Echocardiographic Findings in Critically Ill COVID-19 Patients Treated With and Without Extracorporeal Membrane Oxygenation
Bibliographic record
Abstract
OBJECTIVES: To describe echocardiographic findings among mechanically ventilated patients with COVID-19 acute respiratory distress syndrome, comparing those with and without venovenous extracorporeal membrane oxygenation (VV ECMO) support. DESIGN: Single-center, retrospective cohort study. SETTING: Intensive care unit (ICU) of a quaternary academic center. PARTICIPANTS: Patients with COVID-19 admitted between March 2020 and June 2021 receiving mechanical ventilation, with an echocardiogram within 72 hours of admission. INTERVENTIONS: Admission and follow-up echocardiograms during ICU stay. MEASUREMENTS: Patient characteristics and echocardiographic findings were analyzed. Mortality odds ratio (OR) for right ventricular (RV) systolic dysfunction and acute cor pulmonale (ACP) was calculated. MAIN RESULTS: was 76 (65-95) and 98 (85-140) in ECMO and non-ECMO patients, respectively (p ≤ 0.001). Initial echocardiograms showed no significant differences in left ventricular systolic dysfunction (10% v 15 %, p = 0.31) and RV systolic dysfunction (38% v. 27%, p = 0.27) between ECMO and non-ECMO patients. ACP was more frequent in the ECMO group at baseline (41% v. 26 %, p = 0.02). During the ICU stay, patients on ECMO exhibited a higher prevalence of RV systolic dysfunction (55% v 34%, p = 0.001) and ACP (51% v 26%, p = 0.002). RV systolic dysfunction (OR 1.99; 95% CI 1.09-3.63) and ACP (OR 2.95; 95% CI 1.55-5.62) on the follow-up echocardiograms were associated with higher odds of ICU mortality. CONCLUSIONS: The prevalence of echocardiographic abnormalities, in particular RV dysfunction, was frequent among patients with COVID-19 receiving VV ECMO support and was associated with worse clinical outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".