Quantification of lung water in heart failure using cardiovascular magnetic resonance imaging
Bibliographic record
Abstract
BACKGROUND: Pulmonary edema is a cardinal feature of heart failure but no quantitative tests are available in clinical practice. The goals of this study were to develop a simple cardiovascular magnetic resonance (CMR) approach for lung water quantification, to correlate CMR derived lung water with intra-cardiac pressures and to determine its prognostic significance. METHODS: Lung water density (LWD, %) was measured using a widely available single-shot fast spin-echo acquisition in two study cohorts. Validation Cohort: LWD was compared to left ventricular end-diastolic pressure or pulmonary capillary wedge pressure in 19 patients with heart failure undergoing cardiac catheterization. Prospective Cohort: LWD was measured in 256 subjects, including 121 with heart failure, 82 at-risk for heart failure and 53 healthy controls. Clinical outcomes were evaluated up to 1 year. RESULTS: Within the validation cohort, CMR LWD correlated to invasively measured left-sided filling pressures (R = 0.8, p < 0.05). In the prospective cohort, mean LWD was 16.6 ± 2.1% in controls, 17.9 ± 3.0% in patients at-risk and 19.3 ± 5.4% in patients with heart failure, p < 0.001. In patients with or at-risk for heart failure, LWD > 20.8% (mean + 2 standard deviations of healthy controls) was an independent predictor of death, hospitalization or emergency department visit within 1 year, hazard ratio 2.4 (1.1-5.1, p = 0.03). CONCLUSIONS: In patients with heart failure, increased CMR-derived lung water is associated with increased intra-cardiac filling pressures, and predicts 1 year outcomes. LWD could be incorporated in standard CMR scans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".