Abstract 12774: Detecting Change in Cardiopulmonary Bypass Blood Flow Rate With a Novel, Wireless, Wearable Doppler Ultrasound Patch: A Pilot Study
Bibliographic record
Abstract
Introduction: Assessing preload reserve (PR) is important when managing cardiac surgery patients. Typically, a 10% change in cardiac output is used as a reference standard to gauge PR; how this change in cardiac output translates to common carotid artery (CCA) velocity is unknown. In this pilot study, we compared known changes in cardiopulmonary bypass (CPB) flow rate to those measured by a novel wireless, wearable Doppler ultrasound placed over the CCA. Methods: A sample of 14 adult patients undergoing elective CABG was studied. While on CPB, at least two changes in blood flow (one negative, one positive) were made. The corresponding change in CCA velocity was measured by a wearable continuous wave (CW) Doppler ultrasound patch. For each subject, CPB flow changes were determined from the displayed spectrogram by the peristaltic pulsation frequency and CCA velocity changes were calculated from the automated maximum velocity trace. 5-10s windows pre- and post-CPB speed changes were selected, with sufficient signal quality to determine both relative CPB flow and CCA velocity. A “dose-response” curve was fitted to the relative changes in flow and velocity for each subject, as shown in Fig. 1, and the CCA velocity change corresponding to a 10% change in CPB flow was calculated. Results: CPB flows ranged from 0.5-6.3L/min. Mean relative flow changes were -58 and 179%, corresponding to mean CCA velocity changes of -43 and 107%. A mean CCA velocity change of 14% was found to correspond to 10% CPB flow change, ranging from 1.5-27.8%. Conclusions: CCA velocity measured with the Doppler patch successfully tracked changes in CPB flow. A CCA velocity change of >14% suggests a >10% change in cardiac output. Although preliminary, these results provide another important parameter for predicting the response to preload using a novel, easy-to-use, wireless Doppler device.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".