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/dt max 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, r 2 = 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".