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Record W3045492373 · doi:10.1002/ejhf.1515

June 2020 at a Glance: Focus on COVID-19, Quality of Life and Comorbidities

2020· article· en· W3045492373 on OpenAlexaff
Marianna Adamo, Carlo Lombardi, Marco Metra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineHeart failureCoronavirus disease 2019 (COVID-19)PandemicIntensive care medicineInternal medicineConcomitantSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Angiotensin-converting enzyme 2Angiotensin Receptor BlockersAngiotensin receptorDiseaseAngiotensin-converting enzymeCardiologyRenin–angiotensin systemBlood pressureInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Patients with heart failure (HF) are at increased risk of infections, and infections may precipitate acute HF and be a major cause of morbidity and mortality.1, 2 These threatening relationships have been magnified by the recent coronavirus disease 2019 (COVID-19) pandemic. Management of HF patients with concomitant COVID-19 is a major challenge. To support HF specialists in this difficult task, the Chinese HF Association and National HF Committee and the HF Association of the European Society of Cardiology developed a joint position paper summarizing current literature and clinical experience to give all possible guidance for the management of patients with HF and COVID-19 in clinical practice.3 The peculiar aspects of COVID-19 were integrated with the most recent findings for HF treatment.3, 4 The renin–angiotensin system has a pivotal role in HF.5 The angiotensin converting enzyme 2 (ACE2) is the receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was hypothesized that it may be upregulated by angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) treatment. Tomasoni et al.6 reviewed the relationships between HF and COVID-19 and the role of ACE2 and ACEi/ARB treatment in the pathophysiology and outcome of COVID-19 showing the lack of data supporting an untoward role of these drugs in COVID-19. Consistently, Bean et al.7 analysed a large series of HF patients hospitalized for COVID-19 and found a 37% reduction in the risk of death or need for transfer to a critical care unit in patients on ACEi or ARB, thus supporting the recommendation to continue treatment with these drugs also during the COVID-19 pandemic. The COVID-19 pandemic was associated with a dramatic decrease in the number of hospital admissions for acute cardiac emergencies.8 Also admissions for acute HF decreased during the COVID-19 outbreak despite no differences in HF severity, patients' care and pharmacological management compared with before.9 Telemonitoring therefore appears as the best solution for patients' follow-up.3 To date, patients' selection for telemonitoring has been difficult and use of natriuretic peptides was proposed.10 Galinier et al.11 report the results of a randomized, multicentre, open-label study investigating the role of telemedicine, compared to standard care, in preventing death or HF hospitalization (primary endpoint). The advantage of telemonitoring vs. standard care was noted only in patients with New York Heart Association class III/IV and in those with high adherence to body weight measurements. Quality of life (QOL) has become a major endpoint for HF studies.12-14 Butler et al.15 evaluated the association between changes in QOL scores and clinical improvement perceived by the patient, defined as minimal clinically important difference (MCID), in patients with chronic HF and iron deficiency treated with intravenous iron or placebo. They found that QOL scores were consistent although lower than MCID changes, suggesting that even small changes in QOL scores are clinically significant. Reddy et al.16 investigated the association of QOL reported by patients with HF and preserved ejection fraction (HFpEF) and HF severity defined according to aerobic capacity, exercise capacity, volume of daily activity, functional class, resting echocardiography, and plasma natriuretic peptide levels. QOL was found to be poorest in young, obese and diabetic patients. Poorer QOL was associated with worse physical capacity and activity levels, but was not associated with N-terminal pro B-type natriuretic peptide or echocardiographic parameters. Cardiac and non-cardiac comorbidities are associated with poor outcomes in HF patients.17, 18 Nevertheless, Khan et al.19 found that only 51% of HF with reduced ejection fraction (HFrEF) trials and 27% of HFpEF trials reported baseline comorbidities. The most common reported comorbidities were hypertension (63%), ischaemic heart disease (44%), hyperlipidaemia (48%), diabetes (33%), chronic kidney disease (25%) and atrial fibrillation (25%). Patients' heterogeneity is large in acute HF.20, 21 Bhatt et al.22 stratified 6945 patients with acute HF according to the number of non-cardiac comorbidities. The highest rate of all-cause death or HF hospitalization was observed in patients with four or more comorbidities. The relative risk of the composite endpoint increased with the number of comorbidities at both 30 days and 6 months. Higher comorbidity burden was associated with higher costs of care and length of stay.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.098
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0980.023

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.

Opus teacher head0.054
GPT teacher head0.306
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations5
Published2020
Admission routes1
Has abstractyes

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