December 2020 at a Glance: Focus on COVID-19, Comorbidities and Palliative Care
Notice bibliographique
Résumé
The coronavirus disease 2019 (COVID-19) pandemic had a catastrophic impact on health systems worldwide. In Germany, hospitalizations due to acute heart failure (HF) had a reduction in the first pandemic period (13 March–21 May 2020) compared to control intervals in 2020 (1 January–12 March) and 2019 (13 March–21 May), with an increase in case severity and in-hospital mortality.1 Similarly, a study conducted in London showed a reduction in HF hospitalizations during the pandemic period, compared to the previous year. Although patients had similar characteristics to those of the patients hospitalized in 2019, in-hospital mortality was significantly higher in 2020 than in 2019 (P = 0.015) and hospitalization in 2020 was independently associated with increased in-hospital mortality.2 In a study conducted in Madrid, Spain, including 3080 COVID-19 patients, those with a previous history of chronic HF were more likely to develop acute HF (11.2% vs. 2.1%; P < 0.001) and had higher mortality rates (48.7% vs. 19.0%; P < 0.001).3 In an Italian study including 13 centres and 692 patients, history of HF was an independent predictor of increased in-hospital mortality [adjusted hazard ratio (HR) 2.25, 95% confidence interval (CI) 1.26–4.02; P = 0.006), along with age, sex, oxygen saturation and oxygen partial pressure/fraction of inspired oxygen (PaO2/FiO2) ratio.4 Concerns were raised that angiotensin-converting enzyme inhibitors (ACEi) or angiotensin II receptor blockers (ARBs) may enhance the susceptibility to COVID-19 through an increased expression of ACE2.5 Lebek et al.6 measured ACE2 mRNA expression using real-time quantitative polymerase chain reaction in atrial biopsies of 81 patients undergoing coronary artery bypass grafting. ACE2 mRNA expression was higher in patients treated with ACEi/ARBs compared to those not treated. Furthermore, ACEi/ARB treatment was associated with an independent increase in ACE2 expression in ventricle biopsies of patients with end-stage HF, while left ventricular (LV) assist device implantation led to a decline in mRNA expression. Although these data may show that ACE2 expression is increased by ACEi/ARB treatment, they do not imply that this may increase susceptibility to COVID-19 as ACE2 may actually have a protective role after infection has developed.5 A reduction of cardiac remodelling may be a therapeutic goal in HF with preserved ejection fraction (HFpEF).7 Yamanaka et al.8 investigated the prognostic impact of LV structural changes in patients with HFpEF from the Chronic Heart Failure Analysis and Registry in the Tohoku District-2 (CHART-2) study. Patients who presented LV hypertrophy and enlargement at baseline and those with LV hypertrophy who developed LV enlargement had a worse subsequent prognosis (HR 4.65, 95% CI 3.09–6.99, P < 0.001; HR 4.01, 95% CI 2.85–5.65, P < 0.001, respectively), compared with those without LV hypertrophy and enlargement. Cancer and HF have a close and complex relationship.9, 10 Common mechanistic pathways in cancer and HF, including inflammation, cellular metabolic changes, genetic predisposition, clonal haematopoiesis, angiogenesis, are summarized in this issue of the journal.11 Cooperation between cardiologists and oncologists is key to manage properly these patients.12 Zamorano et al.13 provided an overview of the main cardiovascular adverse events of cancer therapy and proposed strategies for risk assessment, prevention, early diagnosis, medical management and long-term follow-up of cardiotoxicity. Frailty is frequent among HF patients and is associated with poor outcome.14, 15 Available knowledge of cachexia, muscle wasting and physical frailty in patients with cardiovascular illness is reviewed by Bielecka-Dabrowa et al.16 Management of end-stage HF needs to include palliative care.17, 18 A position paper provides day-to-day practical clinical guidance on palliation strategies.19 Sahlollbey et al.20 provided a meta-analysis from 10 randomized trials showing that palliative care, compared with usual care, was associated with a reduction in HF hospitalization, and a modest, though significant, improvement of quality of life and symptoms in patients with advanced HF. Cardiac resynchronization therapy (CRT) in HF with reduced ejection fraction (HFrEF) is less used than indicated by guidelines and several studies tried to identify patients with major benefits from CRT.21-23 A position statement in this issue is aimed at overcoming CRT under-utilization, improving patient selection and implementing dedicated post-implant CRT care pathways.24 Kristensen et al.25 studied the prognostic role of intra-ventricular conduction delay in HFrEF patients without a cardiac device enrolled in two recent major clinical trials. They found that a wide QRS was associated with an increased risk of the primary composite outcome of cardiovascular death or HF hospitalization, irrespective of morphology. New-onset left bundle branch block occurred with an incidence of about 2.5% per year, leading to a greater risk of adverse outcomes.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
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 ».