Cardiovascular Collapse in COVID-19 Infection: The Role of Venoarterial Extracorporeal Membrane Oxygenation (VA-ECMO)
Bibliographic record
Abstract
Coronavirus Disease 2019 (COVID-19) has been associated with cardiovascular complications, including acute cardiac injury, heart failure, and cardiogenic shock (CS). The role of venoarterial extracorporeal membrane oxygenation (VA-ECMO) in the event of COVID-19-associated cardiovascular collapse has not been established. We reviewed the existing literature surrounding the role of VA-ECMO in the treatment of coronavirus-related cardiovascular collapse. COVID-19 is associated with a higher incidence of cardiovascular complications compared with previous coronavirus outbreaks (Severe Acute Respiratory Syndrome Coronavirus and Middle East Respiratory Syndrome Coronavirus). We found only 1 case report from China in which COVID-19-associated fulminant myocarditis and CS were successfully rescued using VA-ECMO as a bridge to recovery. We identified potential clinical scenarios (cardiac injury, myocardial infarction with and without obstructive coronary artery disease, viral myocarditis, and decompensated heart failure) leading to CS and risk factors for poor/uncertain benefit (age, sepsis, mixed/predominantly vasodilatory shock, prothrombotic state or coagulopathy, severe acute respiratory distress syndrome, multiorgan failure, or high-risk prognostic scores) specific to using VA-ECMO as a bridge to recovery in COVID-19 infection. Additional considerations and proposed recommendations specific to the COVID-19 pandemic were formulated with guidance from published data and expert consensus. A small subset of patients with cardiovascular complications from COVID-19 infection may progress to refractory CS. While accepting that resource scarcity may be the overwhelming concern for healthcare systems during this pandemic, VA-ECMO can be considered in highly selected cases of refractory CS and echocardiographic evidence of biventricular failure. The decision to initiate this therapy should take into consideration the availability of resources, perceived benefit, and risks of transmitting disease.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".