Computational modeling for surgical planning of arteriovenous fistula for hemodialysis: the AVF.sim system

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LIST OF FIGURES

Figure 1. Example of left arm with side-to-end AVF: elements of network 6

Figure 2. Elements of the network 7

Figure 3. AVF.SIM workflow 10

Figure 4. Pre-operative data forms 11

Figure 5. Post-operative data form 13

Figure 6. Comparison between measured and predicted brachial artery blood flow volume (BFV) at six weeks after arteriovenous fistula (AVF) surgery in 32 patients ₁₈ Figure 7. Bland-Altman plot showing agreement between measured and predicted brachial artery (BA) blood flow volume (BFV) at six weeks after AVF surgery in 32 patients ₁₉

Figure 8. Correlation between measured and predicted brachial artery (BA) blood flow

volume (BFV) at six weeks after AVF surgery in 32 patients ₁₉ Figure 9. Correlation between pre-operative radial artery (RA) diameter and measured

brachial artery (BA) blood flow volume (BVF) at six weeks after AVF surgery in 30

patients 20

LIST OF TABLES

Table 1. Demographic parameters of Miulli Hospital patient population 15 Table 2. Clinical parameters of Miulli Hospital patient population 16

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ABSTRACT

The native arteriovenous fistula (AVF) represents the vascular access (VA) of first choice for hemodialysis (HD) patients. Despite guidelines recommend pre-operative clinical and ultrasound criteria for the planning of AVF creation, the placement of a functioning AVF for haemodialysis is a difficult clinical task. Use of computational models to predict the AVF outcome could provide an efficient tool to assist the surgeon in selecting the optimal AVF location and configuration. Caroli et al. have recently reported interesting data that were collected in the context of the European ARCH project. They provided preliminary evidence about accuracy and reliability of the patient-specific computational model to predict the blood flow volume (BFV) distribution in the arm vasculature during VA maturation.

In order to evaluate the power of prediction and the acceptance in the clinical routine of this patient-specific computational model, Bozzetto M. and Remuzzi A. designed a multicentre prospective observational study involving six Italian clinical centers. In our centre, thirty-two patients with newly created AVF were enrolled. We placed two upper arm (brachiocephalic) and thirty lower arm (radiocephalic) AVF. All AVF had a side-to-end configuration.

We enjoyed the use of the model in the pre-operative AVF planning and it did not cause a further clinical workload. Despite the small sample size of data, predicted brachial artery blood flow volume at six weeks after surgery had a high significant linear correlation with measured values (in average 690±194 ml/min vs 704±186 ml/min, R=0.8, p<0.0001). Only few patients presented a discrepancy between predicted and measured brachial artery blood flow volume results.

In conclusion, this clinical observational study confirmed the accuracy of AVF blood flow volume predictions and showed that the system is easy to use and well accepted in the clinical setting. The AVF.SIM system can support the surgeon to plan more efficient AVF, with the reduction of no maturation events and the risk for heart failure and steal syndrome related to high blood flow volume AVF.

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4 1. INTRODUCTION

The native arteriovenous fistula (AVF) is widely recognized as the vascular access (VA) of first choice for hemodialysis (HD) patients as it provides the best overall outcomes compared with arteriovenous graft (AVG) and central venous catheter (CVC). However, it still has a low primary and secondary patency rate. In a recent systematic

review and meta-analysis, AVFs had a high rate of primary failure (23%, 95% CI-

18%-28%; 37 cohorts; 7,393 AVFs) and a low to moderate primary and secondary patency rates at 1 year (60% and 71%). [1-3]

Timing of AVF placement during the pre-ESRD period is important to determine the best VA for each individual patient [4]. In contrast to the large improvements in VA use in the United State (US) for prevalent hemodialysis patients, there has been no improvement in AVF use for new patients who initiate hemodialysis therapy. The last report of Dialysis Outcomes and Practise Patterns Study, DOPPS 5 (2012-2014), reported a low use of AVF among incident US patients, with 67% initiating HD therapy with a CVC. On the contrary, AVF use among incident patients was 50% to 60% in most European countries (58% in Italy), and 84% in Japan [5].

Creation and maintenance of a functioning AVF for hemodialysis therapy is a difficult clinical task. Guidelines suggest pre-operative clinical and ultrasound criteria for the surgical planning of AVF creation. It is generally accepted that adequate physical examination, vascular mapping and estimation by doppler-ultrasound (DUS) of arterial and venous segment blood flow and size should be performed before the surgical procedure. These procedures suggest if patient’s vasculature structure and function can be adequate for creation of a native fistula or if potential problems may develop during or after AVF surgery [6-7].

Vascular access dysfunction and complications such as non-maturation, failure, hand ischemia and risk of heart failure are important clinical challenges. The goal of AVF surgery is to obtain a blood flow volume (BFV) that allows a flow rate of at least 300 ml/min of blood in the hemodialysis extracorporeal circulation. On the other hand very high BFV, exceeding 1.5 L/min, should be avoided for the risk of cardiac dysfunction and hand ischemia. [8]

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An objective and reliable prediction of post-operative BVF after the process of vessel remodelling and the consequent maturation of the VA, could be extremely important to assist the surgeon in selecting the optimal AVF location and configuration.

In this context, a computational approach has been proposed for the simulation of hemodynamic and vascular wall dynamics in a complex vascular network, which could predict blood flow volume (BFV) change after vascular access creation. This patient-specific vascular network model was obtained on the basis of a generic vascular network model derived from the data available in literature [9] and adapted by geometrical parameters according to body weight, height, age, and sex of individual patients. The vascular network model was patient-specific as it considered patient’s pre-operative data (demographic and clinical parameters) [10].

Huberts et al.[11] reported a network model of the arm circulation according to a 0D/1D pulse wave propagation scheme of the circulation which was implemented by Manini et al.[12] In this numerical approach, each arterial vessel of the arm was composed of several segments with uniform radii, while the descending aorta, the vessels of the lower body and the controlateral arm and cerebral vessels were modelled as end segments consisting of windkessel elements. It also contains a nonlinear element for simulation of the hemodynamic in the arteriovenous anastomosis. Finally, the vascular network includes elements for distal and proximal cephalic vein, median cubital and basilica veins (Figure 1).

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Figure1 Example of left arm with side-to-end AVF: elements of network.

Assuming fully developed incompressible Newtonian flow in a straight tube, the model solution is based on the computation of hydraulic pressure and volumetric flow rate for each network element derived from conservation of mass and momentum equations. All elements of the vascular network had specific properties of hydraulic resistance, inductance (blood inertia) and compliance. (Figure2).

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Figure 2 Elements of the network: (a) 1D element for principle arterial and venous vessels; (b) 0D windkessel elements for peripheral resistances; (c) 0D element for anastomosis modelling; (d) linear element.

In order to estimate changes in blood vessel dimensions and flow rate induced by AVF maturation, this model has been optimized by a vascular adaptation algorithm based on the assumption that changes in blood vessel diameter take place upon changes in BFV. The peak wall shear stress acts on vascular endothelial cells in order to maintain a physiological value of [13-14].

For the numerical solution a mathematical solver made available as an open source computer code (pyNS). [11]

As reported by Caroli [15], this patient-specific computational model for the prediction of AVF outcome was validated during a prospective observational multicentre controlled clinical trial performed within the ARCH project [16]. Considering the whole patient population (n=63) with upper and lower AVF and different anastomosis configurations (end-to-end and side-to-end), data showed a strong correlation between predicted and measured values of brachial artery BFV at six months after surgery (p=<0.001, R²=0.9). The percent of error of predicted versus measured brachial artery BFV was 3±19% (95% CI- 2 to 8%). This result provided preliminary evidence about

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the accuracy and reliability of the computational models to predict the BVF distribution in the arm vasculature during VA maturation.

The use of new technologies in the clinical setting needs careful evaluation of their effective usability and potential benefit in this critical field. There are some important aspects that have to be considered in this regard, as the accuracy of data generated in the clinical routine and the possible workload increase for the clinicians. In order to evaluate the power of prediction and the acceptance in the clinical routine of this patient-specific computational model, Bozzetto and Remuzzi designed a multicentre prospective observational study involving six Italian clinical centers. They developed a web-based system (AVF.SIM) for simple and easy data transfer and fast consultation of the predicted results of planned AVF outcome.

The object of this thesis was to report the results obtained within Miulli Hospital experience.

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9 2. MATERIALS AND METHODS

2.1

AVF.SIM SYSTEM AND USABILITY TEST

2.1.1 Study Design

The prospective multicenter observational clinical study involved all incident hemodialysis patients selected for a native AVF creation. It was designed with the aim of introducing the computational model in the clinical setting, in particular to evaluate the power of prediction of the AVF.SIM system and its acceptance in the routine clinical practise. The study was based on the collaboration among clinical centers for data collection and the coordination center for computer based simulation.

The enrolment period lasted six months, from January to June 2015. Data collected have been received and processed by the Coordinator Center, Biomedical Engineering Department, Mario Negri Institute of Pharmacological Research.

2.1.2 Study Protocol

As reported in the workflow (Figure 3), following patient enrolment, demographic (age, gender, height, weight) and clinical (haematocrit, plasma protein concentration, mean artery pressure, heart rate, presence or absence of diabetes and/or diabetes) data were systematically collected.

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Figure 3 AVF.SIM workflow.

All data required were inserted in a pre-operative form. Subsequently, a peripheral Doppler Ultrasound (DUS) examination was performed in order to acquire a full upper limb vascular map, that included arterial and venous vessel diameters and blood flow values in different vascular locations.

Furthermore, the physician had to pick the type of the anastomosis he would create. After the pre-operative data acquisition, the AVF was surgically created. About six weeks after surgery a follow-up DUS was performed. It was considered the typical time period in which AVF maturation take place (Figure 4).

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2.1.3 Protocol for vascular DUS examination

For all the patients, a pre-operative DUS examination was performed in order to assess vascular diameters and flows. The patient lied in the supine position with the examined arm parallel to the body in a comfortable position for imaging.

-Venous assessment: short and long axis diameter measurements in the transverse view of cephalic vein from the wrist to the cephalic arc, the cubiti elbow, the basilic vein from the elbow until it joins the brachial vein and the subclavian vein. In order to generate venous congestion, measurements were performed by a proximal pressure cuff, except for the subclavian vein.

- Arterial assessment: short and long axis diameter measurements in the transverse view of brachial, radial, ulnar and subclavian artery. On brachial, radial and ulnar artery the Time Average Velocity (TAV) was also assessed. TAV (cm/s) was calculated by the software selecting the function “Calculate flow” on the DUS machine. The physician had to trace three complete cardiac cycles on the Velocity/Time curve by sampling the central volume of the blood vessel on a longitudinal scan.

In order to assess vessels adaptation, a follow-up DUS examination was performed in every patient at about six months after surgery. Short- and long-axis diameter measurements in the transverse view of brachial, radial, ulnar artery and cephalic or basilic vein were carried out. For brachial, radial and ulnar artery also TAV was assessed, to provide a comparison with blood flow predicted by the simulation tool (Figure 5).

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Figure 5 Post-operative data form.

2.1.4 Data Management

Data collected, in a fillable .pdf forms, were sent from the centers by mail or fax to the coordination station, which performed a computer-based simulations within one hour. Pre-operative data, simulation results and follow-up data were also stored in a secured DMS (Document Management System). Patient’s data were anonymized during data collection and identified by a personal ID assigned by the performing simulation center. The signed written informed consent for the data collection and for the AVF creation was simultaneously obtained.

2.1.5 Usability test

At the end of the study, a questionnaire was submitted to all clinicians, to measure they satisfaction and to collect suggestions for further development of the system and the user interface. Users were asked to fill up a questionnaire with twelve statements, using a level of agreement between 1 to 5.

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2.2 MIULLI HOSPITAL PATIENT POPULATION

In our center 19 consecutive patients were enrolled. After the end of the enrolment study period, we used the AVF.SIM system in the clinical setting for an extension period of five months (from January to May 2016). Therefore, the entire study population of the thesis involved 32 patients (mean age 66±13 (27-86) years, female 50%, hypertensive 100% and diabetic 38%). Demographic and clinical parameters, used to generate patient-specific vascular network models are summarized in Table 1 and Table 2. Mean arterial pressure (MAP) value was 97±10 mmHg, BMI 27±4 Kg/m², haematocrit 33±4% and serum total protein 6.8±0.5 g/dl.

According to the study protocol, DUS pre-operative examination was performed by two experienced physicians the day before AVF creation who used a DUS machine (Terason t3000) with a 7.0-12.0 MHz linear transducer. The DUS intra- and inter-observer variability was minimal (<5%).

In 2 patients AVF was created in the upper arm, that was brachiocephalic in both of them (BC, n=2). In 30 patients AVF was created in the lower arm, with a radiocephalic configuration for all of them (RC, n=30). As regard as the site of AVF placement, 11 of them were created in the right side and 21 in the left side. Anastomosis were all side to end (S-E, n=32). The choice of anastomosis creation technique was made by the surgeons on the basis of clinical and DUS examination.

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N Age,

years Gender AVF arm Side Height (cm)

Weight (Kg) BMI (Kg/m²) BodySurfaceS(sqm) 1 74 M Left Lower 165 53 19 1,6 2 68 F Left Lower 164 64 24 1,7 3 68 M Left Lower 163 79 30 1,9 4 69 F Left Lower 159 68 27 1,7 5 51 F Left Upper 160 53 21 1,5 6 76 M Right Lower 158 62 25 1,6 7 60 F Left Lower 160 62 24 1,6 8 27 F Left Lower 172 55 19 1,7 9 57 M Left Lower 168 60 21 1,7 10 49 M Right Lower 176 85 27 2,0 11 59 M Left Lower 174 85 28 2,0 12 73 F Left Lower 160 76 30 1,8 13 51 M Left Lower 179 110,5 34 2,3 14 79 F Left Lower 165 82 30 1,9 15 76 M Right Lower 174 84 28 2,0 16 68 F Left Lower 157 72 29 1,5 17 70 F Right Lower 140 62 32 1,5 18 84 F Right Lower 156 62 25 1,6 19 52 M Left Lower 174 86 28,4 2,0 20 57 F Left Lower 155 64 26,6 1,6 21 77 M Left Lower 156 75,5 31,0 1,8 22 75 F Right Lower 161 83 32,0 1,9 23 61 M Right Lower 173 69,5 23,2 1,8 24 86 F Left Lower 150 60,5 26,9 1,5 25 43 F Right Lower 169 60 21,0 1,7 26 62 M Left Upper 172 93 31,4 2,1 27 81 F Left Lower 160 73 28,5 1,8 28 80 M Right Lower 165 75 27,5 1,8 29 76 M Right Lower 150 74 32,9 1,7 30 62 M Left Lower 145 52 24,7 1,4 31 78 F Right Lower 147 55 25,5 1,5 32 57 M Left Lower 179 101 31,5 2,2

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N MAP (mmHg) HR (bpm) Hematocrit (%) s Protein (g/dl) Hypertension Diabetes

1 90 71 31,3 6,8 Yes No 2 85 68 38,6 7,1 Yes Yes 3 77 80 35,9 7,3 Yes No 4 97 80 27 6,3 Yes No 5 93 75 37,3 6,9 Yes No 6 93 73 28,3 6,5 Yes Yes 7 87 83 34,8 7,6 Yes No 8 107 78 30,5 5,9 Yes No 9 107 73 36,9 7,2 Yes No 10 97 78 32,9 7,2 Yes No 11 113 66 31,3 6,3 Yes Yes 12 100 75 27,7 6,3 Yes Yes 13 107 64 30,9 6 Yes Yes 14 87 75 33,2 7 Yes No 15 94 72 31,7 6,4 Yes No 16 96 76 35.4 7.3 Yes No 17 90 70 34,7 6,8 Yes Yes 18 83 65 24,4 6,6 Yes Yes 19 99 65 31,4 6,8 Yes No 20 100 75 39,7 7,1 Yes No 21 99 76 25,5 6,6 Yes Yes 22 107 85 31,9 5,7 Yes Yes 23 111 65 32,4 5,6 Yes No 24 120 71 36,3 7,3 Yes No 25 101 73 29,9 6,7 Yes No 26 94 64 40,7 6,9 Yes No 27 113 78 34,8 7,7 Yes Yes 28 101 75 34,1 7,6 Yes No 29 98 78 37,8 6,4 Yes No 30 100 71 39,8 6,6 Yes No 31 87 65 31,9 7 Yes Yes 32 88 83 32,7 7,5 Yes Yes

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17 2.3 STATISTICAL ANALYSIS

Data are calculated as Mean±SD. The agreement between predicted and measured data was investigated using Bland-Altaman plots. The correlation between predicted and measured brachial artery blood flow volume (BFV) was investigated using the linear regression analysis. The comparison between the groups was made by unpaired t-test for population with dissimilar variance.

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18 3. RESULTS

Predicted results were compared with measurements obtained by DUS examination at six weeks after surgery, as shown in Figure 6.

Figure 6 Comparison between measured and predicted brachial artery blood flow volume (BFV) at six weeks after arteriovenous fistula (AVF) surgery in 32 patients.

In general, a good global agreement was observed between predicted and measured brachial artery BFV six weeks after surgery, with average values of 690±194 ml/min vs 704±186 ml/min. The percent error of predicted versus measured brachial artery BFV was of -1.9±20% (95% CI- -9 to 5). A significantly discrepancy between predicted and measured BFV was observed only for few cases.

The Bland-Altman plot reported in Figure 7 indicates a good accuracy between predicted and measured brachial artery BFV in the whole data set.

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19 0 5 0 0 1 0 0 0 1 5 0 0 - 4 0 0 - 2 0 0 0 2 0 0 4 0 0 D i f f e r e n c e i n b r a c h i a l b l o o d f l o w ( m l / m i n ) A v e r a g e b r a c h i a l b l o o d f l o w ( m l / m i n )

Figure 7 Bland-Altman plot showing agreement between measured and predicted brachial artery (BA) blood flow volume (BFV) at six weeks after AVF surgery in 32 patients.

As shown in Fig.8, a statistically significant linear correlation in the whole final dataset was found between predicted and measured results of brachial artery BFV (p<0.0001, R=0.8). 0 4 0 0 8 0 0 1 2 0 0 0 4 0 0 8 0 0 1 2 0 0 B A m e a s u r e d B F V m l / m i n B A p r e d i c t e d B F V m l / m i n R = 0 . 8 p < 0 . 0 0 0 1

Figure 8 Correlation between measured and predicted brachial artery (BA) blood flow volume (BFV) at six weeks after AVF surgery in 32 patients.

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For the limited number of proximal AVF (n=2), further statistical analysis was conducted only on the results of RC s-e AVF (n=30). A slight significant correlation was found between pre-operative radial artery diameter and brachial artery BFV measured at six weeks after surgery (p=0.002; R=0.58), Figure 9. No significant correlation was found between pre-operative brachial artery diameter and brachial artery blood flow measured at six weeks after surgery.

1 . 5 2 . 0 2 . 5 3 . 0 0 4 0 0 8 0 0 1 2 0 0 1 6 0 0 p r e - o p e r a t i v e R A d i a m e t e r ( m m ) p o s t -o p e r a t i v e B A B F V ( m l / m i n ) R = 0 . 5 8 p = 0 . 0 0 2

Figure 9 Correlation between pre-operative radial artery (RA) diameter and measured brachial artery (BA) blood flow volume (BVF) at six weeks after AVF surgery in 30 patients.

There was no evidence of significant correlation between pre-operative artery BFV (radial and brachial) and measured brachial artery blood flow at six weeks after surgery. Interestingly, radial artery diameter significantly increased after surgery in all diabetic patients (n=12) with RC side-to-end radial artery diameter (2.6±0.2mm vs 4±0.6mm, p<0.001); paired t-test. Furthermore, they presented a significant difference of the pre-operative brachial artery diameter when compared with non-diabetic patients (5±0.5mm vs 5.4±0.7mm, p=0.03). Considering all parameters, we did not find other differences between the two groups, particularly about post-operative brachial artery BFV.

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Out of 32 patients, only one of them developed an early failure about ten weeks after AVF creation because of a severe drop in the mean artery pressure which led to AVF thrombosis. Another patient, 1 month after he began hemodialysis, experienced a primary failure due to thrombosis secondary to an acute myocardial infarction. Both patients were not diabetic. About the first group of enrolment, from January to June 2015 (n=19), 16 patients started hemodialysis with a well function AVF. In the second group of enrolment, from January to May 2016, 4 patients started hemodialysis with a well function AVF. We did not experience high BFV AVF.

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22 4. DISCUSSION

The main endpoint of the study was to evaluate the usability of the model by the clinicians and the power of prediction of the AVF.SIM system.

In our centre, patient enrolment, DUS examination and data transmission were easily conducted because they did not cause an excessive further clinical workload. We usually perform the clinical and ultrasound examination recommended by guidelines [17]. However we found the vascular measurements protocol useful as it provides a full upper arm vascular mapping. We also enjoyed the use of the model in the pre-operative AVF planning. The number of enrolled patients in a relatively short time confirms the usefulness of the model that we continued to use the in the routine clinical setting. Considering the AVF configuration, our population was homogeneous as the skilled surgeons always performed the side-to-end anastomosis and provided consistency to the results. Although the small sample size, we obtained significant results on the reliability and the accuracy of predictions. We found a good correlation in the whole population between predicted and measured brachial artery BFV, which is widely approved as a surrogate of blood flow through the AVF. Data suggest that this computational approach (AVF.SIM system) can be potentially used in the surgical planning as well as in preventing very low or very high BFV. In fact, it takes into account patient-specific demographic and clinical factors which play a complex interaction in the post-operative vascular remodelling.

To date, few studies have reported the prediction of AVF outcome. Lok et al. presented a scoring system to stratify the patient’s risk for AVF failure to mature. The most significant clinical predictors were advanced age, peripheral vascular and coronary artery disease and, white race. The Lok model, however does not predict AVF outcome [18]. In a recent study conducted by Masengu et al. there is only a vague indication that female gender, lower arm and low radial artery BFV are associated with failure [19]. However, there is no general consensus on the criteria to select the type of anastomosis for AVF with the only suggestion to avoid arteriovenous fistula in case of small diameter vessels [7, 20]. Data of the present study confirm that the AVF creation cannot be based only on blood vessel diameter, as shown by the slight correlation between pre-operative radial artery diameter and brachial artery BFV at six weeks after surgery.

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As reported by Caroli et al.[15], we noted the interesting behaviour observed in diabetic patients with lower arm AVF, who presented a significant increase in the radial artery after surgery. Although distal AVF creation could represent a challenge for the surgeon, evidence suggests that also diabetic vessels can remodel.

In side-to-end AVF computational model, the anastomosis angle and diameter are not patient-specific but parametric. These elements can influence pressure drop across AVF, which cause variations in wall shear stress with a consequent blood flow rate, arterial and venous diameter increase. In our patient population all RC s-e AVF presented an anastomosis angle which ranged from 25 to 35 degree. Anastomosis diameter could be taken into account to predict vascular remodelling. To provide more evidences, we have measured the arteriotomy diameter during surgery for the last enrolled 16 patients. These findings did not support a correlation between anastomosis diameter and brachial artery BFV (predicted and measured). However, we consider that more efforts should be done in order to complete the model with this operator dependent parameter.

We had a great primary patency rate at 1 year (97%), with only one patient who developed an early failure and one patient who experienced a primary failure about 1 month after he started hemodialys. Both patients underwent AVF thrombosis due to hemodynamic changes of their clinical condition. The most common cause of AVF failure is the vascular thrombosis, primarily due to development of neointimal hyperplasia (NH) and subsequent stenosis. Recent research supports the role of local hemodynamic forces as a triggering factor for the formation of localized NH with the growth of a layer of resident and infiltrating smooth muscle cells (SMC) that produce abundant extracellular matrix. The pulsatile laminar wall shear stress (WSS) acts on the endothelial cells (EC) with a predominant direction that induce the expression of several atheroprotective and antithrombogenic genes. There is evidence that disturbed flow, with low oscillating WSS, induces expression of atherogenic and thrombogenic genes related with the development of NH [21,22].

In conclusion, despite of the main limits of this study as the small sample size and potential inaccuracies due to DUS measurements, this clinical observational study confirmed the accuracy of AVF BFV predictions and showed that the system is easy to use and well accepted in clinical routine. The proposed system supports the selection for

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the surgical procedure and helps to plan more efficient AVF, with potential reduction of no maturation events and the risk for heart failure and steal syndrome related to high blood flow volume AVF. The use of the present computational model can allow the individualization of the vascular access with a patient-centered surgical approach.

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25 6. ACKNOWLEDGMENT

This research study was made in collaboration with the Biomedical Engineering Department of the Mario Negri Institute of Pharmacological Research. I would like to express my thanks to my coordinators, Prof. Andrea Remuzzi and Dr. Michela

Bozzetto, whoprovided insight and expertise that greatly assisted the research. I would

like to express my special appreciation and thanks to my advisors, Dr. Carlo Lomonte and Dr. Francesco Casucci. I would like to thank them for encouraging my research and for allowing me to grow as a research scientist. I would like to thank physicians and nurses of the Nephrology and Dialysis Department at the Miulli Hospital. All of them have been support me when I recruited patients and collected data for my thesis. Finally, I would like to express my special thanks to my graduate school director, Prof. Maria Francesca Egidi, who approved and encouraged my research project. Furthermore, I would like to thank her for providing the language revision of this thesis.

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26 7. BIBLIOGRAPHY

1. Schinstock CA, Albright RC, Williams AW, et al. Outcomes of arteriovenous fistula creation after the Fistula First Initiative. Clin J Am Soc Nephrol. 2011;6(8):1996-2002. 2. Smith Ge, Gohil R, Chetter IC. Factors affecting the patency of arteriovenous fistulas for dialysis access. J Vasc Surg. 2012;55(3):849-855.

3. Al-Jaishi AA, Oliver MJ, Thomas SM, Lok CE, Zhang JC, Garg AX, et al. Patency rates of the arteriovenous fistula for hemodialysis: a systematic review and meta-analysis. Am J Kidney Dis 2014;63:464-78.

4. Lok CE, Davidson I. Optimal choice of dialysis access for chronic kidney diasease patients: developing a life plan for dialysis access. Semin Nephrol. 2012;32(6):530-537. 5. Pisoni RL, Zepel L, Port FK, Robisnson BM. Trends in US Vascular Access, Patient Preferences, and Related Practise: An Update From the US DOPPS Practise Monitor With International Comparison. Am J kidney Dis. 2015;65(6):905-915.

6. National Kidney Foundation: KDOQI Clinical Practise Guidelines and Clinical Practise recommendations for 2006 Updates: Hemodialysis Adequacy, Peritoneal Dialysis Adequacy and Vascualar Access. Am J Kidney Dis 2006;48(1):1-322.

7. Tordoir J, Canaud B, Haage P, Konner K, Basci A, Fouque D, Kooman J, Martin-Malo A, Pedrini L, Pizzarelli F, Tattersall J, Vennegoor M, Wanner C, ter Wee P, Vanholder R.: EBPG on vascular access. Nephrology Dialysis Transplantation (2007) 22 [Suppl 2]:ii88-ii117.

8. Basile C, Lomonte C, Vernaglione L, Casucci F, Antonelli M, Losurdo N. The relationship between the flow of arteriovenous fistula and cardiac output in haemodialysis patients. Nephrol Dil Transplant 2008;23:282-7.

9. Reymond P, merenda F, Perren F et al. Validation ofe a one-dimensional model of the systemic arterial tree. Am J Physiol Heart Circ Physiol 2009;297:208-222.

10. Passera K, Manini S, Antiga L, Remuzzi A. Patient-specif model of arterial circulation for surgical planning of vascular access. J Vasc Access 2013;14(2):180-192. 11. HubertsW, Bode AS, Kroon W, Planken RN, Tordoir JHM, van de Vossen FN, Boosbom EM. A pulse wave propagation model to support decision-making in vascular access planning in the clinic. Med Eng Phys. 2012 Mar;34(2):233-48.

12. Manini S, Antiga L, Botti L, Remuzzi A. pyNS: An Open-Source Framework for 0D Haemodynamic modelling. Ann Biomed Eng. 2015 Jun;43(6):1461-73.

13. Ene-Iordache B, Mosconi L, Antiga L, et al. Radial artery remodeling in response to shear stress increase within arteriovenous fistula for hemodialysis access. Endothelium. 2003;10(2):95-102.

14. Manini S, Passera K, Huberts W, Botti L, Antiga L, Remuzzi A. Computational model for simulation of vascular adaptation following vascular access surgery in hemodialysis patients. Comput Methods Biomech Biomed Engin 2014;17:1358-67.

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15. Caroli A, Manini S, Antiga L, Passera K, Ene Iordache B, Rota S, Remuzzi G, Bode A, Leermakers J, van de Vosse FN, Vanholder R, Malovrh M, Tordoir J, Remuzzi A. Validation of a patient-specific hemodynamic computational model for surgical planning of vascular access in hemodialysis patients. Kidney Int. 2013 Dec;84(6):1237-45.

16. Bode A, Caroli A, Huberts W, Planken N, Antiga L, Bosboom M, Remuzzi A, Torodir J, et al. Clinical study protocol for the ARCH project. Computational modelling for improvement of outcome after vascular access creation. J Vasc Access 2011;12(4):369-376.

17. Lomonte C, Basile C. Preoperative assessment and planning of haemodialysis vascular access. Clin Kidney J 2015;8:278-81.

18. Lok CE, Allon M, Moist L, Oliver MJ, Shah H, Zimmerman D. Risk equation determing unsuccessful cannulation events and failure to maturation in arteriovenous fistula (REDUCE FTM I). J Am Soc Nephrol 2006;17:3204-12.

19. Masengu A, Maxwell AP, Hanko JB. Investigating clinical predictors of arteriovenous fistula functional patency in a European cohort. Clin Kidney J 2016;9:142-7.

20. Roy-Chaudhury P, Lee TC, Munda R. Predicting dialysis vascular access blood flow and diameter: too much, too little, or just right. Kidney Int 2013;84:1076-8.

21. Brahmbhatt A, Remuzzi A, Franzoni M, Misra S. The molecular mechanism of hemodialysis vascular access failure. Kidney International 2016;89:303-316.

22. Bozzetto M, Ene-Iordache B, Remuzzi A. Transitional flow in the venous side of patient-specific arteriovenous fistulae for hemodilaysis. Annals of Biomedical Engineering 2015.

Computational modeling for surgical planning of arteriovenous fistula for hemodialysis: the AVF.sim system

CONTENTS

LIST OF FIGURES

LIST OF TABLES

ABSTRACT

2.1