Left Ventricular Pacing Minimizes Diastolic Ventricular Interaction, Allowing Improved Preload-Dependent Systolic Performance
Bibliographic record
Abstract
BACKGROUND: Left ventricular (LV) pacing improves hemodynamics in patients with heart failure. We hypothesized that at least part of this benefit occurs by minimization of external constraint to LV filling from ventricular interaction. METHODS AND RESULTS: We present median values (interquartile ranges) for 13 heart failure patients with LV pacing systems implanted for New York Heart Association class III/IV limitation. We used the conductance catheter method to measure LV pressure and volume simultaneously. External constraint was measured from the end-diastolic pressure-volume relation recorded during inferior vena caval occlusion, during LV pacing, and while pacing was suspended. External constraint to LV filling was reduced by 3.0 (4.6 to 0.6) mm Hg from 4.8 (0.6 to 7.5) mm Hg (P<0.01) in response to LV pacing; effective filling pressure (LV end-diastolic pressure minus external constraint) increased by 4.0 (2.2 to 5.8) mm Hg from 17.7 (13.3 to 22.6; P<0.01). LV end-diastolic volume increased by 10 (3 to 11) mL from 238 (169 to 295) mL (P=0.01), whereas LV end-systolic volume did not change significantly (-1 [-2 to 3] mL from 180 [124 to 236] mL, P=0.97), which resulted in an increase in stroke volume of 11 (5 to 13) mL from 49 (38 to 59) mL (P<0.01). LV stroke work increased by 720 (550 to 1180) mL . mm Hg from 3400 (2110 to 4480) mL . mm Hg (P=0.01), and maximum dP/dt increased by 120 (2 to 161) mm Hg/s from 635 (521 to 767) mm Hg/s (P=0.03). CONCLUSIONS: This study suggests a potentially important mechanism by which LV pacing may produce hemodynamic benefit. LV pacing minimizes external constraint to LV filling, resulting in an increase in effective filling pressure; the consequent increase in LV end-diastolic volume increases stroke volume via the Starling mechanism.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".