Data-driven models for the prediction of coronary atherosclerotic plaque progression/regression.

Coronary artery disease is defined by the existence of atherosclerotic plaque on the arterial wall, which can cause blood flow impairment, or plaque rupture, and ultimately lead to myocardial ischemia. Intravascular ultrasound (IVUS) imaging can provide a detailed characterization of lumen and vessel features, and so plaque burden, in coronary vessels. Prediction of the regions in a vascular segment where plaque burden can either increase (progression) or decrease (regression) following a certain therapy, has remained an elusive major milestone in cardiology. Studies like IBIS-4 showed an association between plaque burden regression and high-intensity rosuvastatin therapy over 13 months. Nevertheless, it has not been possible to predict if a patient would respond in a favorable/adverse fashion to such a treatment. This work aims to (i) Develop a framework that processes lumen and vessel cross-sectional contours and extracts geometric descriptors from baseline and follow-up IVUS pullbacks; and to (ii) Develop, train, and validate a machine learning model based on baseline/follow-up IVUS datasets that predicts future percent of atheroma volume changes in coronary vascular segments using only baseline information, i.e. geometric features and clinical data. This is a post hoc analysis, revisiting the IBIS-4 study. We employed 140 arteries, from 81 patients, for which expert delineation of lumen and vessel contours were available at baseline and 13-month follow-up. Contour data from baseline and follow-up pullbacks were co-registered and then processed to extract several frame-wise features, e.g. areas, plaque burden, eccentricity, etc. Each pullback was divided into regions of interest (ROIs), following different criteria. Frame-wise features were condensed into region-wise markers using tools from statistics, signal processing, and information theory. Finally, a stratified 5-fold cross-validation strategy (20 repetitions) was used to train/validate an XGBoost regression models. A feature selection method before the model training was also applied. When the models were trained/validated on ROI defined by the difference between follow-up and baseline plaque burden, the average accuracy and Mathews correlation coefficient were 0.70 and 0.41 respectively. Using a ROI partition criterion based only on the baseline's plaque burden resulted in averages of 0.60 accuracy and 0.23 Mathews correlation coefficient. An XGBoost model was capable of predicting plaque progression/regression changes in coronary vascular segments of patients treated with rosuvastatin therapy in 13 months. The proposed method, first of its kind, successfully managed to address the problem of stratification of patients at risk of coronary plaque progression, using IVUS images and standard patient clinical data.

Comparison of endothelial shear stress between ultrathin strut bioresorbable polymer drug-eluting stent vs durable-polymer drug-eluting stent post-stent implantation: An optical coherence tomography substudy from BIOFLOW II.

BACKGROUND: Recent clinical data indicate a different performance of biodegradable polymer (BP)-drug eluting stent (DES) compared to durable polymer (DP)-DES. Whether this can be explained by a beneficial impact of BP-DES stent design on the local hemodynamic forces distribution remains unclear. OBJECTIVES: To compare endothelial shear stress (ESS) distribution after implantation of ultrathin (us) BP-DES and DP-DES and examine the association between ESS and neointimal thickness (NIT) distribution in the two devices at 9 months follow up. METHODS AND RESULTS: We retrospectively identified patients from the BIOFLOW II trial that had undergone OCT imaging. OCT data were utilized to reconstruct the surface of the stented segment at baseline and 9 months follow-up, simulate blood flow, and measure ESS and NIT in the stented segment. The patients were divided into 3 groups depending on whether DP-DES (N = 8, n = 56,160 sectors), BP-DES with a stent diameter of >3 mm (strut thickness of 80 µm, N = 6, n = 36,504 sectors), or BP-DES with a stent diameter of ≤3 mm (strut thickness of 60 µm, N = 8, n = 50,040 sectors) were used for treatment. The ESS, and NIT distribution and the association of these two variables were estimated and compared among the 3 groups. RESULTS: In the DP-DES group mean NIT was 0.18 ± 0.17 mm and ESS 1.68 ± 1.66 Pa; for the BP-DES ≤3 mm group the NIT was 0.17 ± 0.11 mm and ESS 1.49 ± 1.24 Pa and for the BP-DES >3 mm group 0.20 ± 0.23 mm and 1.42 ± 1.24 Pa respectively (p < 0.001 for both NIT and ESS comparisons across groups). A negative correlation between NIT and baseline ESS was found, the correlation coefficient for all the stented segments was -0.33, p < 0.001. CONCLUSION: In this OCT sub-study of the BIOFLOW II trial, the NIT was statistically different between groups of patients treated with BP-DES and DP-DES. In addition, regions of low ESS were associated with increased NIT in all studied devices.

Quantitative coronary three-dimensional geometry and its association with atherosclerotic disease burden and composition.

BACKGROUND: Isolate features of the coronary anatomy have been associated with the pathophysiology of atherosclerotic disease. Computational methods have been described to allow precise quantification of the complex three-dimensional (3D) coronary geometry. The present study tested whether quantitative parameters that describe the spatial 3D coronary geometry is associated with the extension and composition of the underlying coronary artery disease (CAD). METHODS: Patients with CAD scheduled for percutaneous intervention were investigated with coronary computed tomography angiography (CCTA), and invasive coronary angiography, and virtual histology intravascular ultrasound (IVUS-VH). For all target vessels, 3D centerlines were extracted from CCTA images and processed to quantify 23 geometric indexes, grouped into 3 main categories as follows: (i) length-based; (ii) curvature-based, torsion-based, and curvature/torsion-combined; (iii) vessel path-based. The geometric variables were compared with IVUS-VH parameters assessing the extent and composition of coronary atherosclerosis. RESULTS: A total of 36 coronary patients (99 vessels) comprised the study population. From the 23 geometric indexes, 18 parameters were significantly (p < 0.05) associated with at least 1 IVUS-VH parameter at a univariate analysis. All three main geometric categories provided parameters significantly related with atherosclerosis variables. The 3D geometric indexes were associated with the degree of atherosclerotic extension, as well as with plaque composition. Geometric features remained significantly associated with all IVUS-VH parameters even after multivariate adjustment for clinical characteristics. CONCLUSIONS: Quantitative 3D vessel morphology emerges as a relevant factor associated with atherosclerosis in patients with established CAD.

Pleiotropic antifibrotic actions of aspirin-triggered resolvin D1 in the lungs.

Introduction: Pulmonary fibrosis is a destructive, progressive disease that dramatically reduces life quality of patients, ultimately leading to death. Therapeutic regimens for pulmonary fibrosis have shown limited benefits, hence justifying the efforts to evaluate the outcome of alternative treatments. Methods: Using a mouse model of bleomycin (BLM)-induced lung fibrosis, in the current work we asked whether treatment with pro-resolution molecules, such as pro-resolving lipid mediators (SPMs) could ameliorate pulmonary fibrosis. To this end, we injected aspirin-triggered resolvin D1 (7S,8R,17R-trihydroxy-4Z,9E,11E,13Z,15E19Z-docosahexaenoic acid; ATRvD1; i.v.) 7 and 10 days after BLM (intratracheal) challenge and samples were two weeks later. Results and discussion: Assessment of outcome in the lung tissues revealed that ATRvD1 partially restored lung architecture, reduced leukocyte infiltration, and inhibited formation of interstitial edema. In addition, lung tissues from BLM-induced mice treated with ATRvD1 displayed reduced levels of TNF-α, MCP-1, IL-1-ß, and TGF-ß. Of further interest, ATRvD1 decreased lung tissue expression of MMP-9, without affecting TIMP-1. Highlighting the beneficial effects of ATRvD1, we found reduced deposition of collagen and fibronectin in the lung tissues. Congruent with the anti-fibrotic effects that ATRvD1 exerted in lung tissues, α-SMA expression was decreased, suggesting that myofibroblast differentiation was inhibited by ATRvD1. Turning to culture systems, we next showed that ATRvD1 impaired TGF-ß-induced fibroblast differentiation into myofibroblast. After showing that ATRvD1 hampered extracellular vesicles (EVs) release in the supernatants from TGF-ß-stimulated cultures of mouse macrophages, we verified that ATRvD1 also inhibited the release of EVs in the bronco-alveolar lavage (BAL) fluid of BLM-induced mice. Motivated by studies showing that BLM-induced lung fibrosis is linked to angiogenesis, we asked whether ATRvD1 could blunt BLM-induced angiogenesis in the hamster cheek pouch model (HCP). Indeed, our intravital microscopy studies confirmed that ATRvD1 abrogates BLM-induced angiogenesis. Collectively, our findings suggest that treatment of pulmonary fibrosis patients with ATRvD1 deserves to be explored as a therapeutic option in the clinical setting.

A computational study of aortic insufficiency in patients supported with continuous flow left ventricular assist devices: Is it time for a paradigm shift in management?

Background: De novo aortic insufficiency (AI) following continuous flow left ventricular assist device (CF-LVAD) implantation is a common complication. Traditional early management utilizes speed augmentation to overcome the regurgitant flow in an attempt to augment net forward flow, but this strategy increases the aortic transvalvular gradient which predisposes the patient to progressive aortic valve pathology and may have deleterious effects on aortic shear stress and right ventricular (RV) function. Materials and methods: We employed a closed-loop lumped-parameter mathematical model of the cardiovascular system including the four cardiac chambers with corresponding valves, pulmonary and systemic circulations, and the LVAD. The model is used to generate boundary conditions which are prescribed in blood flow simulations performed in a three-dimensional (3D) model of the ascending aorta, aortic arch, and thoracic descending aorta. Using the models, impact of various patient management strategies, including speed augmentation and pharmacological treatment on systemic and pulmonary (PA) vasculature, were investigated for four typical phenotypes of LVAD patients with varying degrees of RV to PA coupling and AI severity. Results: The introduction of mild/moderate or severe AI to the coupled RV and pulmonary artery at a speed of 5,500 RPM led to a reduction in net flow from 5.4 L/min (no AI) to 4.5 L/min (mild/moderate) to 2.1 L/min (severe). RV coupling ratio (Ees/Ea) decreased from 1.01 (no AI) to 0.96 (mild/moderate) to 0.76 (severe). Increasing LVAD speed to 6,400 RPM in the severe AI and coupled scenario, led to a 42% increase in net flow and a 16% increase in regurgitant flow (RF) with a nominal decrease of 1.6% in RV myocardial oxygen consumption (MVO2). Blood pressure control with the coupled RV with severe AI at 5,500 RPM led to an 81% increase in net flow with a 15% reduction of RF and an 8% reduction in RV MVO2. With an uncoupled RV, the introduction of mild/moderate or severe AI at a speed of 5,500 RPM led to a reduction in net flow from 5.0 L/min (no AI) to 4.0 L/min (mild/moderate) to 1.8 L/min (severe). Increasing the speed to 6,400 RPM with severe AI and an uncoupled RV increased net flow by 45%, RF by 15% and reduced RV MVO2 by 1.1%. For the uncoupled RV with severe AI, blood pressure control alone led to a 22% increase in net flow, 4.2% reduction in RF, and 3.9% reduction in RV MVO2; pulmonary vasodilation alone led to a 18% increase in net flow, 7% reduction in RF, and 26% reduction in RV MVO2; whereas, combined BP control and pulmonary vasodilation led to a 113% increase in net flow, 20% reduction in RF and 31% reduction in RV MVO2. Compared to speed augmentation, blood pressure control consistently resulted in a reduction in WSS throughout the proximal regions of the arterial system. Conclusion: Speed augmentation to overcome AI in patients supported by CF-LVAD appears to augment flow but also increases RF and WSS in the aorta, and reduces RV MVO2. Aggressive blood pressure control and pulmonary vasodilation, particularly in those patients with an uncoupled RV can improve net flow with more advantageous effects on the RV and AI RF.

Scaling laws and the left main coronary artery bifurcation. A combination of geometric and simulation analyses.

The geometry of coronary arteries is believed to play the role as an atherosclerotic risk factor on its own. The full characterization of the normal coronary network has been reported in the literature. Reports on the integration of geometry and functional data for normal coronary vessels started to proliferate more recently. In this work, we analyze and integrate the geometric data retrieved from angiography images of the left main coronary bifurcation in angiographically normal patients and hemodynamic data generated from blood flow models to analyze the role of allometric laws and the connection between flow distribution and wall shear stress loads on the left anterior descending and left circumflex arteries. This in-silico study contributes to the characterization of normal coronary anatomy and its impact on the hemodynamic shear stresses acting over the vessel wall, shedding light on the impact of geometry-based versus simulation-based hypotheses to define boundary conditions for numerical simulations. We discuss the role of the wall shear stress corresponding to scenarios adopted by the scientific community and the ones proposed in this study. For the simulation-based hypothesis, we propose an iterative strategy to define boundary conditions at the main left coronary bifurcation, such that wall shear stresses are matched between the left descending and left circumflex arteries. From this study, we conclude that a one-fits-all power law exponent of 7/3 results in an good trade-off between computational cost and wall shear stress balance between daughter vessels.

Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets.

Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm2, [1.1,-1.5]mm2, while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.

Simultaneous assessment of coronary stenosis relevance with automated computed tomography angiography and intravascular ultrasound analyses and fractional flow reserve.

OBJECTIVES: To assess the diagnostic performance of computed tomography angiography (CTA) and intravascular ultrasound (IVUS) derived minimum lumen areas (MLA) from the same lesions that correspond to an FFR ≤0.80. METHODS AND RESULTS: A total of 24 patients (33 arteries) were collected retrospectively according to the following inclusion criteria: presence of a CTA diagnostic followed by an IVUS and FFR percutaneous coronary procedures. CTA and IVUS lumen contours were automatically performed using previously validated methods.The correlation between CTA and IVUS for the MLA was r = 0.45. In terms of MLA, the mean difference between CTA and IVUS was 0.81 mm2. Of note, a much smaller CTA-derived MLA (2.10 mm2) was found to be related to significant FFR lesions compared to that of the MLA derived from IVUS (3.19 mm2). The area under the curve, accuracy, sensitivity and specificity for this CTA-derived MLA were 0.80, 0.76, 0.50 and 0.87, respectively, while these values for IVUS-derived MLA were 0.87, 0.85, 0.80 and 0.87. CONCLUSIONS: Computed tomography angiography and intravascular ultrasound-derived minimum lumen areas have moderate diagnostic efficiency, albeit slightly better for IVUS, in identifying hemodynamically severe coronary stenoses. The utility of MLA, automatically derived from either CTA or IVUS as an alternative to FFR to guide the decision to revascularize, should be tested clinically.

A simple coronary blood flow model to study the collateral flow index.

In this work, we present a novel modeling framework to investigate the effects of collateral circulation into the coronary blood flow physiology. A prototypical model of the coronary tree, integrated with the concept of Collateral Flow Index (CFI), is employed to gain insight about the role of model parameters associated with the collateral circuitry, which results in physically-realizable solutions for specific CFI data. Then, we discuss the mathematical feasibility of pressure-derived CFI, anatomical implications and practical considerations involving the estimation of model parameters in collateral connections. A sensitivity analysis is carried out, and the investigation of the impact of the collateral circulation on FFR values is also addressed.

Coronary arterial geometry: A comprehensive comparison of two imaging modalities.

The characterization of vascular geometry is a fundamental step towards the correct interpretation of coronary artery disease. In this work, we report a comprehensive comparison of the geometry featured by coronary vessels as obtained from coronary computed tomography angiography (CCTA) and the combination of intravascular ultrasound (IVUS) with bi-plane angiography (AX) modalities. We analyzed 34 vessels from 28 patients with coronary disease, which were deferred to CCTA and IVUS procedures. We discuss agreement and discrepancies between several geometric indexes extracted from vascular geometries. Such an analysis allows us to understand to which extent the coronary vascular geometry can be reliable in the interpretation of geometric risk factors, and as a surrogate to characterize coronary artery disease.

A low-cost portable simulator of a domestic cat larynx for teaching endotracheal intubation.

OBJECTIVE: To design and construct an affordable simulator of the cat larynx for training intubation maneuvers and to share the designs for its fabrication. STUDY DESIGN: Research and development study. ANIMALS: A domestic cat. METHODS: The cadaver of a cat, dead by natural causes, was frozen in sternal recumbency with the neck extended and the mouth wide open. A computed tomography image was acquired and used to construct a digital three-dimensional (3D) model of the pharynx and trachea. A digitally adapted model was 3D-printed and used to generate a silicone model of these structures, which was placed within a wooden container. The quality of the simulator was assessed by 46 veterinary anesthesiologists and veterinarians with experience in tracheal intubation maneuvers, and their opinions were obtained through an anonymous questionnaire. RESULTS: Several preliminary prototypes were assessed regarding stability, texture and cost. Finally, a silicone model of a cat larynx (LaryngoCUBE) was produced and encased in a wooden container. Results from the questionnaire showed high scores regarding anatomy, tissue texture and intubation maneuver realism, compared with the real procedure. CONCLUSIONS: and clinical relevance Use of LaryngoCUBE as a training tool may improve the skills of students and reduce the use of animals for teaching endotracheal intubation. Blueprints and computational models are provided online so that the simulator can be fully reproduced.

Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets.

Aims: Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. Methods and results: First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs) to regress the final lumen boundary. The dataset consisted of 85 IVUS pullbacks (52 patients). The dataset was partitioned at the pullback-level using 73 pullbacks for training (20 586 frames), 6 pullbacks for validation (1692 frames), and 6 for testing (1692 frames). The degree of overlapping, between the ground truth and ML contours, median (interquartile range, IQR) systematically increased from 0.896 (0.874-0.933) for MF1 to 0.925 (0.911-0.948) for MF11. The median (IQR) of the distance error was also reduced from 3.83 (2.94-4.98)% for MF1 to 3.02 (2.25-3.95)% for MF11-GP. The corresponding median (IQR) in the lumen area error remained between 5.49 (2.50-10.50)% for MF1 and 5.12 (2.15-9.00)% for MF11-GP. The dispersion in the relative distance and area errors consistently decreased as we increased the number of frames, and also when the GP regressor was coupled to the MFCNN output. Conclusion: These results demonstrate that the proposed ML approach is suitable to effectively segment the lumen boundary in IVUS scans, reducing the burden of costly and time-consuming manual delineation.

Coronary fractional flow reserve derived from intravascular ultrasound imaging: Validation of a new computational method of fusion between anatomy and physiology.

OBJECTIVES: To evaluate the diagnostic performance of a novel computational algorithm based on three-dimensional intravascular ultrasound (IVUS) imaging in estimating fractional flow reserve (IVUSFR ), compared to gold-standard invasive measurements (FFRINVAS ). BACKGROUND: IVUS provides accurate anatomical evaluation of the lumen and vessel wall and has been validated as a useful tool to guide percutaneous coronary intervention. However, IVUS poorly represents the functional status (i.e., flow-related information) of the imaged vessel. METHODS: Patients with known or suspected stable coronary disease scheduled for elective cardiac catheterization underwent FFRINVAS measurement and IVUS imaging in the same procedure to evaluate intermediate lesions. A processing methodology was applied on IVUS to generate a computational mesh condensing the geometric characteristics of the vessel. Computation of IVUSFR was obtained from patient-level morphological definition of arterial districts and from territory-specific boundary conditions. FFRINVAS measurements were dichotomized at the 0.80 threshold to define hemodynamically significant lesions. RESULTS: A total of 24 patients with 34 vessels were analyzed. IVUSFR significantly correlated (r = 0.79; P < 0.001) and showed good agreement with FFRINVAS , with a mean difference of -0.008 ± 0.067 (P = 0.47). IVUSFR presented an overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 91%, 89%, 92%, 80%, and 96%, respectively, to detect significant stenosis. CONCLUSION: The computational processing of IVUSFR is a new method that allows the evaluation of the functional significance of coronary stenosis in an accurate way, enriching the anatomical information of grayscale IVUS.

Computer-aided quantification of microvascular networks: Application to alterations due to pathological angiogenesis in the hamster.

Angiogenesis is both a physiological and a pathological process of great complexity, which is difficult to measure objectively and automatically. The hamster cheek pouch (HCP) prepared for intravital-microscopy (IVM) has been used to characterize microvascular functions in many studies and was chosen to investigate microvascular characteristics observed in normal non-infected hamsters as compared to those HCPs parasitized by Trypanosoma cruzi. Images of HCPs captured at IVM were subjected to computer based measurements of angiogenesis and histamine-induced macromolecular (FITC-dextran) leakage with an image segmentation approach that has the capacity to discriminate between fluorescence emitted by macromolecular tracers inside the vasculature and in the extravascular space. We present such an automatic segmentation methodology using known tools from image processing field that, to our knowledge, have not been tested in IVM images. We have compared this methodology with a recently published segmentation strategy based on image intensity thresholding. Our method renders an accurate and robust segmentation of blood vessels for different microvascular scenarios, normal and pathological. Application of the proposed strategy for objective and automatic measurement of angiogenesis detection was explored in detail.

On the search of arterial geometry heritability.

BACKGROUND: Geometrical risk factors for CAD have been previously proposed before. To date, however, the effect of those factors is not conclusive, and remains as an open research field. Here, we hypothesize that some of these factors have a genetic component explaining inter-individual variability. OBJECTIVE: To detect heritability indicators of the coronary arterial geometry. MATERIALS AND METHODS: A patient sample of 48 individuals, consisting of 24 siblings, was used. Three dimensional geometry of the LAD, LCx and RCA were reconstructed from standard CCTA. Arterial models were characterized in terms of 20+ geometric descriptors (phenotypes). A comprehensive statistical analysis to detect potential heritability of such phenotypes was employed. Heritability was assessed by means of several statistical indexes. Finally, the association of phenotypes to stenotic lesion is also reported. RESULTS: The RCA scored positive indications for heritability in 15+ phenotypes, while the LAD in 10 and the LCx in only 3 phenotypes. Association between presence of lesion and phenotypes was higher in the LAD, 10+ phenotypes, while for the LCx only 2 phenotypes were significantly associated, and none association was found for the RCA. CONCLUSION: The RCA showed potential heritability for the largest number of phenotypes, followed by the LAD. The LCx presents the weaker association of morphology among siblings. Regarding lesion-geometry associations, the there are hints of an underlying relation in the LAD, the LCx featured a weaker association and the RCA showed none. This difference could be related to the different hemodynamic environments in these arteries.