Combined arteriovenous fistula and venous aneurysm following knee arthrodesis

We study a case of a 65-year-old woman who developed popliteal arteriovenous fistula (AVF) and venous aneurysm following left knee arthrodesis. Presenting features included left popliteal and calf pain, a tender pulsatile mass posterior to her left knee, popliteal bruit and a thrill at the popliteal fossa and ankle. Left femoral angiography showed an AVF arising from the right tibioperoneal trunk and an aneurysm at the level of the AVF. Findings at open investigation included AVF between the tibioperoneal trunk and the popliteal vein, and a venous aneurysm arising from the popliteal vein opposite the neck of the arteriovenous communication. The aneurysm and fistula were repaired using prolene suture.

Overexpression of transforming growth factor-beta is associated with increased hyaluronan content and impairment of repair in Marfan syndrome aortic aneurysm

Background-Marfan syndrome (MFS), a condition caused by fibrillin-1 gene mutation is associated with aortic aneurysm that shows elastic lamellae disruption, accumulation of glycosaminoglycans, and vascular smooth muscle cell (VSMC) apoptosis with minimal inflammatory response. We examined aneurysm tissue and cultured cells for expression of transforming growth factor-beta1 to -beta3 (TGF beta 1 to 3), hyaluronan content, apoptosis, markers of cell migration, and infiltration of vascular progenitor cells (CD34). Methods and Results-MFS aortic aneurysm (6 males, 5 females; age 8 to 78 years) and normal aorta (5 males, 3 females; age 22 to 56 years) were used. Immunohistochemistry showed increased expression of TGF beta 1 to 3, hyaluronan, and CD34-positive microcapillaries in MFS aneurysm compared with control. There was increased expression of TGF beta 1 to 3 and hyaluronan in MFS cultured VSMCs, adventitial fibroblasts (AF), and skin fibroblasts (SF). Apoptosis was increased in MFS (VSMC: mean cell loss in MFS 29%, n of subjects = 5, versus control 8%, n = 3, P < 0.05; AF: 28%, n = 5 versus 7%, n = 5, P < 0.05; SF: 29%, n = 3 versus 4%, n = 3, not significant). In MFS, there was a 2-fold increase in adventitial microcapillaries containing CD34-positive cells compared with control tissue. Scratch wound assay showed absence of CD44, MT1-MMP, and beta-3 integrin at the leading edge of migration in MFS indicating altered directional migration. Western blot showed increased expression of TGF beta 1 to 3 in MFS but no change in expression of CD44, MT1-MMP, or beta-3 integrin compared with controls. Conclusions-There was overexpression of TGF-beta in MFS associated with altered hyaluronan synthesis, increased apoptosis, impaired progenitor cell recruitment, and abnormal directional migration. These factors limit tissue repair and are likely to contribute to aneurysm development.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study

Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

The risk of re-intervention after endovascular aortic aneurysm repair

This thesis studies survival analysis techniques dealing with censoring to produce predictive tools that predict the risk of endovascular aortic aneurysm repair (EVAR) re-intervention. Censoring indicates that some patients do not continue follow up, so their outcome class is unknown. Methods dealing with censoring have drawbacks and cannot handle the high censoring of the two EVAR datasets collected. Therefore, this thesis presents a new solution to high censoring by modifying an approach that was incapable of differentiating between risks groups of aortic complications. Feature selection (FS) becomes complicated with censoring. Most survival FS methods depends on Cox's model, however machine learning classifiers (MLC) are preferred. Few methods adopted MLC to perform survival FS, but they cannot be used with high censoring. This thesis proposes two FS methods which use MLC to evaluate features. The two FS methods use the new solution to deal with censoring. They combine factor analysis with greedy stepwise FS search which allows eliminated features to enter the FS process. The first FS method searches for the best neural networks' configuration and subset of features. The second approach combines support vector machines, neural networks, and K nearest neighbor classifiers using simple and weighted majority voting to construct a multiple classifier system (MCS) for improving the performance of individual classifiers. It presents a new hybrid FS process by using MCS as a wrapper method and merging it with the iterated feature ranking filter method to further reduce the features. The proposed techniques outperformed FS methods based on Cox's model such as; Akaike and Bayesian information criteria, and least absolute shrinkage and selector operator in the log-rank test's p-values, sensitivity, and concordance. This proves that the proposed techniques are more powerful in correctly predicting the risk of re-intervention. Consequently, they enable doctors to set patients’ appropriate future observation plan.