Relationship Between Left Ventricular Geometry and Invasive Hemodynamics in Pediatric Pulmonary Hypertension
Bibliographic record
Abstract
Background Ventricular septal flattening, frequently present in pulmonary hypertension (PH), can be quantified using eccentricity index (EI). EI has not been evaluated by concurrent echocardiography and cardiac catheterization and traditionally does not account for postsystolic septal flattening, often seen in PH. We evaluated left ventricular shape, including a novel measure of maximal EI to account for postsystolic septal flattening, to establish the relationship with concurrent invasive hemodynamics. Methods Echocardiography was performed at 2 institutions in 78 pediatric PH patients during cardiac catheterization and in 78 matched controls. From midpapillary parasternal short-axis views, EI and right-to-left ventricular diameter ratio were assessed. Results EI and right-to-left ventricular measures were significantly increased in PH compared with controls. Shape measures correlated with invasive hemodynamics and PH outcome measures (PH-related hospitalization, functional class, medical therapy escalation, and BNP [brain natriuretic peptide]). End-systolic EI of 1.16 best identified the presence of PH, whereas a maximal EI of 1.42 and 1.94 best identified half-systemic and systemic PH, respectively. A maximal EI of 1.27 was associated with an odds ratio of 16.16 (95% CI, 6.62-39.46) for PH-related hospitalization or escalation of therapy. Conclusions Using simultaneous echocardiography and catheterization in the largest study population to date, we demonstrate that EI and right-to-left ventricular ratio correlate with invasive hemodynamics and outcomes measures, and EI can accurately define those with clinically important PH. These measures strengthen the ability of echocardiography to identify and follow pediatric PH patients, especially in the absence of methods to quantify right ventricular systolic pressures.
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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 | 0.000 | 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".