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An arteriovenous fistula (AVF) needs to mature before it becomes suitable to cannulate for haemodialysis treatment. Maturation importantly depends on the post-operative flow increase. Unfortunately, 20–40% of AVFs fail to mature (FTM). A patient specific computational model that predicts immediate post-operative flow was developed, and it was hypothesised that providing information from this model for planning of fistula creation might reduce FTM rates. A multicentre, randomised controlled trial in nine Dutch hospitals was conducted in which patients with renal failure who were referred for AVF creation, were recruited. Patients were randomly assigned (1:1) to the control or computer simulation group. Both groups underwent a work up, with physical and duplex ultrasonography (DUS) examination. In the simulation group the data from the DUS examination were used for model simulations, and based on the immediate post-operative flow prediction, the ideal AVF configuration was recommended. The primary endpoint was AVF maturation defined as an AVF flow ≥500 mL/min and a vein inner diameter of ≥4 mm six weeks post-operatively. The secondary endpoint was model performance (i.e. comparisons between measured and predicted flows, and (multivariable) regression analysis for maturation probability with accompanying area under the receiver operator characteristic curve [AUC]). A total of 236 patients were randomly assigned (116 in the control and 120 in the simulation group), of whom 205 (100 and 105 respectively) were analysed for the primary endpoint. There was no difference in FTM rates between the groups (29% and 32% respectively). Immediate post-operative flow prediction had an OR of 1.15 (1.06–1.26; p <.001) per 100 mL/min for maturation, and the accompanying AUC was 0.67 (0.59–0.75). Providing pre-operative patient specific flow simulations during surgical planning does not result in improved maturation rates. Further study is needed to improve the predictive power of these simulations in order to render the computational model an adjunct to surgical planning. [ABSTRACT FROM AUTHOR]