Exploring the clinical benefit of ventilation therapy across various patient groups with COVID-19 using real-world data
Notice bibliographique
Résumé
Scarcity of ventilators during COVID-19 pandemic has urged public health authorities to develop prioritization recommendations and guidelines with the real-time decision-making process based on the resources and contexts. Nevertheless, patients with COVID-19 who will benefit the most from ventilation therapy have not been well-defined yet. Thus, the objective of this study was to investigate the benefit of ventilation therapy among various patient groups with COVID-19 admitted to hospitals, based on the real-world data of hospitalized adult patients. Data used in the longitudinal study included 599,340 records of hospitalized patients who were admitted from February 2020 to June 2021. All participants were categorized based on sex, age, city of residence, the hospitals' affiliated university, and their date of hospitalization. Age groups were defined as 18-39, 40-64, and more than 65-year-old participants. Two models were used in this study: in the first model, participants were assessed by their probability of receiving ventilation therapy during hospitalization based on demographic and clinical factors using mixed-effects logistic regression. In the second model, the clinical benefit of receiving ventilation therapy among various patient groups was quantified while considering the probability of receiving ventilation therapy during hospital admission, as estimated in the first model. The interaction coefficient in the second model indicated the difference in the slope of the logit probability of recovery for a one-unit increase in the probability of receiving ventilation therapy between the patients who received ventilation compared to those who did not while considering other factors constant. The interaction coefficient was used as an indicator to quantify the benefit of ventilation reception and possibly be used as a criterion for comparison among various patient groups. Among participants, 60,113 (10.0%) cases received ventilation therapy, 85,158 (14.2%) passed away due to COVID-19, and 514,182 (85.8%) recovered. The mean (SD) age was 58.5 (18.3) [range = 18-114, being 58.3 (18.2) among women, and 58.6 (18.4) among men]. Among all groups with sufficient data for analysis, patients aged 40-64 years who had chronic respiratory diseases (CRD) and malignancy benefitted the most from ventilation therapy; followed by patients aged 65 + years who had malignancy, cardiovascular diseases (CVD), and diabetes (DM); and patients aged 18-39 years who had malignancy. Patients aged 65 + who had CRD and CVD gained the least benefit from ventilation therapy. Among patients with DM, patients aged 65 + years benefited from ventilation therapy, followed by 40-64 years. Among patients with CVD, patients aged 18-39 years benefited the most from ventilation therapy, followed by patients aged 40-64 years and 65 + years. Among patients with DM and CVD, patients aged 40-64 years benefited from ventilation therapy, followed by 65 + years. Among patients with no history of CRD, malignancy, CVD, or DM, patients aged 18-39 years benefited the most from ventilation therapy, followed by patients aged 40-64 years and 65 + years. This study promotes a new aspect of treating patients for ventilators as a scarce medical resource, considering whether ventilation therapy would improve the patient's clinical outcome. Should the prioritization guidelines for ventilators allocation take no notice of the real-world data, patients might end up being deprived of ventilation therapy, who could benefit the most from it. It could be suggested that rather than focusing on the scarcity of ventilators, guidelines focus on evidence-based decision-making algorithms to also take the usefulness of the intervention into account, whose beneficial effect is dependent on the selection of the right time in the right patient.
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 | 0,009 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».