Carotid Artery Corrected Flow Time Measured by Wearable Doppler Ultrasound Detects Stroke Volume Change Measured by Transesophageal Echocardiography After Coronary Artery Bypass Grafting
Bibliographic record
Abstract
Background: As a measure of preload responsiveness (PR), change in carotid artery corrected flow time (ccFTΔ) is a surrogate for change in stroke volume (SVΔ). However, the optimal threshold and accuracy of ccFTΔ to detect SVΔ are inconsistent in previous reports. Research Question: Does ccFTΔ from a wireless, wearable Doppler ultrasound accurately detect a 10% SVΔ measured by transesophageal echocardiography? Study Design and Methods: This was a prospective, single-center study of adult patients after elective coronary artery bypass grafting. PR was defined as ≥ 10% augmentation in transesophageal echocardiography left ventricular outflow tract velocity time integral (as a surrogate for SVΔ) during Trendelenburg positioning. Synchronous carotid Doppler imaging was captured by a wireless, wearable Doppler ultrasound. The optimal ccFTΔ threshold to detect PR, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Linear correlation between ccFTΔ and SVΔ was assessed by Pearson correlation coefficient. We also evaluated the effect of the number of consecutively averaged cardiac cycles on ccFTΔ accuracy. Results: This analysis included 30 patients; 7 patients showed a ≥ 10% SVΔ during Trendelenburg positioning. The optimal ccFTΔ thresholds were +6.6 ms or 2.2% with sensitivities of 100%, specificities of 70%, and AUCs of 0.89 and 0.88, respectively. A strong, linear correlation between ccFTΔ and SVΔ was found (r = 0.70; P < .001). The mean AUC increased from 0.68 to 0.87 when using 1 vs 20 consecutively averaged cardiac cycles. Interpretation: After cardiopulmonary bypass, ccFTΔ measured by wireless, wearable ultrasound detected SVΔ during Trendelenburg positioning with high accuracy. The AUC improved as a function of consecutively averaged cardiac cycles. As a surrogate for preload-induced SVΔ, ccFTΔ can direct fluid therapy in the postoperative period.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".