Myocardial Involvement in COVID-19: an Interaction Between Comorbidities and Heart Failure with Preserved Ejection Fraction. A Further Indication of the Role of Inflammation
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Coronavirus Disease 2019 (COVID-19) and cardiovascular (CV) disease have a close relationship that emerged from the earliest reports. The aim of this review is to show the possible associations between COVID-19 and heart failure (HF) with preserved ejection fraction (HFpEF). RECENT FINDINGS: In hospitalized patients with COVID-19, the prevalence of HFpEF is high, ranging from 4 to 16%, probably due to the shared cardio-metabolic risk profile. Indeed, comorbidities including hypertension, diabetes, obesity and chronic kidney disease - known predictors of a severe course of COVID-19 - are major causes of HFpEF, too. COVID-19 may represent a precipitating factor leading to acute decompensation of HF in patients with known HFpEF and in those with subclinical diastolic dysfunction, which becomes overt. COVID-19 may also directly or indirectly affect the heart. In otherwise healthy patients, echocardiographic studies showed that the majority of COVID-19 patients present diastolic (rather than systolic) impairment, pulmonary hypertension and right ventricular dysfunction. Such abnormalities are observed both in the acute or subacute phase of COVID-19. Cardiac magnetic resonance reveals myocardial inflammation and fibrosis in up to the 78% of patients in the chronic phase of the disease. These findings suggest that COVID-19 might be a novel independent risk factor for the development of HFpEF, through the activation of a systemic pro-inflammatory state. Follow-up studies are urgently needed to better understand long-term sequelae of COVID-19 inflammatory cardiomyopathy.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".