Weiping Li, Connor M. Bunch, Sufyan Zackariya, Shivani S. Patel, Hallie Buckner, Shaun Condon, Matthew Walsh, Joseph Miller, Mark Walsh, Timothy L. Hall, Jionghua Jin, Jan P. Stegemann, Cheri X. Deng
6 min
Rapid and accurate assessment of hemostasis is critical for managing life-threatening hemorrhage, yet current clinical assays like prothrombin time (PT) or the von Clauss fibrinogen assay are often too slow or limited in scope. This study investigates whether Resonant Acoustic Rheometry (RAR)—an ultrasound-based technique that measures the viscoelastic properties of soft biomaterials—can serve as a rapid, sensitive, and cost-effective point-of-care assay to guide transfusion decisions in bleeding patients.
The researchers conducted a retrospective observational study using plasma samples from 38 hospitalized patients with various conditions, including trauma, surgery, and liver disease. They compared RAR measurements against standard clinical tests, including Thromboelastography (TEG) and the Clauss fibrinogen assay. RAR was used to measure resonant surface waves in plasma samples under nine different reagent conditions (varying calcium, kaolin, tissue factor, and thrombin). Finally, the team trained a quadratic classifier on selected RAR parameters to predict the need for fresh frozen plasma (FFP) and cryoprecipitate (CRYO) transfusions.
RAR successfully captured the dynamic transition of plasma from liquid to solid, with parameters such as Coagulation Start Time, Duration, and Final Resonant Frequency (FRF) reflecting the mechanical changes during clotting. RAR parameters showed significant correlations with standard TEG metrics and the Clauss fibrinogen assay, particularly when data were stratified by patient comorbidities like diabetes or cirrhosis. Furthermore, a machine learning model using RAR data achieved high accuracy in predicting the need for FFP and CRYO transfusions, suggesting that RAR could provide actionable data for personalized resuscitation.
Existing viscoelastic hemostatic assays (VHAs) like TEG and ROTEM are valuable but often expensive, limited in throughput, and not universally available. RAR offers a potential alternative that utilizes standard 96-well labware and low-cost ultrasound systems. By providing rapid, quantitative insights into clot strength and coagulation kinetics, RAR could help clinicians move away from empiric blood product administration toward more precise, goal-directed therapy for hemorrhaging patients.
Disordered hemostasis associated with life-threatening hemorrhage commonly afflicts patients in the emergency department, critical care unit, and perioperative settings. Rapid and sensitive hemostasis phenotyping is needed to guide administration of blood components and hemostatic adjuncts to reverse aberrant hemostasis. Here, we report the use of resonant acoustic rheometry (RAR), a technique that quantifies the viscoelastic properties of soft biomaterials, for assessing plasma coagulation in a cohort of 38 bleeding patients admitted to the hospital. RAR captured the dynamic characteristics of plasma coagulation that were dependent on coagulation activators or reagent conditions. RAR coagulation parameters correlated with TEG reaction time and TEG functional fibrinogen, especially when stratified by comorbidities. A quadratic classifier trained on selective RAR parameters predicted transfusion of fresh frozen plasma and cryoprecipitate with modest to high overall accuracy. While these results demonstrate the feasibility of RAR for plasma coagulation and utility of a machine learning model, the relative small number of patients, especially the small number of patients who received transfusion, is a limitation of this study. Further studies are need to test a larger number of patients to further validate the capability of RAR as a cost-effective and sensitive hemostasis assay to obtain quantitative data to guide clinical-decision making in managing severely hemorrhaging patients.
Sam: Because coagulation is a non-linear system. A patient's comorbidities—cirrhosis, diabetes, anticoagulant use—modulate how their clot responds to reagents. A linear cut-off treats all that variation as noise. The quadratic classifier integrates multiple mechanical inputs to map onto clinical outcomes, which is why correlations were stronger when the authors stratified by underlying condition. In those subgroups, the coagulopathy is more uniform and the mechanical signal is cleaner.
Alex: That also explains why the Final Resonant Frequency tracked fibrinogen so closely in those patients specifically.
Sam: Precisely. And there's a mechanistic reason to expect it. RAR uses plasma, not whole blood, so it's measuring the fibrin meshwork without the platelet and erythrocyte contribution that usually complicates whole-blood viscoelastic tests. That's actually an advantage for isolating fibrinogen activity—but it's also the primary trade-off.
Alex: Right—if you're missing platelets and red cells, you're missing clot contraction. That's a real gap for trauma, where the cellular contribution matters. [[RP_SECTION:limitations-and-clinical-context|Limitations and Clinical Context]]
Sam: It is. The plasma-only design is the central limitation for translating this to a trauma bay. What you gain is compatibility with standard 96-well plates and a high-throughput format that makes RAR scalable. What you lose is the full hemostasis phenotype. It's a surrogate for whole-blood thromboelastography, not a replacement.
Alex: So where does this fit in the broader clinical picture? Why does better fibrinogen measurement matter right now?
Sam: The context is the failure of empiric treatment strategies. CRYOSTAT-2—a large randomized trial—found no mortality benefit from giving cryoprecipitate early and universally to trauma patients. The interpretation is that blanket administration doesn't work because only a subset of patients are actually fibrinogen-depleted. The diagnostic gap is that we can't identify that subset quickly enough at the bedside to treat them selectively.
Alex: So RAR's argument is that it could fill that gap—give you a fast, cheap, absolute measure of fibrinogen-driven clot strength that tells you who actually needs replenishment.
Sam: That's the hypothesis. And the absolute viscoelastic readout matters here. Current point-of-care assays give you relative measures—amplitude at a fixed time point, for instance—that are hard to compare across centers or calibrate against a principled threshold. RAR gives you shear modulus in physical units, which is what you need to set one.
Alex: But we have to be clear about what 38 patients can actually support. The classifier numbers look good, but the transfusion group within that cohort was small. What would a skeptical reviewer demand next? [[RP_SECTION:future-validation-requirements|Future Validation Requirements]]
Sam: A prospective validation in a larger, more diverse cohort—ideally multicenter, with pre-specified thresholds rather than thresholds fit to the same data used to evaluate them. And the plasma-only limitation needs to be addressed directly: can you get equivalent diagnostic performance from a whole-blood RAR format, or does adding cellular components degrade the signal? Those are the two questions that have to be answered before this moves toward standard-of-care.
Alex: So the study establishes that the physical measurement is sound, that it correlates with the right clinical variables, and that a classifier built on it can distinguish transfusion-requiring patients in a small cohort. The mechanism is plausible, the signal is there—but the validation work is still ahead.
Sam: That's a fair read. What makes it worth paying attention to is the combination: absolute viscoelastic measurement, standard lab consumables, and a format that could realistically run in parallel with existing workflows. If the prospective data holds up, it addresses a genuine gap that a well-powered randomized trial has already shown we don't have a good answer to. That's a reasonable foundation to build on.
Alex: Thanks for listening to ResearchPod.