Dataset Information

Impact of Design and Deployment Technique on the Hydrodynamic Resistance of Flow Diverters : An in Vitro Experimental Study.

ABSTRACT:

Purpose

Despite the high efficacy of flow diverters (FD) in treating sidewall intracranial aneurysms, failures are reported. One of the physical factors determining efficacy is the flow reducing capacity of the FD that is currently unknown to the operator. Our aim was to measure the flow reducing capacity expressed as the hydrodynamic resistance (HR), the metallic surface area (MSA) and pore density (PD) of two different FD designs and quantitatively investigate the impact of sizing and the deployment technique on these parameters.

Methods

Altogether 38 Pipeline (Medtronic) and P64 (Phenox) FD‑s were implanted in holder tubes by a neurointerventionist in nominally sized, oversized and longitudinally compressed or elongated manners. The tubes were placed in a flow model with the flow directed across the FD through a side hole on the tube. HR was expressed by the measured pressure drop as the function of the flow rate. Deployed length, MSA and PD were also measured and correlated with the HR.

Results

Both PD and MSA changed with varying deployment length, which correlates well with the change in HR. Oversizing the device by 1 mm in diameter has reduced the HR on average to one fifth of the original value for both manufacturers.

Conclusion

This study demonstrates experimentally that different FD designs have different flow diverting capacities (HR). Parameters are greatly influenced by radial sizing and longitudinal compression or elongation during implantation. Our results might be useful in procedure planning, predicting clinical outcome, and in patient-specific numerical flow simulations.

SUBMITTER: Gyurki D

PROVIDER: S-EPMC8894302 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Impact of Design and Deployment Technique on the Hydrodynamic Resistance of Flow Diverters : An in Vitro Experimental Study.

Gyürki Dániel D Csippa Benjamin B Paál György G Szikora István I

Clinical neuroradiology 20211022 1

<h4>Purpose</h4>Despite the high efficacy of flow diverters (FD) in treating sidewall intracranial aneurysms, failures are reported. One of the physical factors determining efficacy is the flow reducing capacity of the FD that is currently unknown to the operator. Our aim was to measure the flow reducing capacity expressed as the hydrodynamic resistance (HR), the metallic surface area (MSA) and pore density (PD) of two different FD designs and quantitatively investigate the impact of sizing and ...[more]

PMID: 34686884

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Project description:OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

Dataset Information

Impact of Design and Deployment Technique on the Hydrodynamic Resistance of Flow Diverters : An in Vitro Experimental Study.

Purpose

Methods

Results

Conclusion

Publications

Impact of Design and Deployment Technique on the Hydrodynamic Resistance of Flow Diverters : An in Vitro Experimental Study.

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