Practice patterns in extracorporeal life support in tertiary cardiac intensive care units in north america: a survey-based analysis from the critical care cardiology trials network
Notice bibliographique
Résumé
Abstract Funding Acknowledgements Type of funding sources: None. Background The use of veno-arterial (VA) and veno-venous (VV) extracorporeal membrane oxygenation (ECMO) has significantly increased in the last decade. However, there is substantial variability in practice patterns between institutions. To date, the optimal ECMO program model is unclear and possible differences between ECMO centers in staffing, organization and team structure have been poorly characterized. Purpose Our aim was to describe contemporary practices of care for patients undergoing ECMO in tertiary cardiac centers in North America. Methods An 11-question anonymous survey was sent to all participating sites in the Critical Care Cardiology Trials Network (CCCTN), a prospective registry of advanced cardiac intensive care units (CICUs) in North America, coordinated by the TIMI Study Group. The survey evaluated ECMO staffing models, decision-making processes, cannulation and longitudinal care. Results The response rate was 100% (39/39) across CCCTN centers. The decision to proceed with VA ECMO was made as a team in 79% of the cases and in 58.3% of the VV ECMO cases, rather than by an individual specialty. An ECMO consult service was used in 67% of the centers, and integrated with the cardiogenic shock team in 58%. The most common specialty participating in the ECMO service was cardiothoracic surgery (CTS) (73.1%), followed by heart failure specialists (38.5%), with critical care cardiology (CCC) in only 23.1% of the centers (Figure 1A). In the majority of centers, VA ECMO cannulation was performed by CTS alone (46.2%), Interventional cardiology (IC) and CTS (38.5%), and IC alone (7.7%, Figure 1B). Cannulation for VA ECMO was not performed by intensivists at any center. VV ECMO cannulations were performed by CTS in 51.3%, intensivists in 17.9% and IC in 2.6% of the centers. VA ECMO patients were admitted to the cardiovascular surgical intensive care unit in 64.1% of centers, with 20.5 % admitted to the CICU (Figure 1C). VV ECMO patients were admitted to the cardiovascular surgical intensive care unit in 36.8% of sites, and the medical intensive care unit in 34.2%. The most common ECMO specialist model was having a perfusionist at the bedside, followed by nurse specialist (Figure 1D). Specialty services consulted within 24-48 hours of cannulation included physical therapy in 48.7%, palliative care in 30.8%, and bioethics in only 2.6% of centers. Simulation-based ECMO training is performed in 59% of the centers. Half (51.3%) of the centers perform ECPR, while 17.9% reported plans of developing a program. Conclusions Although there is significant variability in ECMO cannulation, location of care, staffing practices in advanced CICUs in North America, a multidisciplinary team approach is common in ECMO centers, with early involvement of physical therapy and palliative care specialists. Further research to establish the optimal model for ECMO programs is of high importance.
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 | 0,006 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».