Abstract WMP100: Multicenter Validation Study of Robot-Assisted Transcranial Doppler (raTCD) for Enhanced Right-to-Left Shunt Detection Compared to Transthoracic and Transesophageal Echocardiography
Bibliographic record
Abstract
Background: Cryptogenic strokes account for approximately 30-40% of all stroke cases, underscoring the critical need for effective identification of right-to-left shunt (RLS) and PFO. The recently published BUBL Study (NCT04604015), a multicenter, prospective trial, demonstrated a 3-fold increase in the detection of RLS/PFO using raTCD compared TTE. Wechsler recognized the study's important findings in an accompanying editorial, while also highlighting the need for further validation through additional studies. This study aims to provide robust real-world data to further substantiate the findings of the BUBL Study. Methods: This study involved a multicenter retrospective analysis of prospectively collected real-world clinical data across 5 centers in the US and Canada. The inclusion criterion was any patient who underwent raTCD for RLS detection as part of their stroke evaluation. A standardized TCD, TTE, and TEE bubble study protocol was consistently applied. Key outcomes include overall and large (Spencer Grade ≥3) RLS detection rates for raTCD, detection rates comparison between matched raTCD, TTE, and TEE. Results: A total of 1,372 patients underwent raTCD, with 455 and 114 had matched TTE and TEE respectively. The overall cohort had a mean age of 55.5 ± 13.3 yrs, with 44% female. Using raTCD, RLS was detected in 54% (735/1,372) and large RLS in 28% (383/1,372) of the overall population. When compared with TTE (n=455), raTCD identified 1.7 times more cases of RLS/PFO (56.7% vs. 33.2%, p<0.001 – Table 1). For all and large RLS detected by raTCD, 27.4% (125/455) and 12.3% (56/455), respectively were negative on TTE. Comparing TEE and raTCD, 14.9% (17/114) were negative on TEE but positive on raTCD compared to 5.3% (6/114) which were positive on TEE and negative on raTCD. Overall, using TEE as the “gold standard” raTCD reported a SEN of 92.1%. For the overall population (n=1,372) TTE and TEE status was unknown for 917 patients and will subsequently be investigated. Conclusions: This study represents the largest real-world study to date, validating raTCD against traditional diagnostic modalities. The study showed that raTCD detected 1.7 times the number of positive RLS/PFO compared to TTE, revealing the limitations of TTE as a screening modality for PFO in cryptogenic stroke. These results build upon the prospective multicenter BUBL Study reinforcing the imperative to incorporate raTCD into a new diagnostic algorithm for cryptogenic stroke workup.
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".