Myocardial Involvement in COVID-19: an Interaction Between Comorbidities and Heart Failure with Preserved Ejection Fraction. A Further Indication of the Role of Inflammation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 it