Multilaboratory Study of Flow-Induced Hemolysis Using the FDA Benchmark Nozzle Model

Abstract

Multilaboratory in vitro blood damage testing was performed on a simple nozzle model to determine how different flow parameters and blood properties affect device-induced hemolysis and to generate data for comparison with computational fluid dynamics-based predictions of blood damage as part of an FDA initiative for assessing medical device safety. Three independent laboratories evaluated hemolysis as a function of nozzle entrance geometry, flow rate, and blood properties. Bovine blood anticoagulated with acid citrate dextrose solution (2–80 h post-draw) was recirculated through nozzle-containing and paired nozzle-free control loops for 2 h. Controlled parameters included hematocrit (36 ± 1.5%), temperature (25°C), blood volume, flow rate, and pressure. Three nozzle test conditions were evaluated (n = 26–36 trials each): (i) sudden contraction at the entrance with a blood flow rate of 5 L/min, (ii) gradual cone at the entrance with a 6-L/min blood flow rate, and (iii) sudden-contraction inlet at 6 L/min. The blood damage caused only by the nozzle model was calculated by subtracting the hemolysis generated by the paired control loop test. Despite high intralaboratory variability, significant differences among the three test conditions were observed, with the sharp nozzle entrance causing the most hemolysis. Modified index of hemolysis (MIHnozzle) values were 0.292 ± 0.249, 0.021 ± 0.128, and 1.239 ± 0.667 for conditions i–iii, respectively. Porcine blood generated hemolysis results similar to those obtained with bovine blood. Although the interlaboratory hemolysis results are only applicable for the specific blood parameters and nozzle model used here, these empirical data may help to advance computational fluid dynamics models for predicting blood damage.

A Reusable, Compliant, Small Volume Blood Reservoir for In Vitro Hemolysis Testing

“…Aside from possible natural yellowing of the reusable base due to ambient UV-light exposure, performance remains unchanged after four years of consistent use in a multi-laboratory study. 8 This novel reservoir has been used successfully during the hemolysis assessment in the FDA benchmark nozzles, rotary blood pump prototypes, and for flow visualization experiments. 8 …”

“…8 This novel reservoir has been used successfully during the hemolysis assessment in the FDA benchmark nozzles, rotary blood pump prototypes, and for flow visualization experiments. 8 …”

“…In support of the Food and Drug Administration (FDA) Critical Path Initiative Computation Fluid Dynamics (CFD)/Blood Damage Project, a cost-effective, small volume blood reservoir consisting of a disposable bag clamped to a reusable base (Figure 1-B) was developed to enhance washout while allowing for the complete elimination of air during in vitro testing. 8 …”

Red Blood Cell Mechanical Fragility Test for Clinical Research Applications

“…The new reduced volume RBT has some limitations. The large volume RBT has been previously shown to have excellent intralaboratory reproducibility but poor interlaboratory reproducibility . While the phenomenon of interlaboratory reproducibility was not investigated in our development of the reduced volume method, we hypothesize that it will be similar.…”

Modelagem matemática da fluidodinâmica não-newtoniana e bifásica simplificada da hemólise induzida mecanicamente em sistemas de bombeamento centrífugo de sangue

“…Porém, há ainda muito para ser feito nesta área (Malinauskas et al, 2017). Por exemplo, publicações (Hariharan et al, 2011), , (Stewart et al, 2013), (Herbertson et al, 2015), com análise comparativa de resultados obtidos por vinte e oito diferentes grupos de pesquisa ao redor do mundo, mostraram uma grande variabilidade de métodos e resultados; esses grupos realizaram simulações de dinâmica dos fluidos computacional para quantificar o campo de velocidades, o campo de tensões e o índice de hemólise, em um modelo virtual padronizado de bocal com seções de escoamento paralelo, convergente e divergente. Os resultados numéricos obtidos para os campos de velocidade e para os campos de tensões variaram consideravelmente entre eles e em relação aos resultados ex-perimentais , (Stewart et al, 2013), obtidos para o modelo físico (construído a partir do mesmo projeto utilizado para o modelo virtual).…”