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Enregistrement W3021060057 · doi:10.1093/ntr/ntaa075

Smoking Cessation During the COVID-19 Epidemic

2020· article· en· W3021060057 sur OpenAlexaff
S. Eisenberg, Mark J. Eisenberg

Notice bibliographique

RevueNicotine & Tobacco Research · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensMcGill UniversityJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésCoronavirus disease 2019 (COVID-19)Smoking cessation2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineBetacoronavirusPandemicEnvironmental healthVareniclineVirologyOutbreakInternal medicineDisease

Résumé

récupéré en direct d'OpenAlex

The COVID-19 epidemic presents a unique public health opportunity for smoking cessation. Smokers are at a higher risk of developing COVID-19 and are also at a higher risk of developing severe COVID-19 complications.1 Although there are no data available regarding the benefits of smoking cessation during the COVID-19 epidemic, there is evidence to suggest that smoking cessation for 4 weeks or more will lessen the risk of developing COVID-19 as well as the risk of developing severe COVID-19 complications. Both smoking and COVID-19 affect the respiratory system. Smoking is known to increase the risk of lung cancer (relative risk [RR] 10.92; 95% confidence interval [CI] 8.28–14.20), chronic obstructive pulmonary disease (RR 4.01; 95% CI 3.18–5.05), and asthma (RR 1.61; 95% CI 1.07–2.42).2 In a study examining respiratory syncytial virus, a virus similar to SARS-CoV-2, it was shown that cigarette smoke causes necrosis of airway epithelial cells and prevents viral-induced apoptosis. Apoptosis usually limits viral replication and inflammation. However, when it is replaced with necrosis, both viral replication and inflammation are enhanced, leading to an increased susceptibility of acquiring viral infections.3 Furthermore, smokers often have more hand-to-face movements (compared with nonsmokers), making viral transmission more probable. Smokers also have an increased risk of developing severe complications once they become infected with SARS-CoV-2.1 A recent systematic review examined five studies that analyzed the smoking status of patients during the COVID-19 epidemic in China. The size of the patient population in all of these studies ranged from 41 to 1099 and the studies only included patients who were COVID-19 positive.1 The authors concluded that smokers (compared with nonsmokers) were 1.4 (RR 1.4; 95% CI 0.98–2.00) times more likely to suffer from severe symptoms of COVID-19. They were also 2.4 (RR 2.4; 95% CI 1.43–4.04) times more likely to be placed in the intensive care unit, require mechanical ventilation, or die.1 Another recent Chinese study published in the Lancet, compared the incidence of severe COVID-19 symptoms in 52 critically ill patients admitted to the intensive care unit. Comparing smokers to nonsmokers, 26 (81%) versus 9 (45%) had acute respiratory distress syndrome, 30 (94%) versus 7 (35%) required mechanical ventilation, 15 (29%) had heart failure, and 12 (23%) had kidney failure.4 Smokers are therefore more likely to acquire SARS-CoV-2 and are more likely to have adverse outcomes once the infection is acquired. Although there are limited data available, studies from the surgical literature suggest that even 4 weeks of smoking cessation may decrease the risk of adverse outcomes and intubation associated with COVID-19.5 In a study published in the Canadian Journal of Anesthesia in 2012, the authors conducted a systematic review and meta-analysis of 25 studies on short-term preoperative smoking cessation and postoperative complications. The authors of this study identified that at least 4 weeks of smoking cessation lowers the risk of respiratory complications compared with current smokers (RR 0.77; 95% CI 0.61–0.96 and RR 0.53; 95% CI 0.37–0.76).5 In another surgical study examining over 600 000 noncardiac surgical patients, current smokers had a higher likelihood of 30-day mortality (RR 1.38; 95% CI 1.11–1.72) and a higher incidence of postoperative complications such as surgical site infection (odds ratio [OR] 1.30; 95% CI 1.80–2.43), pneumonia (OR 2.09; 95% CI 1.80–2.43), unplanned intubation (OR 1.87; 95% CI 1.58–2.21), and septic shock (OR 1.55; 95% CI 1.29–1.87).6 Thus, based on data from the surgical literature, there is reason to conclude that 4 weeks of smoking cessation will be associated with a lower incidence of adverse events and intubation among COVID-19 patients. Physicians can play a crucial role in helping smokers quit smoking during the COVID-19 epidemic. Physicians can use telemedicine to advise their patients regarding smoking cessation and to recommend pharmacotherapy. In a study published in the Lancet in 2016, varenicline was shown to be the most effective pharmacotherapy for smoking cessation followed by bupropion and the nicotine patch.7 In this 12-week study of 8144 participants, patients treated with varenicline had better abstinence rates compared with those on placebo (OR 3.61; 95% CI 3.07–4.24), those using the nicotine patch (OR 1.68; 95% CI 1.46–1.93), and those using bupropion (OR 1.75; 95% CI 1.52–2.01). In addition, participants placed on bupropion and the nicotine patch achieved higher smoking cessation rates compared with those on placebo (OR 2.07; 1.75–2.45 and OR 2.15; 95% CI 1.82–2.54).7 The primary endpoint in the study was confirmed smoking cessation for weeks 9–12.7 These medications can be prescribed by the patient’s physician and can be delivered to their homes from the nearest pharmacy. Finally, physicians should also make their patients aware of behavioral therapy hotlines for smoking cessation. The National Cancer Institute offers these services on their website (smokefree.gov). Smoking cessation is likely to reduce the risk of developing COVID-19 as well as the likelihood of developing severe COVID-19 complications. For this reason, physicians should advise their patients to stop smoking immediately. A Contributorship Form detailing each author’s specific involvement with this content, as well as any supplementary data, are available online at https://academic.oup.com/ntr. None. None declared.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,108
Score d'incertitude au seuil0,215

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,011
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0020,001
Communication savante0,0030,002
Science ouverte0,0010,003
Intégrité de la recherche0,0020,004
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

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.

Tête enseignante Opus0,364
Tête enseignante GPT0,513
Écart entre enseignants0,149 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations49
Publié2020
Routes d'admission1
Résumé présentnon

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