More frequent optimization by the adaptive crt algorithm in patients with higher daily activity: analysis of the adaptive crt trial
Bibliographic record
Abstract
Purpose: The benefit of CRT can be improved through optimization of its pacing parameters. Adaptive CRT algorithm (aCRT) evaluates intrinsic electrical conduction once every minute and provides either LV or BIV pacing with dynamically optimized AV and VV delays. Safety and clinical efficacy of the aCRT has been demonstrated in theAdaptive CRT trial. We investigated whether the frequency of the AV delay adjustments by the algorithm was related to patients daily activity. Methods: Daily activity is measured by a sensor as a percentage of the day when the activity exceeds a certain threshold. For each patient an average daily activity over the FU (20.4±5.7 months) was calculated. Patients (n=314) were stratified into 4 quartiles according to their average daily activity. We calculated: a) the percent of the once-a-minute algorithm conduction measurements which led to a subsequent adjustment in the device AV delay and b) the percent of patients who improved in Packer's Clinical Composite Score (CCS) and worsened from pre-implant to FU; c) changes in the LV EF and left-ventricular end-systolic index (LV ESVi) over the 12-month FU. The F-test was used to compare the means across the quartiles. Results: Patients in higher quartiles of the daily activity levels had greater frequency of AV delay adjustments by the aCRT algorithm and were characterized by a greater proportion of responders to CRT as defined by the improvement in CCS. There were no significant differences in LV EF and LV ESVi changes across patient activity quartiles. Table 1. Frequency of AV delay adjustments by the aCRT algorithm and changes in clinical end-points from pre-implant to 12-month FU stratified by activity levels Conclusions: Patients with higher activity experience more frequent adjustments of AV delays by the Adaptive CRT algorithm and are characterized by a greater improvement in clinical condition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".