Clinical Outcomes of COVID-19-Induced Acute Respiratory Distress Syndrome in Patients With Three Different Respiratory Support Modalities: A Retrospective Cohort Study
Notice bibliographique
Résumé
Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced acute respiratory failure and acute respiratory distress syndrome (ARDS) had a considerable impact on intensive care utilization and resource optimization. Multiple modalities for respiratory support were implemented during the COVID-19 pandemic with the main concern of being able to identify those patients at high risk of rapidly progressive respiratory failure, whom the early initiation of invasive respiratory support would, in particular, affect the outcome in comparison to the noninvasive management strategy. Objectives In our cohort study, we describe demographic characteristics, respiratory support modalities, and their relation to patient outcomes. Method Patients 18 years of age and older who were admitted to a tertiary center COVID-19-dedicated medical intensive care unit (MICU) in Qatar between March 2020 and May 2020 with a confirmed diagnosis of COVID-19 pneumonia were included in this study. Patients were divided into invasive or noninvasive, and those who required invasive strategy were subdivided into the early intubation group (patients who were intubated within 72 hours of intensive care unit (ICU) admission) and the late intubation group (patients who were intubated after 72 hours from ICU admission). The primary outcome was ICU and hospital mortality, and the secondary effects were the length of stay and mortality determinants. Results A total of 686 patients were admitted to the medical intensive care unit (MICU) during the study period. There were 222 (32.4%), 131 (19.1%), and 333 (48.5%) patients in the early, late, and not intubated groups, respectively. Compared to the late intubated group, the early intubated group had a higher proportion of males. Diabetes (39.8%) was the most common comorbidity, followed by hypertension (HTN) (36%) and heart disease (9.8%). The 30-day ICU and hospital mortality were significantly higher in the late intubated group compared to the early intubated group (30.5% versus 15.8%, and 30% versus 16.2%). The median ICU and hospital stay days in the total sample were 8 (interquartile range (IQR): 5-14) and 19 (IQR: 14-25), respectively. The mean estimates of 30-day ICU survival times for early intubated, late intubated, and not intubated groups were 25.14 (95% confidence interval (CI): 23.71, 26.57), 23.35 (95% CI: 21.63, 25.07), and 29.91 (95% CI: 29.74, 30.09) respectively. Conclusions In our study, the COVID-19 ARDS patients who required early invasive ventilatory support and in whom the physiological parameter was more severe (Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores) had a better outcome than the late intubation group. Age more than 60 years old, diabetes, hypertension, chronic kidney disease (CKD), and chronic liver disease (CLD) were the main predictor of mortality in total.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».