How to deliver personalized cardiac resynchronization therapy through the precise measurement of the acute hemodynamic response: Insights from the iSpot trial
Bibliographic record
Abstract
Abstract Introduction New pacing technologies offer a greater choice of left ventricular pacing sites and greater personalization of cardiac resynchronization therapy (CRT). The effects on cardiac function of novel pacing configurations are often compared using multi‐beat averages of acute hemodynamic measurements. In this analysis of the iSpot trial, we explore whether this is sufficient. Materials and Methods The iSpot trial was an international, prospective, acute hemodynamic trial that assessed seven CRT configurations: standard CRT, MultiSpot (posterolateral vein), and MultiVein (anterior and posterior vein) pacing. Invasive and noninvasive blood pressure, and left ventricular (LV) dP/dtmax were recorded. Eight beats were recorded before and after an alternation from AAI to the tested pacing configuration and vice‐versa. Eight alternations were performed for each configuration at each of the five atrioventricular delays. Results Twenty‐five patients underwent the full protocol of eight alternations. Only four (16%) patients had a statistically significant >3 mm Hg improvement over conventional CRT configuration (posterolateral vein, distal electrode). However, if only one alternation was analyzed (standard multi‐beat averaging protocol), 15 (60%) patients falsely appeared to have a superior nonconventional configuration. Responses to pacing were significantly correlated between the different hemodynamic measures: invasive systolic blood pressure (SBP) vs noninvasive SBP r = 0.82 (P < .001); invasive SBP vs LV dP/dt r = 0.57, r2 = 0.32 (P < .001). Conclusions Current standard multibeat acquisition protocols are unfortunately unable to prevent false impressions of optimality arising in individual patients. Personalization processes need to include distinct repeated transitions to the tested pacing configuration in addition to averaging multiple beats. The need is not only during research stages but also during clinical implementation.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".