The Doppler shock index measured by a wearable ultrasound patch accurately detects moderate‐to‐severe central hypovolemia during lower body negative pressure
Bibliographic record
Abstract
Objective Moderate-to-severe hemorrhage is a life-threatening condition, which is challenging to detect in a timely fashion using traditional vital signs because of the human body's robust physiologic compensatory mechanisms. Measuring and trending blood flow could improve diagnosis of clinically significant exsanguination. A lightweight, wireless, wearable Doppler ultrasound patch that captures and trends blood flow velocity could enhance hemorrhage detection. Methods In 11 healthy volunteers undergoing simulated hemorrhage and resuscitation during graded lower body negative pressure (LBNP) and release, we studied the relationship between stroke volume (SV) and common carotid artery velocity time integral (VTI) and corrected flow time (FTc). We assessed the diagnostic accuracy of 2 variations of a novel metric, the Doppler shock index (ie, the DSIVTI and DSIFTc), at capturing moderate-to-severe central hypovolemia defined as a 30% reduction in SV. The DSIVTI and DSIFTc are calculated as the heart rate divided by either the VTI or FTc, respectively. Results A total of 17,822 cardiac cycles were analyzed across 22 LBNP protocols. The average SV reduction to the lowest tolerated LBNP stage was 40%; there was no clinically significant fall in the mean arterial pressure. Correlations between changing SV and the common carotid artery VTI and FTc were strong (R2 of 0.87, respectively) and concordant. The DSIVTI and DSIFTc accurately detected moderate-to-severe central hypovolemia with values for the area under the receiver operator curves of 0.96 and 0.97, respectively. Conclusion In a human model of hemorrhage and resuscitation, measures from a wearable Doppler ultrasound patch correlated strongly with SV and identified moderate-to-severe central hypovolemia with excellent diagnostic accuracy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".