Developing Theoretical Models for Atherosclerotic Lesions: A Methodological Approach Using Interdisciplinary Insights.

Atherosclerosis, a leading cause of cardiovascular disease, necessitates advanced and innovative modeling techniques to better understand and predict plaque dynamics. The present work presents two distinct hypothetical models inspired by different research fields: the logistic map from chaos theory and Markov models from stochastic processes. The logistic map effectively models the nonlinear progression and sudden changes in plaque stability, reflecting the chaotic nature of atherosclerotic events. In contrast, Markov models, including traditional Markov chains, spatial Markov models, and Markov random fields, provide a probabilistic framework to assess plaque stability and transitions. Spatial Markov models, visualized through heatmaps, highlight the spatial distribution of transition probabilities, emphasizing local interactions and dependencies. Markov random fields incorporate complex spatial interactions, inspired by advances in physics and computational biology, but present challenges in parameter estimation and computational complexity. While these hypothetical models offer promising insights, they require rigorous validation with real-world data to confirm their accuracy and applicability. This study underscores the importance of interdisciplinary approaches in developing theoretical models for atherosclerotic plaques.

Challenges of Predicting Arteriovenous Access Survival Prior to Conversion from Catheter.

OBJECTIVE: The decision to convert from catheter to arteriovenous access is difficult yet very important. The ability to accurately predict fistula survival prior to surgery would significantly improve the decision making process. Many previously investigated demographic and clinical features have been associated with fistula failure. However, it is not conclusively understood how reliable predictions based on these parameters are at an individual level. The aim of this study was to investigate the probability of arteriovenous fistula maturation and survival after conversion using machine learning workflows. METHODS: A retrospective cohort study on multicentre data from a large North American dialysis organisation was conducted. The study population comprised 73 031 chronic in centre haemodialysis patients. The dataset included 49 variables including demographic and clinical features. Two distinct feature selection and prediction pipelines were used: LASSO regression and Boruta followed by a random forest classifier. Predictions were facilitated for re-conversion to catheter within one year. Additionally, all cause mortality predictions were conducted to serve as a comparator. RESULTS: In total, 38 151 patients (52.2%) had complete data and made up the main cohort. Sensitivity analyses were conducted in 67 421 patients (92.3%) after eliminating variables with a high proportion of missing data points. Selected features diverged between datasets and workflows. A previously failed arteriovenous access appeared to be the most stable predictor for subsequent failure. Prediction of re-conversion based on the demographic and clinical information resulted in an area under the receiver operating characteristic curve (ROCAUC) between 0.541 and 0.571, whereas models predicting all cause mortality performed considerably better (ROCAUC 0.662 - 0.683). CONCLUSION: While group level depiction of major adverse outcomes after catheter to arteriovenous fistula or graft conversion is possible using the included variables, patient level predictions are associated with limited performance. Factors during and after fistula creation as well as biomolecular and genetic biomarkers might be more relevant predictors of fistula survival than baseline clinical conditions.

Abdominal Aortic Aneurysm Detection in Bioelectrical Impedance Cardiovascular Screenings-A Pilot Study.

Screening and diagnosing abdominal aortic aneurysms (AAA) are currently dependent on imaging studies such as ultrasound or computed tomography angiography. All imaging studies offer distinct advantages but also suffer from inherent limitations such as examiner dependency or ionizing radiation. Bioelectrical impedance analysis has previously been investigated with respect to its use in the detection of several cardiovascular and renal pathologies. The present pilot study assessed the feasibility of AAA detection based on bioimpedance analysis. In this single-center exploratory pilot study, measurements were conducted among three different cohorts: patients with AAA, end-stage renal disease patients without AAA, and healthy controls. The device used in the study, CombynECG, is an open-market accessible device for segmental bioelectrical impedance analysis. The data was preprocessed and used to train four different machine learning models on a randomized training sample (80% of the full dataset). Each model was then evaluated on a test set (20% of the full dataset). The total sample included 22 patients with AAA, 16 chronic kidney disease patients, and 23 healthy controls. All four models showed strong predictive performance in the test partitions. Specificity ranged from 71.4 to 100%, while sensitivity ranged from 66.7 to 100%. The best-performing model had 100% accuracy for classification when applied to the test sample. Additionally, an exploratory analysis to approximate the maximum AAA diameter was conducted. An association analysis revealed several impedance parameters that might possess predictive ability with respect to aneurysm size. AAA detection via bioelectrical impedance analysis is technically feasible and appears to be a promising technology for large-scale clinical studies and routine clinical screening assessments.

Endoleak Detection after Endovascular Aortic Repair via Coded-Excitation Ultrasound-A Feasibility Study.

Endoleaks are the most common complication after endovascular aortic repair (EVAR). Their correct identification is one of the main objectives of surveillance protocols after EVAR. So far, computed tomography angiography (CTA), contrast-enhanced (CEUS) and Duplex ultrasound (DUS), as well as magnetic resonance angiography, have been investigated for their ability to detect endoleaks. In general, all technologies have distinct benefits and disadvantages, with CTA and CEUS emerging as the reference standard for surveillance after EVAR. However, they are both contrast-enhancer-dependent, and CTA additionally exposes patients to ionizing radiation. In the present study, we investigated B-Flow, a type of coded-excitation ultrasound that was specifically designed to optimize the visualization of blood flow, for its ability to detect endoleaks, and compared its performance to CEUS, CTA, and DUS. In total, 34 patients were included in the analysis that accumulated in 43 distinct B-Flow investigations. They underwent a total of 132 imaging investigations. Agreement between B-Flow and other imaging modalities was high (>80.0%), while inter-method reliability can be interpreted as good. However, with B-Flow, six and one endoleaks would have been missed compared to CEUS and CTA, respectively. Regarding endoleak classification, all metrics were lower but retained an adequate level of comparison. In a subset of patients requiring intervention, B-Flow had 100% accuracy regarding both endoleak detection and classification. Ultrasonography enables endoleak detection and classification without the need for pharmaceutical contrast enhancement or radiation. Ultrasound coded-excitation imaging in the application of B-Flow could further simplify surveillance after EVAR by offering adequate accuracy without requiring intravenous contrast enhancement. Our findings may promote subsequent investigations of coded-excitation imaging for endoleak detection and classification in the surveillance after EVAR.

Clinical Applications of B-Flow Ultrasound: A Scoping Review of the Literature.

Coded excitation ultrasound investigations have the potential to augment the resolution, increase the efficiency, and expand the possibilities of noninvasive diagnostic imaging. B-Flow ultrasound, a type of digitally encoded imaging, was developed more than 20 years ago with the aim to optimize the visualization of blood flow. It has been investigated for a plethora of applications so far. A scoping review regarding its clinical applications was conducted based on a systematic literature research. B-Flow has been investigated in various anatomic locations and pathologies. However, previous research is limited by small sample sizes, the rare occurrence of elaborate study designs, the reliance on subjective reports and qualitative data, as well as several potential biases. While results are in general promising, it should therefore still be considered an emerging technology. Nevertheless, the limitations can be addressed in future research and the potential to expand its applications make B-Flow an interesting candidate for further investigations.