Point-of-Care Ultrasound for the Detection of Vascular Access Site Complications—The ULTRASITCOM Study
Bibliographic record
Abstract
Background Recent technological advancements have expanded access to ultrasound technology. Invasive cardiac procedures come with risks of vascular access complications, necessitating efficient detection methods for dangerous complications such as pseudoaneurysms. Current clinical practice has relied on physical examination, and often requires formal diagnostic imaging to diagnose these complications. The ULTRAsound Assessment of Access SITe COMplications study assessed the diagnostic accuracy of point-of-care ultrasound (POCUS) as an adjunct to physical examination for the detection of pseudoaneurysms following invasive cardiac procedures. Methods We conducted a single-center study that enrolled patients who underwent invasive cardiovascular procedures with suspected access site complications. Cardiology fellows were trained on the use of POCUS by a radiologist with expertise in vascular imaging. The primary outcome focused on the diagnostic odds ratio (DOR) of combined clinical and POCUS assessments compared to Doppler ultrasound or computed tomography. Results Among 111 participants, most were female (59.5%), with a mean age of 72.2 years, and with transfemoral access being most prevalent (67.6%). A total of 15 participants were found to have a pseudoaneurysm on formal diagnostic imaging. The combined clinical and POCUS assessments were highly sensitive and demonstrated superior DOR for detecting pseudoaneurysms compared to the physical examination alone (DOR 42.6 [95% CI, 34.6-50.6] vs 15.6 [95% CI, 11.7-19.5]; P < .01). Conclusions Point-of-care ultrasound is a highly sensitive tool for detecting pseudoaneurysms following invasive cardiovascular procedures. These findings suggest the potential integration of POCUS into routine practice, which could result in timely complication identification and management, thereby improving patient outcomes and reducing health care costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".