Right Ventricular Systolic and Diastolic Parameters on the Basis of Pressure-Volume Loop Theory in Patients With Heart Failure
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess the clinical utility of right ventricular (RV) systolic and diastolic parameters derived from the RV pressure waveform obtained through right heart catheterization (RHC) based on pressure-volume (PV) loop theory in patients with heart failure (HF). METHODS: The study included patients hospitalized for advanced HF who underwent RHC at our institution. RV end-systolic elastance (Ees), RV arterial elastance (Ea), RVEes/Ea ratio and RV diastolic stiffness coefficient (β) were calculated from RV pressure waveforms. The prognostic value of these parameters was evaluated for the primary outcome defined as all-cause mortality or HF-related hospitalization. RESULTS: A total of 254 patients were analyzed, including 141 with a left ventricular ejection fraction (LVEF) <40% and 113 with LVEF ≥40%. Among the RV systolic and diastolic parameters, a low RVEes/Ea ratio (<0.55) (hazard ratio [HR], 4.1; 95% confidence interval [CI], 2.4-6.9; p < 0.001) and an elevated RVβ (≥0.025) (HR, 4.8; 95% CI, 2.5-9.0; p < 0.001) were associated with a higher risk of the primary outcome. In those with LVEF <40%, a low RVEes/Ea ratio was a stronger independent predictor of the primary outcome (HR, 3.5; 95%CI, 1.6-7.5; p = 0.002), whereas in those with LVEF ≥40%, elevated RVβ was the more significant independent risk factor (HR, 3.0; 95%CI, 1.1-8.2; p = 0.029), even after multiple adjustments for covariate factors. CONCLUSION: RV waveform evaluation based on PV loop theory was effective in predicting prognosis in HF patients, irrespective of LVEF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".