Detecting the Change in Total Circulatory Flow with a Wireless, Wearable Doppler Ultrasound Patch: A Pilot Study
Bibliographic record
Abstract
Measuring fluid responsiveness is important in the management of critically ill patients, with a 10-15% change in cardiac output typically being used to indicate "fluid responsiveness." Ideally, these changes would be measured noninvasively and peripherally. The aim of this study was to determine how the common carotid artery (CCA) maximum velocity changes with total circulatory flow when confounding factors are mitigated and determine a value for CCA maximum velocity corresponding to a 10% change in total circulatory flow. DESIGN: Prospective observational pilot study. SETTING: Patients undergoing elective, on-pump coronary artery bypass grafting (CABG) surgery. PATIENTS: Fourteen patients were referred for elective coronary artery bypass grafting surgery. INTERVENTIONS: Cardiopulmonary bypass (CPB) pump flow changes during surgery, as chosen by the perfusionist. MEASUREMENTS: A hands-free, wearable Doppler patch was used for CCA velocity measurements with the aim of preventing user errors in ultrasound measurements. Maximum CCA velocity was determined from the spectrogram acquired by the Doppler patch. CPB flow rates were recorded as displayed on the CPB console, and further measured from the peristaltic pulsation frequency visible on the recorded Doppler spectrograms. MAIN RESULTS: Changes in CCA maximum velocity tracked well with changes in CPB flow. On average, a 13.6% change in CCA maximum velocity was found to correspond to a 10% change in CPB flow rate. CONCLUSIONS: Changes in CCA velocity may be a useful surrogate for determining fluid responsiveness when user error can be mitigated.
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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 | 0.000 | 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".