Heart Failure During the COVID-19 Pandemic: Clinical, Diagnostic, Management, and Organizational Dilemmas
Bibliographic record
Abstract
The coronavirus 2019 (COVID-19) infection pandemic has affected the care of patients with heart failure (HF). Several consensus documents describe the appropriate diagnostic algorithm and treatment approach for patients with HF and associated COVID-19 infection. However, few questions about the mechanisms by which COVID can exacerbate HF in patients with high-risk (Stage B) or symptomatic HF (Stage C) remain unanswered. Therefore, the type of HF occurring during infection is poorly investigated. The diagnostic differentiation and management should be focused on the identification of the HF phenotype, underlying causes, and subsequent tailored therapy. In this framework, the relationship existing between COVID and onset of acute decompensated HF, isolated right HF, and cardiogenic shock is questioned, and the specific management is mainly based on local hospital organization rather than a standardized model. Similarly, some specific populations such as advanced HF, heart transplant, patients with left ventricular assist device (LVAD), or valve disease remain under investigated. In this systematic review, we examine recent advances regarding the relationships between HF and COVID-19 pandemic with respect to epidemiology, pathogenetic mechanisms, and differential diagnosis. Also, according to the recent HF guidelines definition, we highlight different clinical profile identification, pointing out the main concerns in understudied HF populations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".