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.

Polymer film formulations for the preparation of enteric pharmaceutical capsules

Objectives. Standard pharmaceutical capsules are designed to dissolve in the acidic environment of the stomach releasing the encapsulated contents for absorption. When release is required further along the gastrointestinal tract capsules can be coated with acid insoluble polymers to enable passage through the stomach and dissolution in the intestine. This paper describes formulations that have the potential to be used to produce two-piece hard capsules for post-gastric delivery without the requirement of an exterior coat. Methods. The formulation uses three polysaccharides: sodium alginate, hypromellose and gellan gum to provide acid insolubility and the ability to form capsules using standard industrial equipment. Key findings. The rheological profile, on cooling, of the base material, water content and thickness of the films were shown to be comparable with those of commercial capsules. The capsules remained intact for 2 h in 100 mm HCl at pH 1.2, and within 5 min of being removed from the acid and submerged in phosphate-buffered saline at pH 6.8 were ruptured. Conclusions. Selected formulations from this study have potential for use as delayed release capsules.

Cervicovaginal vascular endothelial growth factor (VEGF) and risk of preterm birth (PTB)

Objective - During pregnancy, the human cervix undergoes angiogenic transformations. VEGF is expressed in cervical stroma and is proposed to play key roles in the process of cervical ripening and dilation. This study was conducted to evaluate whether cervical secretion of VEGF can be of clinical value in predicting impending PTB. Study Design - In an observational prospective cohort study, we analyzed cervical fluid samples from 103 pregnant women (GA: median [IQR]: 28 [25-31] wks) who presented for either a routine prenatal visit (n=61) or for evaluation of threatened preterm labor (n=42). Cervical secretions were collected under a standard protocol which was followed in all cases. Cervical length (CL) was assessed by transvaginal ultrasound using well-established criteria. Dilation was evaluated by digital exam performed only after collection of the biological samples. VEGF levels were immunoassayed by investigators unaware of the clinical outcome. Main exclusion criteria were ruptured membranes, active labor, vaginal bleeding, vaginal exam or intercourse within 24h. Results were analyzed with and without normalization for total protein. Results - 1) Clinical characteristics of the cohort are presented in Table;2) VEGF was detectable in all specimens, with no correlation between its levels, CL, twins or GA at collection; 3) There was an inverse correlation between VEGF and cervical dilation (R=-0.646, P=0.003); 4) Women with cervical dilation =1 cm had lower VEGF compared to those with a closed cervix (P=0.003); 5) Women who experienced PTB within 14 days (n=11) had lower VEGF (P=0.003); 6) A free VEGF level of =600 pg/mL had a sensitivity, specificity, +LR and -LR of 70%, 95%, 13.5 and 0.3, respectively in predicting PTB within 14 days. Conclusions - Low VEGF levels in the cervicovaginal secretions of pregnant women are associated with an increased risk of PTB within 2 weeks of collection. Active engagement of VEGF in the process of cervical ripening and dilatation and/or increased affinity of extracellular matrix components for VEGF may provide explanation for our findings.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.