Simultaneous Venous-Arterial Doppler Ultrasound During Early Fluid Resuscitation to Characterize a Novel Doppler Starling Curve: A Prospective Observational Pilot Study
Bibliographic record
Abstract
Background: The likelihood of a patient being preload responsive—a state where the cardiac output or stroke volume (SV) increases significantly in response to preload—depends on both cardiac filling and function. This relationship is described by the canonical Frank-Starling curve. Research Question: We hypothesize that a novel method for phenotyping hypoperfused patients (ie, the “Doppler Starling curve”) using synchronously measured jugular venous Doppler as a marker of central venous pressure (CVP) and corrected flow time of the carotid artery (ccFT) as a surrogate for SV will refine the pretest probability of preload responsiveness/unresponsiveness. Study Design and Methods: We retrospectively analyzed a prospectively collected convenience sample of hypoperfused adult emergency department (ED) patients. Doppler measurements were obtained before and during a preload challenge using a wireless, wearable Doppler ultrasound system. Based on internal jugular and carotid artery Doppler surrogates of CVP and SV, respectively, we placed hemodynamic assessments into quadrants (Q x ) prior to preload augmentation: low CVP with normal SV (Q 1 ), high CVP and normal SV (Q 2 ), low CVP and low SV (Q 3 ) and high CVP and low SV (Q 4 ). The proportion of preload responsive and unresponsive assessments in each quadrant was calculated based on the maximal change in ccFT (ccFT Δ ) during either a passive leg raise or rapid fluid challenge. Results: We analyzed 41 patients (68 hemodynamic assessments) between February and April 2021. The prevalence of each phenotype was: 15 (22%) in Q 1 , 8 (12%) in Q 2 , 39 (57%) in Q 3 , and 6 (9%) in Q 4 . Preload unresponsiveness rates were: Q 1 , 20%; Q 2 , 50%; Q 3 , 33%, and Q 4 , 67%. Interpretation: Even fluid naïve ED patients with sonographic estimates of low CVP have high rates of fluid unresponsiveness, making dynamic testing valuable to prevent ineffective IVF administration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".