Prognostic Value of Left Ventricular End-Diastolic Pressure in Patients With Non-ST-Segment Elevation Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Elevated left ventricular end-diastolic pressure (LVEDP) has been reported to predict an increased mortality in patients with ST-segment elevation myocardial infarction. However, its prognostic value in patients with non-ST-segment elevation myocardial infarction (NSTEMI) remains unclear. METHODS: We performed a retrospective analysis of NSTEMI patients who underwent coronary angiography between January 2013 and June 2014. We excluded patients who did not undergo LVEDP measurements. Baseline and angiographic characteristics, in-hospital heart failure as well as in-hospital mortality were recorded. RESULTS: After exclusion, 367 patients were included in the final analysis. The median (interquartile range) LVEDP was 19 mm Hg (14 - 24 mm Hg). By receiver operating characteristic curve analysis, the optimal cutoff value for predicting in-hospital mortality was 22 mm Hg (area under the curve 0.80, sensitivity 80%, and specificity 71%). Of 367 patients, 109 patients (29.7%) had LVEDP > 22 mm Hg. Patients with LVEDP > 22 mm Hg had a greater number of comorbidities. There was no statistically significant difference in the rate of multi-vessel disease. Patients with LVEDP > 22 mm Hg had a significantly higher rate of in-hospital heart failure (22.0% vs. 13.2%, P = 0.03) and in-hospital mortality (3.7% vs. 0.4%, P = 0.03) than those with LVEDP ≤ 22 mm Hg. CONCLUSION: Elevated LVEDP was significantly associated with a higher in-hospital mortality in patients with NSTEMI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".