COVID-19 management in patients with comorbid conditions
Notice bibliographique
Résumé
The novel coronavirus disease 2019 (COVID-19) causes serious respiratory illness and related disorders. Vulnerable populations, including those with chronic obstructive pulmonary disease, heart disease, diabetes, chronic kidney disease, obesity, and the elderly, face an increased risk of severe complications. As the pandemic evolves, various diagnostic techniques are available to detect severe acute respiratory distress syndrome (SARS-CoV-2), including clinical presentation, rapid antigen/antibody testing, molecular testing, supplemental laboratory analysis, and imaging. Based on peer-reviewed data, treatment options include convalescent plasma transfusion, corticosteroids, antivirals, and immunomodulatory medications. Convalescent plasma therapy, historically used in outbreaks like Middle East respiratory syndrome, Ebola, and SARS, is suggested by the World Health Organization for critically ill COVID-19 patients when vaccines or antiviral drugs are unavailable. Neutralizing antibodies in convalescent plasma help control viral load and improve patient outcomes, especially when administered early, though effectiveness varies. The United States Food and Drug Administration has authorized its emergency use for severe COVID-19 cases, but potential risks such as transfusion reactions and transfusion-related acute lung injury require further investigation to establish definitive efficacy. Antiviral agents like Remdesivir, an adenosine nucleotide analog, inhibit viral RNA polymerase and have shown efficacy in reducing COVID-19 severity, leading to its emergency use authorization for hospitalized patients. Other antivirals like ritonavir, lopinavir, and umifenovir disrupt viral replication and entry, but their effectiveness against SARS-CoV-2 remains under investigation. Dexamethasone, a corticosteroid, has been used in critically ill COVID-19 patients to reduce inflammation and prevent respiratory failure, as shown in the RECOVERY trial. Other immunosuppressants like ruxolitinib, baricitinib, and colchicine help modulate the immune response, reducing cytokine storms and inflammation-related complications. However, corticosteroids carry risks such as hyperglycemia, immunosuppression, and delayed viral clearance, requiring careful administration. Systematic reviews of clinical studies revealed that hydroxychloroquine with or without azithromycin did not decrease viral load nor reduce the severity of symptoms, but increased mortality among acutely hospitalized patients. There was no improvement in patients’ clinical conditions after 15 days compared to standard treatment. The United States Food and Drug Administration has revoked the authorization for the use of hydroxychloroquine in COVID-19 patients due to the null benefit-risk balance. Monoclonal antibodies like itolizumab, gimsilumab, sarilumab, and tocilizumab are being studied for their ability to reduce the severe inflammatory response in COVID-19 patients, particularly cytokine release syndrome and acute respiratory distress syndrome. These antibodies target specific immune pathways to decrease pro-inflammatory cytokines, with some showing promising results in clinical trials, though their use remains under investigation. The Clustered Regularly Interspaced Short Palindromic Repeats/Cas13 family of enzymes, sequenced from many COVID-19-positive patients, can potentially inhibit SARS-CoV-2 replication, cleave the RNA genome, and aid in the amplification of the genome assay. Cas13 can also target emerging pathogens via an adeno-associated virus vector when delivered to the infected lungs. In addition to pharmacological agents, vaccines effectively prevent symptomatic infection, reduce hospitalizations, minimize mortality rates, and ultimately reduce the severity of the disease. This paper aims to explore the management of patients with underlying conditions who present with COVID-19 to lessen the burden on healthcare systems.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».