Cardiac Function Stratification Based on Echocardiographic or Clinical Markers of Left Ventricular Filling Pressures Predicts Death and Hospitalization Better Than Stratification by Ventricular Systolic Function Alone
Bibliographic record
Abstract
BACKGROUND: A normal left ventricular ejection fraction (LVEF) often underestimates the poor prognosis associated with diastolic dysfunction. METHODS: We compared overall and hospital-free survival according to echocardiographic diastolic function classification (echo class), clinical probability of diastolic dysfunction (clinical class) and LV grades based on biplane LVEF, in 114 subjects followed-up over a median of 47 months. Diastolic function was classified into normal, impaired relaxation, and severe dysfunction (SDD), using a previously validated 3-staged classification. RESULTS: There were 16 deaths and 42 combined end points of death and hospitalization. Although each classification method globally prognosticated survival (P = 0.001, P =0.046, and P = 0.034 by the echo class, clinical class and LVEF grades, respectively), only echo class correctly distinguished three risk levels. Death was not hierarchically predicted by LVEF whereas severe diastolic dysfunction was associated with a hazard ratio by univariate or a multivariate model (that evaluated the effects of age, gender, and LVEF) of 4.31 (P =0.004) or 3.88 (P = 0.03), respectively. Also, a significant separation was found for the combined end points associated with SDD relative to nonsevere echo classes (P = 0.045). Neither clinical risk staging, nor LV grading showed significant separation of the Kaplan-Meier plots between "high risk" versus others combined, and Normal LV grade versus others combined, respectively. Severe diastolic dysfunction trended strongly as an independent predictor of combined end point with multivariate hazard of 2.29 (95% CI 0.99-5.26 P=0.05). CONCLUSION: Stratification of the severity of diastolic dysfunction using comprehensive echocardiographic parameters of systolic and diastolic function is effective at predicting death and hospital-free survival.
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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 | 0.000 | 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".