915ATP efficacy on terminating ventricular tachycardia by device type, indication, and ventricular median cycle length
Bibliographic record
Abstract
Abstract Funding Acknowledgements Medtronic OnBehalf PainFree SST Background Anti-tachycardia Pacing (ATP) is an established therapy that terminates VT without the need for painful ICD shocks. Here we use the data from PainFree SST clinical trial to evaluate the ATP success rate by device type, indication and MCL. Methods Spontaneous episodes that were detected by ICD or CRT-D devices in the VT, fast VT and VF zones were included in the analysis. Episodes successfully terminated by ATP were deemed as having ATP success. Using the GEE method, ATP success rate and its 95% CI were calculated for device types, indications and ventricular MCL. Results Of the 2770 enrolled patients (79% male, average age 65 years), 1699 (61%) were implanted with an ICD and 1071 (39%) with a CRT-D system; 1917 (69%) were reported as primary prevention and 847 (31%) were secondary prevention patients. For all MVT episodes, the ATP success rate was similar between ICD and CRT-D devices (82.3% vs 80.3%, p = 0.74). Patients with secondary prevention had a higher ATP success rate compared to those with primary prevention but the difference was not statistically significant (84.4% vs 76.8%, p = 0.16). Regardless of device type and indication, ATP success rate was significantly higher in the slower VTs (MCL ≥ 320 ms) compared to the faster VTs (MCL ≥ 240 to < 320 ms) (89.2% vs 73.7%, p < 0.0001). Conclusion We found that ATP had a greater than 80% rate of success for terminating ventricular tachycardias overall. Slower VTs was significantly associated with a higher ATP success rate regardless of device type and indication compared to faster VTs. For faster VTs with a MCL ≥ 240 to < 320 ms, the ATP success rate was still successful at terminating VT more than 70% of the time. Table 1. ATP Success Rates - No. of Enrolled Subjects (% of total) No. of Episodes Analyzed for ATP Success (No. of Subjects) GEE-estimated ATP Success Rate (95% CI) P-value* Overall 2770 (100%) 2277 (376) 81.5% (78.4%, 84.2%) - Device Type - - - 0.7440 ICD 1699 (61.3%) 1484 (229) 82.3% (78.3%, 85.6%) - CRT-D 1071 (38.7%) 793 (147) 80.3% (75.0%, 84.6%) - Indication - - - 0.1609 Primary Prevention 1917 (69.2%) 631 (160) 76.8% (71.2%, 81.6%) - Secondary Prevention 847 (30.6%) 1615 (212) 84.4% (80.7%, 87.6%) - Median Cycle Length - - - <0.0001 (>/=) 240 ms and < 320 ms - 861 (257) 73.7% (69.2%, 77.7%) - (>/=) 320 ms - 1416 (209) 89.2% (85.7%, 91.9%) - * Per a GEE main effect model for all episodes where device type, indication and median cycle length were considered.
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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) | 0.000 | 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".