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Enregistrement W3030808886 · doi:10.1002/nop2.525

The critical bedside role in identifying and treating lung injury during the COVID‐19 pandemic

2020· editorial· en· W3030808886 sur OpenAlexaboutno aff
Deanna L. Johnson, Joshua Parreco

Notice bibliographique

RevueNursing Open · 2020
Typeeditorial
Langueen
DomaineMedicine
ThématiqueRespiratory Support and Mechanisms
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésARDSMedicineLungDiffuse alveolar damageRespiratory distressIntensive care medicineAnesthesiaAcute respiratory distressInternal medicine

Résumé

récupéré en direct d'OpenAlex

Most early deaths from COVID-19 were from adult respiratory distress syndrome (ARDS) that led to multiorgan system failure (Arentz et al., 2020). COVID-19 primarily injures the vascular endothelium in such a unique way that a COVID-19 patient with ARDS (CARDS) can even die if they are young and healthy. Patients with ARDS develop stiff lungs that are difficult to ventilate without causing ventilator-induced lung injury (VILI). Through a series of clinical trials known as ARDSNet, that spanned over 20 years, clinicians were able to identify the ideal ventilator settings necessary to treat these patients. The trials revealed that low tidal volumes with high positive end-expiratory pressure resulted in less injury from the ventilator (VILI) (Acute Respiratory Distress Syndrome Network et al., 2000). More recently, another component to lung damage in the progression to ARDS has been described. In 2017, Brochard et al, in a collaboration between centres in Canada and Italy, first identified the concept known as patient self-inflicted lung injury (P-SILI) (Brochard, Slutsky, & Pesenti, 2017). This is where an initial lung injury causes capillary leak, lung oedema and impaired gas exchange. This leads to increased respiratory drive and higher tidal volumes from the patient's own spontaneous breaths. This causes more capillary leak and further damage to the lungs in a similar way that a ventilator can cause damage to lungs through VILI. After the initial onset of respiratory distress from COVID, the patient's lungs will be soft and easy to spontaneously ventilate despite very poor oxygenation (Grasselli et al., 2020). If the mechanism of P-SILI is kept in mind, the logical treatments become apparent. The patient should not be forcefully breathing, and the patient should not have a high cardiac output. The initial approach to treating the respiratory distress through non-invasive support (i.e. high-flow nasal oxygen) and patient discomfort through analgesics or anxiolytics may help by preventing excessive inspiratory efforts. If the respiratory drive cannot be reduced, persistently strong spontaneous inspiratory efforts will lead to worsening lung damage through P-SILI and eventually CARDS (Marini & Gattinoni, 2020). If this process cannot be interrupted, it may be necessary to intubate and mechanically ventilate these patients. Rates of agitation in ICU patients have been reported to be as high as 70% (Fraser, Prato, Riker, Berthiaume, & Wilkins, 2000). Deep sedation and paralysis by neuromuscular blocking agents may be necessary to prevent the high pressures that can result in VILI from patients who are “fighting the vent”. Communication difficulties, family absence and ventilator weaning have been identified as key components of the psychological toll that critical illness can take on these patients (Rotondi et al., 2002). Liberation from the ventilator and eventual extubation can be difficult in patients suffering from CARDS due to limitations placed on visitation and the required personal protective equipment for caregivers. Nurses provide a vital bedside role through reliable interpretation and management of anxiety and agitation during times of both aggressive ventilator support and weaning (Tate, Devito Dabbs, Hoffman, Milbrandt, & Happ, 2012). Effective symptom management for anxiety and agitation is associated with many improvements in patient outcomes such as more ventilator-free days and shorter lengths of stay (Campbell & Happ, 2010). As the COVID-19 pandemic continues to unfold, the knowledge of the concepts of P-SILI and VILI is essential for bedside nurses. Adequate assessment of the levels of anxiety and agitation present in these patients is vital to prevent self-inflicted and iatrogenic lung injury. Nurses, that truly know the patient, are the eyes and ears for all other caregivers. It may be necessary to provide aggressive treatments that decrease the damage being done to the lungs through spontaneous breathing. Only the bedside nurse can provide the vital clues to balance the necessary support. Recognizing and treating these symptoms early could be the key to improving outcomes in patients with COVID-19 infections. The severity and breadth of this global pandemic must not sway or deter us from the basic tenets of bedside patient comfort and succour. The authors would like to thank the following individuals for their contributions to the critical review of the concepts discussed in this editorial: Rishi Rattan, MD, FACS; Gary Curcio, MD, FACS; and Kenneth Stahl, MD, FACS. None. The authors of this editorial contributed equally.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,163
Score d'incertitude au seuil0,780

Scores Codex et Gemma par catégorie

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

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 Opus0,042
Tête enseignante GPT0,408
Écart entre enseignants0,366 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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