The objective of this study is to find a correlation between the abdominal aortic aneurysm (AAA) geometric parameters, wall stress shear (WSS), abdominal flow patterns, intraluminal thrombus (ILT), and AAA arterial wall rupture using computational fluid dynamics (CFD). Real AAA 3D models were created by three-dimensional (3D) reconstruction of in vivo acquired computed tomography (CT) images from 5 patients. Based on 3D AAA models, high quality volume meshes were created using an optimal tetrahedral aspect ratio for the whole domain. In order to quantify the WSS and the recirculation inside the AAA, a 3D CFD using finite elements analysis was used. The CFD computation was performed assuming that the arterial wall is rigid and the blood is considered a homogeneous Newtonian fluid with a density of 1050 kg/m3 and a kinematic viscosity of 4 × 10−3 Pa·s. Parallelization procedures were used in order to increase the performance of the CFD calculations. A relation between AAA geometric parameters (asymmetry index (β), saccular index (γ), deformation diameter ratio (χ), and tortuosity index (ε)) and hemodynamic loads was observed, and it could be used as a potential predictor of AAA arterial wall rupture and potential ILT formation.

We developed and independently validated a rheumatoid arthritis (RA) mortality prediction model using the machine learning method Random Survival Forests (RSF). Two independent cohorts from Madrid (Spain) were used: the Hospital Clínico San Carlos RA Cohort (HCSC-RAC; training; 1,461 patients), and the Hospital Universitario de La Princesa Early Arthritis Register Longitudinal study (PEARL; validation; 280 patients). Demographic and clinical-related variables collected during the first two years after disease diagnosis were used. 148 and 21 patients from HCSC-RAC and PEARL died during a median follow-up time of 4.3 and 5.0 years, respectively. Age at diagnosis, median erythrocyte sedimentation rate, and number of hospital admissions showed the higher predictive capacity. Prediction errors in the training and validation cohorts were 0.187 and 0.233, respectively. A survival tree identified five mortality risk groups using the predicted ensemble mortality. After 1 and 7 years of follow-up, time-dependent specificity and sensitivity in the validation cohort were 0.79–0.80 and 0.43–0.48, respectively, using the cut-off value dividing the two lower risk categories. Calibration curves showed overestimation of the mortality risk in the validation cohort. In conclusion, we were able to develop a clinical prediction model for RA mortality using RSF, providing evidence for further work on external validation.

Management and follow-up of chronic aortic dissections continues to be a clinical challenge due to progressive aortic dilatation. To predict dilatation, guidelines suggest follow-up of the aortic diameter. However, dilatation is triggered by haemodynamic parameters (pressure and wall shear stresses (WSS)), and geometry of false (FL) and true lumen (TL). We aimed at a better understanding of TL and FL haemodynamics by performing in-silico (CFD) and invitro studies on an idealized dissected aorta and compared this to a typical patient. We observed an increase in diastolic pressure and wall stress in the FL and the presence of diastolic retrograde flow. The inflow jet increased WSS at the proximal FL while a large variability in WSS was induced distally, all being risk factors for wall weakening. In-silico, in-vitro and in-vivo findings were very similar and complementary, showing that their combination can help in a more integrated and extensive assessment of aortic dissections, improving understanding of the haemodynamic conditions and related clinical evolution.

The morphometry of the abdominal aortic aneurysms (AAA) has been recognized as one of the main factors that may predispose its rupture. The variation of the AAA morphometry, over time, induces modifications in hemodynamic behavior which, in turn, alters the spatial and temporal distribution of hemodynamic stress on the aneurismatic wall, establishing a bidirectional process that can influence the rupture phenomenon. In order to evaluate potential correlations between the main geometric parameters characterizing the AAA and hemodynamic stresses, 13 unrupture AAA patient-specific models were created. To AAA geometric characterization, 12 indices based on lumen center line were defined and determined. The computing of temporal and spatial distributions of hemodynamic stresses was conducted through Computational Fluid Dynamics. Statistical techniques were used to assess the relationships between the hemodynamic parameters and the different geometrical indices of the AAA. Regression analyses were conducted to obtain linear predictor models for hemodynamic stresses using the different indices defined in this paper as predictor variables. The statistical analysis confirmed that the length L, the asymmetry and the saccular index significantly influenced the hemodynamic stresses. The results obtained show the potential of the use of statistical techniques in predicting the rupture risk of patient-specific AAA.