Aiding Chronic Obstructive Pulmonary Disease and Congestive Heart Failure Ultrasound-guided Management through Enhanced Point-of-Care Ultrasound (ACCUMEN-POCUS): Protocol for a Randomized Controlled Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND Hospital at home (HAH) programs offer acute care at home as a substitute for inpatient hospitalization, reducing healthcare costs while maintaining safety and quality of care. Despite point-of-care ultrasound’s (POCUS) validation in inpatient and emergency settings, its role in HAH care remains underexplored. Common conditions treated on medical HAH programs such as acute exacerbation of chronic obstructive pulmonary disease (AE-COPD), acute decompensated heart failure (ADHF), and pneumonia are highly amenable to the integration of POCUS into clinical decision making and have been proven to improve healthcare utilization outcomes. POCUS’ portability makes it ideal for use in HAH but its feasibility remains to be proven given the need for provider training and use in virtual settings where a non-physician practitioner is providing in-person care. OBJECTIVE This study evaluates the feasibility and clinical utility of remotely interpreted lung and inferior vena cava (IVC) POCUS acquired by Community Paramedics (CPs) to support real-time clinical decision-making for HAH patients with AE-COPD, ADHF, and pneumonia in Calgary, Canada. METHODS This randomized control trial (RCT) compared usual HAH care (control) to lung and IVC POCUS-enhanced HAH care (intervention). Handheld POCUS devices captured images, which were downloaded and securely shared using a cloud-based application. This enabled real-time image sharing among the clinical team, facilitating immediate decision-making by remote physicians. A mixed-methods approach will evaluate clinical outcomes, patients’ experience, healthcare utilization, and healthcare provider perceptions of POCUS integration. The primary outcome of the study is defined as length-of-stay for the index HAH admission. Quantitative analysis will assess clinical efficacy and healthcare resource use, while qualitative methods such as interviews and surveys will capture patient and provider experiences. RESULTS Study funding began in April 2022, with data collection having commenced in Dec 2023. Patient recruitment was finalized on December 31, 2024. The study included a three-month follow-up for significant outcomes and will include a one-year follow-up for long-term healthcare utilization, including admissions to long-term care. A total of 20 patients were enrolled (10 intervention, 10 control). Initial results highlighted feasibility and potential benefits of remotely-acquired POCUS imaging in HAH. Full data analysis is in progress. CONCLUSIONS This study is the first RCT to investigate virtually-acquired POCUS by non-physician practitioners for real-time lung and IVC remote decision-making in HAH care. Findings will provide insights into whether serial lung and IVC POCUS assessments improve ADHF, AE-COPD, and pneumonia outcomes in the HAH setting. The study will also enhance understanding of the value of POCUS integration from a provider perspective. By assessing its clinical impact and feasibility, this research may inform future guidelines for incorporating POCUS into home-based acute care, ultimately improving patient care and optimizing healthcare resource utilization. CLINICALTRIAL ClinicalTrials.gov NCT05423652
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 | 0,024 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,010 | 0,006 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,088 | 0,015 |
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 ».