Predictive Value of Lung Ultrasound Combined With ACEF Score for the Prognosis of Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Lung ultrasound (LUS) and the ACEF score (age, creatinine, and ejection fraction) have been shown to be pivotal in predicting an unfavorable prognosis in acute myocardial infarction (AMI). HYPOTHESIS: The aim of this study is to investigate the prognostic value of LUS combined with ACEF score in AMI. METHODS: The ACEF score and the total number of B-lines in eight thoracic regions of LUS were calculated. Adverse events were recorded during hospitalization and follow-up, defined as all-cause death and other cardiovascular events. Multivariate logistic regression identified predictors of adverse events during hospitalization. Multivariate Cox regression identified predictors of adverse events during follow-up. RESULTS: We enrolled 204 patients. The B-lines (adjusted OR 1.08, [95% CI: 1.03-1.13], p < 0.01) and the ACEF score (adjusted OR 2.71 [95% CI: 1.07-6.81], p < 0.05) independently predicted adverse events during hospitalization. The C-index values were 0.81 (p < 0.01) for the ACEF score, 0.81 (p < 0.01) for LUS, and 0.86 (p < 0.01) for their combination. One hundred seventy-one patients were followed up for 12 months (IQR, 8.13-15.93). Both the B-lines (adjusted HR 1.06 [95% CI: 1.03-1.09], p < 0.05) and the ACEF score (adjusted HR 1.95 [95% CI: 1.10-3.43], p < 0.05) remained associated with an increased risk of adverse events during follow-up. The C-index values were 0.74 (p < 0.01) for the ACEF score, 0.73 (p < 0.01) for LUS, and 0.80 (p < 0.01) for their combined predictive ability. CONCLUSIONS: The B-lines and ACEF score are associated with adverse events in AMI patients. When combined, they provide increasing value in assessing the risk of adverse events, which has significant implications for risk stratification.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".