The Need for Evaluating Right Ventricular Adaptation and Ventriculo–Arterial Coupling
Bibliographic record
Abstract
We read with great interest the paper of Ghio et al. on the prognostic value of assessing right ventricular (RV) function in patients with heart failure with reduced (HFrEF), mid-range (HFmrEF), or preserved ejection fraction (HFpEF).1 This study showed that the tricuspid annular plane systolic excursion to pulmonary artery systolic pressure (TAPSE/PASP) ratio is an independent predictor of survival regardless of left ventricular ejection fraction (LVEF). In addition, pulmonary hypertension was associated with an increased risk of RV dysfunction in patients with HFpEF and HFmrEF but not with HFrEF. Since RV dysfunction is strongly related to increased afterload, it has been suggested that RV function in the different heart failure subtypes may be better quantified based on the assessment of ventriculo–arterial coupling. The TAPSE/PASP ratio as a marker of RV–vascular coupling also proved to be associated with prognosis.2 However, reference values for the TAPSE/PASP ratio have not been universally defined. In the article of Ghio and colleagues,1 the prevalence of RV dysfunction was 12.6% compared with 40% in another study.3 In HFpEF patients, the prevalence of pulmonary hypertension did vary according to the method used for non-invasive measurement of haemodynamic parameters: authors found increased pulmonary pressure value in 31% by TAPSE measurement, in 26% using tissue Doppler, and merely in 13% by RV fractional area change calculation.4 Uncertainty exists regarding the prognostic relevance of ventriculo–arterial coupling in heart failure subtypes. Ghio and colleagues showed that in HFpEF and HFmrEF a TAPSE value of ≤14 mm was associated with RV dysfunction in patients with PASP >40 mmHg. A strong correlation was also found between reduced TAPSE and elevated PASP. Notably, PASP <40 mmHg is usually associated with normal RV function,1 which may account for the lack of statistical significance at multivariable analysis. In contrast, Bosch et al. found that TAPSE and PASP were similarly decreased, and RV–arterial coupling was prognostically important in heart failure regardless of LVEF.3 Possible reasons for these discrepancies include differences in the study populations, as well as in the prevalence of pre- and post-capillary pulmonary hypertension.4 Finally, although the TAPSE/PASP ratio can be easily obtained, it does not provide an accurate appraisal of vascular compliance, resistance and pressure. Furthermore, in cases of severe RV dilatation and dysfunction or severe tricuspid regurgitation, the transvalvular gradient may be underestimated. These limitations can be overcome by estimating pulmonary arterial stiffness and pulmonary vascular function as derived from pulmonary artery pulse wave velocity and flow area changes. An accurate method that provides additional information about RV volume, geometry and pulmonary branches is cardiac magnetic resonance.5 In conclusion, in both HFrEF and HFpEF, RV function and afterload are important parameters for risk stratification. A valid and reliable method for evaluating the different relation between RV adaptation and pulmonary afterload could be of potential benefit in these patients.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".