated in the majority of patients (619/644, 97%) without complications related to its activation or usage during the follow-up period. Conclusion The AutoCapture algorithm measured accurate PCTs at implant and showed a stable trend during follow-up out to approximately 2 years in patients with LBBAP.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".