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)
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.088 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".