Lung Ultrasound and Mortality in a Cardiogenic Shock Population: A Prospective Registry-Based Analysis
Bibliographic record
Abstract
AIMS: Lung ultrasound (LUS) is a widely used technique to assess de-aeration in critically ill patients with respiratory failure. There is paucity of data on LUS in cardiogenic shock (CS). We sought to evaluate the epidemiology of lung congestion and its relation with outcome. METHODS AND RESULTS: The Altshock-2 registry is a multicentre, prospective, observational registry including all-comer CS patients. The LUS protocol included the examination of four zones using dichotomous assessment of lung congestion severity: ≤50% or >50%. LUS was performed at admission and at 24 h. Univariate and multivariate logistic regression analyses were performed. Overall, 185 patients (mean age 64.2 ± 13.5 years; 25.9% female) had a LUS at admission. A total of 128 patients (69.2%) had ≥50% of the investigated lung field with B-lines. At univariate Cox regression analysis, B-lines ≥50% at 24 h were significantly associated with increased 30-day mortality (hazard ratio [HR] 4.705; 95% confidence interval [CI] 2.329-9.508) and the reduction of B-lines during 24 h was associated with lower 30-day mortality (HR 0.739; 95% CI 0.571-0.956; p = 0.021). Results were confirmed at multivariate analysis after adjustment for significant covariates: B-lines ≥50% at 24 h (HR 2.23; 95% CI 1.042-8.654; p = 0.041) and the reduction in B-lines from baseline to 24 h (HR 0.815; 95% CI 0.415-1.132; p = 0.039). The sensitivity analysis, excluding patients with cardiac arrest, led to significantly increased accuracy in outcome prediction. CONCLUSION: Assessment and monitoring of lung congestion with LUS over the first 24 h in patients with CS allow to further stratify clinical outcomes with higher accuracy when added to SCAI classification, especially when excluding patients with cardiac arrest at CS presentation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".