Thrombosis and Hemodynamics: external and intrathrombus gradients

A Randomized, Controlled Trial of Continuous Heparin Infusion to Prevent Asymptomatic Catheter-Related Thrombosis at Discharge in Infants After Cardiac Surgery: The CHIP-CRT Trial

OBJECTIVES

There are conflicting results in preventing catheter-related thrombosis (CRT). Continuing infusion of unfractionated heparin (UFH) was a potential option for CRT. This study was to determine the effect of continuous UFH infusion on asymptomatic CRT at discharge in infants after cardiac surgery.

STUDY DESIGN

This study was a randomized, placebo-controlled, clinical trial at a single center. All infants with central venous catheters after cardiac surgery, below 3 months of age, were eligible. Stratified by CRT, infants were randomly assigned to the UFH group or the normal saline group. UFH was initiated at a speed of 10 to 15 units/kg/h for infants with CRT and 2 to 3 units/kg/h without CRT. The primary outcome was to determine the rate of CRT at discharge. The secondary outcomes included thrombosis 6 months after surgery, adverse events of UFH, and post-thrombotic symptoms.

RESULTS

Due to slow recruitment during the COVID-19 pandemic, this trial was prematurely stopped. Only 35 infants were randomly assigned to the UFH or control groups. There was no statistically significant difference in CRT rate at discharge (P=0.429) and 6 months after surgery (P=1.000) between groups. All CRTs except one disappeared at discharge. No thrombosis or post-thrombotic symptom was reported at follow-up evaluation. There was no difference between groups in duration of thrombus (P=0.088), D dimer (P=0.412), catheter in situ days (P=0.281), and post-thrombotic syndrome (P=1.000), except for activated partial thromboplastin time (P=0.001).

CONCLUSIONS

With the early stop of this trial and limited data, it is difficult to draw a definitive conclusion about the efficacy of UFH on CRT. Meanwhile, considering the data from 6 months follow-up, in this population, asymptomatic CRT might resolve with no intervention.

Decoding thrombosis through code: a review of computational models

From the molecular level up to a blood vessel, thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in thrombosis are poised to bring about significant knowledge growth in the years to come.

A three-dimensional multiscale model for the prediction of thrombus growth under flow with single-platelet resolution

Modeling thrombus growth in pathological flows allows evaluation of risk under patient-specific pharmacological, hematological, and hemodynamical conditions. We have developed a 3D multiscale framework for the prediction of thrombus growth under flow on a spatially resolved surface presenting collagen and tissue factor (TF). The multiscale framework is composed of four coupled modules: a Neural Network (NN) that accounts for platelet signaling, a Lattice Kinetic Monte Carlo (LKMC) simulation for tracking platelet positions, a Finite Volume Method (FVM) simulator for solving convection-diffusion-reaction equations describing agonist release and transport, and a Lattice Boltzmann (LB) flow solver for computing the blood flow field over the growing thrombus. A reduced model of the coagulation cascade was embedded into the framework to account for TF-driven thrombin production. The 3D model was first tested against in vitro microfluidics experiments of whole blood perfusion with various antiplatelet agents targeting COX-1, P₂Y₁, or the IP receptor. The model was able to accurately capture the evolution and morphology of the growing thrombus. Certain problems of 2D models for thrombus growth (artifactual dendritic growth) were naturally avoided with realistic trajectories of platelets in 3D flow. The generalizability of the 3D multiscale solver enabled simulations of important clinical situations, such as cylindrical blood vessels and acute flow narrowing (stenosis). Enhanced platelet-platelet bonding at pathologically high shear rates (e.g., von Willebrand factor unfolding) was required for accurately describing thrombus growth in stenotic flows. Overall, the approach allows consideration of patient-specific platelet signaling and vascular geometry for the prediction of thrombotic episodes.

The excessive formation of blood clots under flow within the circulatory system (thrombosis) is known to initiate heart attacks and strokes. Therefore, obtaining insights into the formation and progression of these clots will be useful in evaluating pharmacological options. To this end, we have developed a 3D computational model that tracks the growth of a blood clot under flow from initial platelet deposition to full vessel occlusion in the presence of soluble platelet agonists. We first validated the model against experimental predictions of blood clots formed in vitro. Due to the construction of the model in 3D, we were able to carry out simulations of clot formation under important clinical situations, namely cylindrical blood vessels and acute flow narrowings (stenoses). To our knowledge, our model is the first of its kind that can account for patient-specific platelet phenotypes to perform robust 3D simulations of thrombus growth in geometries of clinical relevance.