Background: The management of patent dialysis fistulas in patients after kidney transplantation (KTx) is controversial—the options that are usually considered are the fistula’s closure or observation. Many complications of dialysis fistulas occur in patients after KTx, and immunosuppression increases the risk of fistula aneurysms and hyperkinetic flow. This study aimed to evaluate the results of dialysis fistula aneurysm treatment in patients after KTx and to compare them to procedures performed in an end-stage renal disease (ESRD) dialyzed population. Methods: We enrolled 83 renal transplant recipients and 123 ESRD patients with dialysis fistula aneurysms qualified for surgical revision to this single-center, prospective study. The results of the surgical treatment of dialysis fistula aneurysms were analyzed, and the primary, assisted primary and secondary patency rate, percentage and type of complications were also assessed. Results: For the treatment of dialysis fistula aneurysms in transplant patients, we performed dialysis fistula excisions with fistula closure in 50 patients (60.2%), excision with primary fistula reconstruction (n = 10, 12.0%) or excision with PTFE bypasses (n = 23, 27.7%). Postoperative complications occurred in 11 patients (13.3%) during a follow-up (median follow-up, 36 months), mostly in distant periods (median time after correction procedure, 11.7 months). The most common complication was outflow stenosis, followed by hematoma, dialysis fistula thrombosis and the formation of a new aneurysm and postoperative bleeding, infection and lymphocele. The 12-month primary, primary assisted and secondary patency rates of fistulas corrected by aneurysm excision and primary reconstruction in the KTx group were all 100%; in the control ESRD group, the 12-month primary rate was 70%, and the primary assisted and secondary patency rates were 100%. The 12-month primary, primarily assisted and secondary patency rates after dialysis fistula aneurysm excision combined with PTFE bypass were better in the KTx group than in the control ESRD group (85% vs. 71.8%, 90% vs. 84.5% and 95% vs. 91.7%, respectively). Kaplan–Meier analysis showed a significant difference in primary patency (p = 0.018) and assisted primary (p = 0.018) rates and a strong tendency in secondary patency rates (p = 0.053) between the KTx and ESRD groups after dialysis fistula excisions combined with PTFE bypass. No statistically significant differences in patency rates between fistulas treated by primary reconstruction and reconstructed with PTFE bypass were observed in KTx patients. Conclusions: Reconstructions of dialysis fistula aneurysms give good long-term results, with a low risk of complications. The reconstruction of dialysis fistulas can be an effective treatment method. Thus, this is an attractive option in addition to fistula ligation or observation in patients after KTx. Reconstructions of dialysis fistula aneurysms enable the preservation of the dialysis fistula while reducing various complications.

Random Forests (RFs) typically train each tree on a bootstrap sample of the same size as the training set, i.e., bootstrap rate (BR) equals 1.0. We systematically examine how varying BR from 0.2 to 5.0 affects RF performance across 39 heterogeneous regression datasets and 16 RF configurations, evaluating with repeated two-fold cross-validation and mean squared error. Our results demonstrate that tuning the BR can yield significant improvements over the default: the best setup relied on BR \leq 1.0 for 24 datasets, BR > 1.0 for 15, and BR = 1.0 was optimal in 4 cases only. We establish a link between dataset characteristics and the preferred BR: datasets with strong global feature-target relationships favor higher BRs, while those with higher local target variance benefit from lower BRs. To further investigate this relationship, we conducted experiments on synthetic datasets with controlled noise levels. These experiments reproduce the observed bias-variance trade-off: in low-noise scenarios, higher BRs effectively reduce model bias, whereas in high-noise settings, lower BRs help reduce model variance. Overall, BR is an influential hyperparameter that should be tuned to optimize RF regression models.