June 2020 at a Glance: Focus on COVID-19, Quality of Life and Comorbidities
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,098 | 0,023 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».