Echocardiographic Characteristics of Subjects With COVID-19: A Case Series
Bibliographic record
Abstract
Although coronavirus disease 2019 (COVID-19) manifests in most cases with respiratory symptoms, other presentations can occur. Direct damage to the cardiovascular system has been reported and recently, acute myocardial injury has been identified as a risk factor for mortality. Transthoracic echocardiography is a non-invasive tool that allows the detection of myocardial damage with validated markers (left ventricular ejection fraction and global longitudinal strain). Herein, we present the echocardiographic findings in four patients with COVID-19. All cases had acute respiratory distress syndrome (100%). Three out of four had elevated levels of creatine kinase and creatine kinase myocardial band. One case had ventricular concentric remodeling (25%). All cases (100%) had altered ventricular function: two had a reduced ejection fraction (50%) and, of those available for global longitudinal strain analysis, all had abnormal global longitudinal strain (100%). One case was found to have a tricuspid vegetation of 12 × 10 mm with no other manifestation of endocarditis. All of our cases had left ventricular dysfunction as assessed by echocardiography. One of our patients had a vegetation in the tricuspid valve. Two of our cases had a reduced ejection fraction. The importance of acute cardiac injury in COVID-19 has recently been established. A recent study found it to be an independent risk factor for mortality in patients with this disease. Information regarding echocardiographic characteristics of this population is scarce. Further research to elucidate the impact of these characteristics on morbidity and mortality is urgently needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".