Abstract 12770: Left-Atrial Contractile Contribution to Left Ventricle Filling is Preserved in Moderate Chronic Heart Failure
Bibliographic record
Abstract
Introduction: The extent that left atrium (LA) functional remodeling is impacted by left ventricle (LV) systolic function or clinical history in patients with chronic heart failure (HF) is unresolved. Cardiac imaging and plasma biomarkers may provide valuable information in this regard. Hypothesis: Elastic and active contributions to LV stroke volume (LV SV) are variable, independent of LV systolic function and reflected in natriuretic peptide measurements. Methods: We recruited 27 controls without overt cardiovascular disease and 68 patients with moderate HF, including preserved and reduced LV ejection fraction (LVEF). Cardiac MRI was used to evaluate LV volumes at end diastole and end systole, and LA volumes at end diastole, diastasis and end systole to determine passive and active volume changes. Plasma mid-region pro-atrial and N-terminal pro-brain natriuretic peptides (MR-proANP and NT-proBNP, respectively) were measured. Results: Elastic LA contribution to LV SV was significantly lower in HF (median, IQR: 14%, 8-23%) than controls (24%, 17-30%; p=0.001), but active contribution was not (23%, 0-34% vs 24%, 21-31%; p=0.163). In patients with HF, active LA contribution to LV SV was negatively associated with history of hypertension (β=-0.41, p=0.007) and positively associated with aging (β=0.40, p=0.008) in a multiple linear regression model. Seven of nine subjects with HF and a history of atrial fibrillation (AF), but in sinus rhythm (SR) on the day of study, had active LA contributions to LV SV >15%. Increased LA volume at end diastole (β=0.51, p=0.001) and LVEF (β=-0.33, p=0.008) were independently associated with NT-proBNP (β=0.51, p=0.001) in a model that included age and history of AF. Similarly, LA volume at end diastole (β=0.48, p=0.001) was independently associated with MR-proANP in a model that included age and history of AF. Notably, natriuretic propeptides were not associated with elastic or active LA contributions to LV SV. Conclusions: Heart failure is characterized by preserved LA active contribution to LV SV, regardless of LVEF, which increases with age, and is modifiable by hypertension and AF. Imaging-based assessment can elucidate the contribution of LA function in HF and may provide insight into the impact of AF in patients with HF.
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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) | 0.002 | 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".