Development of physiologically-informed computational coronary artery plaques for use in virtual imaging trials.

BACKGROUND: As a leading cause of death, worldwide, cardiovascular disease is of great clinical importance. Among cardiovascular diseases, coronary artery disease (CAD) is a key contributor, and it is the attributed cause of death for 10% of all deaths annually. The prevalence of CAD is commensurate with the rise in new medical imaging technologies intended to aid in its diagnosis and treatment. The necessary clinical trials required to validate and optimize these technologies require a large cohort of carefully controlled patients, considerable time to complete, and can be prohibitively expensive. A safer, faster, less expensive alternative is using virtual imaging trials (VITs), utilizing virtual patients or phantoms combined with accurate computer models of imaging devices. PURPOSE: In this work, we develop realistic, physiologically-informed models for coronary plaques for application in cardiac imaging VITs. METHODS: Histology images of plaques at micron-level resolution were used to train a deep convolutional generative adversarial network (DC-GAN) to create a library of anatomically variable plaque models with clinical anatomical realism. The stability of each plaque was evaluated by finite element analysis (FEA) in which plaque components and vessels were meshed as volumes, modeled as specialized tissues, and subjected to the range of normal coronary blood pressures. To demonstrate the utility of the plaque models, we combined them with the whole-body XCAT computational phantom to perform initial simulations comparing standard energy-integrating detector (EID) CT with photon-counting detector (PCD) CT. RESULTS: Our results show the network is capable of generating realistic, anatomically variable plaques. Our simulation results provide an initial demonstration of the utility of the generated plaque models as targets to compare different imaging devices. CONCLUSIONS: Vast, realistic, and variable CAD pathologies can be generated to incorporate into computational phantoms for VITs. There they can serve as a known truth from which to optimize and evaluate cardiac imaging technologies quantitatively.

Quantitative imaging biomarkers of coronary plaque morphology: insights from EVAPORATE.

Aims: Residual cardiovascular risk persists despite statin therapy. In REDUCE-IT, icosapent ethyl (IPE) reduced total events, but the mechanisms of benefit are not fully understood. EVAPORATE evaluated the effects of IPE on plaque characteristics by coronary computed tomography angiography (CCTA). Given the conclusion that the IPE-treated patients demonstrate that plaque burden decreases has already been published in the primary study analysis, we aimed to demonstrate whether the use of an analytic technique defined and validated in histological terms could extend the primary study in terms of whether such changes could be reliably seen in less time on drug, at the individual (rather than only at the cohort) level, or both, as neither of these were established by the primary study result. Methods and Results: EVAPORATE randomized the patients to IPE 4 g/day or placebo. Plaque morphology, including lipid-rich necrotic core (LRNC), fibrous cap thickness, and intraplaque hemorrhage (IPH), was assessed using the ElucidVivo® (Elucid Bioimaging Inc.) on CCTA. The changes in plaque morphology between the treatment groups were analyzed. A neural network to predict treatment assignment was used to infer patient representation that encodes significant morphological changes. Fifty-five patients completed the 18-month visit in EVAPORATE with interpretable images at each of the three time points. The decrease of LRNC between the patients on IPE vs. placebo at 9 months (reduction of 2 mm3 vs. an increase of 41 mm3, p = 0.008), widening at 18 months (6 mm3 vs. 58 mm3 increase, p = 0.015) were observed. While not statistically significant on a univariable basis, reductions in wall thickness and increases in cap thickness motivated multivariable modeling on an individual patient basis. The per-patient response assessment was possible using a multivariable model of lipid-rich phenotype at the 9-month follow-up, p < 0.01 (sustained at 18 months), generalizing well to a validation cohort. Conclusion: Plaques in the IPE-treated patients acquired more characteristics of stability. Reliable assessment using histologically validated analysis of individual response is possible at 9 months, with sustained stabilization at 18 months, providing a quantitative basis to elucidate drug mechanism and assess individual patient response.

Virtual pathology: Reaching higher standards for noninvasive CTA tissue characterization capability by using histology as a truth standard.

AIMS: Despite advances in therapy, reduction in myocardial infarction or death remains elusive. Whereas computed tomography angiography (CTA) is increasingly appreciated, the analyses are often subjective or qualitative. Methods for specific tissue characterization using histopathologic correlates have recently been reported. We extend this here to demonstrate accurate discrimination between, and quantitation of, lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), and fibrotic tissues. METHODS: NCT02143102 collected 576 tissue samples with paired CTA. Cardiovascular pathologists annotated LRNC, IPH, and dense calcification (CALC) regions as a reference standard. Blinded to histology, CTA was analyzed using ElucidVivo (Elucid Bioimaging Inc., Boston, MA USA). Structure and tissue characteristics of atherosclerotic plaque from CTA, accounting for both the imaging acquisition process and the biology, accounting for differences in density distributions that result from the different cellular and molecular level milieu of the relevant tissue types. RESULTS: LRNC was tested across a true range of 0-10 mm2, with a difference of 0.15 mm2 and a slope of 0.92. IPH was tested across a true range of 0-18 mm2, with a difference from histology of 1.68 mm2 and a slope of 0.95. CALC was tested across a range of 0-14 mm2, with a difference of -0.06 mm2 and a slope of 0.99. Matrix tissue (MATX) was tested across a range of 4-52 mm2, with a difference of 0.02 mm2 and a slope of 0.91. CONCLUSION: LRNC, IPH, CALC, and MATX may be objectively quantified using histopathologic correlates automatically from CTA for use singly or in combination to optimize patient care. The availability of objectively validated quantitative markers that may be followed longitudinally may extend the clinical utility of CTA. Additionally, these measures contribute efficacy variables for developing novel drugs and clinical decision support tools for tailored therapeutics.

In silico model of atherosclerosis with individual patient calibration to enable precision medicine for cardiovascular disease.

OBJECTIVE: Guidance for preventing myocardial infarction and ischemic stroke by tailoring treatment for individual patients with atherosclerosis is an unmet need. Such development may be possible with computational modeling. Given the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Here, we aimed to develop a clinically relevant scale model of atherosclerosis, calibrate it with individual patient data, and use it to simulate optimized pharmacotherapy for individual patients. APPROACH AND RESULTS: The study used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model for simulating individualized responses to pharmacotherapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. After calibrating the systems biology models for individual patients, we simulated intensive lipid-lowering, anti-inflammatory, and anti-diabetic drugs. We also simulated a combination therapy. Drug response was evaluated as the degree of change in plaque stability, where an improvement was defined as a reduction of plaque instability. In patients with initially unstable lesions, simulated responses varied from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement. CONCLUSION: In this pilot study, proteomics-based system biology modeling was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%, providing a potential strategy for improved personalized management of patients with cardiovascular disease.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Atherosclerosis risk classification with computed tomography angiography: A radiologic-pathologic validation study.

BACKGROUND AND AIMS: The application of machine learning to assess plaque risk phenotypes on cardiovascular CT angiography (CTA) is an area of active investigation. Studies using accepted histologic definitions of plaque risk as ground truth for machine learning models are uncommon. The aim was to evaluate the accuracy of a machine-learning software for determining plaque risk phenotype as compared to expert pathologists (histologic ground truth). METHODS: Sections of atherosclerotic plaques paired with CTA were prospectively collected from patients undergoing carotid endarterectomy at two centers. Specimens were annotated for lipid-rich necrotic core, calcification, matrix, and intraplaque hemorrhage at 2 mm spacing and classified as minimal disease, stable plaque, or unstable plaque according to a modified American Heart Association histological definition. Phenotype is determined in two steps: plaque morphology is delineated according to histological tissue definitions, followed by a machine learning classifier. The performance in derivation and validation cohorts for plaque risk categorization and stenosis was compared to histologic ground truth at each matched cross-section. RESULTS: A total of 496 and 408 vessel cross-sections in the derivation and validation cohorts (from 30 and 23 patients, respectively). The software demonstrated excellent agreement in the validation cohort with histological ground truth plaque risk phenotypes with weighted kappa of 0.82 [0.78-0.86] and area under the receiver operating curve for correct identification of plaque type was 0.97 [0.96, 0.98], 0.95 [0.94, 0.97], 0.99 [0.99, 1.0] for unstable plaque, stable plaque, and minimal disease, respectively. Diameter stenosis correlated poorly to histologically defined plaque type; weighted kappa 0.25 in the validation cohort. CONCLUSIONS: A machine-learning software trained on histological ground-truth tissue inputs demonstrated high accuracy for identifying plaque stability phenotypes as compared to expert pathologists.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM. This paper aims to set guidelines on how to build machine learning models using DIMs in radiomics and to apply and report them appropriately. We provide a list of recommendations, named RANDAM (an abbreviation of "Radiomic ANalysis and DAta Modeling"), for analysis, modeling, and reporting in a radiomic study to make machine learning analyses in radiomics more reproducible. RANDAM contains five main components to use in reporting radiomics studies: design, data preparation, data analysis and modeling, reporting, and material availability. Real case studies in lung cancer research are presented along with simulation studies to compare different feature selection methods and several validation strategies.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier. We follow with an end-to-end real-world example of development and validation of a classifier for atherosclerotic plaque phenotypes. We consider diagnostic accuracy and interchangeability to be clinically meaningful claims for a phenotype classification model informed by mp-QIB inputs, aiming to provide tools to demonstrate agreement between imaging-derived characteristics and clinically established phenotypes. Understanding that we are working in an evolving field, we close our manuscript with an acknowledgement of existing challenges and a discussion of where additional work is needed. In particular, we discuss the challenges involved with technical performance and analytical validation of mp-QIBs. We intend for this manuscript to further advance the robust and promising science of multiparametric biomarker development.

A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs).

Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction. In this paper we present three use cases of multiparameter quantitative imaging: i) multidimensional descriptor, ii) phenotype classification, and iii) risk prediction. A fourth application based on data-driven markers from radiomics is also presented. We describe the technical performance characteristics and their metrics common to all use cases, and provide a structure for the development, estimation, and testing of multiparameter quantitative imaging. This paper serves as an overview for a series of individual articles on the four applications, providing the statistical framework for multiparameter imaging applications in medicine.

Patient-specific biomechanical analysis of atherosclerotic plaques enabled by histologically validated tissue characterization from computed tomography angiography: A case study.

BACKGROUND: Rupture of unstable atherosclerotic plaques with a large lipid-rich necrotic core and a thin fibrous cap cause myocardial infarction and stroke. Yet it has not been possible to assess this for individual patients. Clinical guidelines still rely on use of luminal narrowing, a poor indicator but one that persists for lack of effective means to do better. We present a case study demonstrating the assessment of biomechanical indices pertaining to plaque rupture risk non-invasively for individual patients enabled by histologically validated tissue characterization. METHODS: Routinely acquired clinical images of plaques were analyzed to characterize vascular wall tissues using software validated by histology (ElucidVivo, Elucid Bioimaging Inc.). Based on the tissue distribution, wall stress and strain were then calculated at spatial locations with varied fibrous cap thicknesses at diastolic, mean and systolic blood pressures. RESULTS: The von Mises stress of 152 [131, 172] kPa and the equivalent strain of 0.10 [0.08, 0.12] were calculated where the fibrous cap thickness was smallest (560 µm) (95% CI in brackets). The stress at this location was at a level predictive of plaque failure. Stress and strain at locations with larger cap thicknesses were calculated to be lower, demonstrating a clinically relevant range of risk levels. CONCLUSION: Patient specific tissue characterization can identify distributions of stress and strain in a clinically relevant range. This capability may be used to identify high-risk lesions and personalize treatment decisions for individual patients with cardiovascular disease and improve prevention of myocardial infarction and stroke.

Association of S100A8/A9 with Lipid-Rich Necrotic Core and Treatment with Biologic Therapy in Patients with Psoriasis: Results from an Observational Cohort Study.

Psoriasis is a systemic inflammatory disease with an increased risk of atherosclerotic events and premature cardiovascular disease. S100A7, A8/A9, and A12 are protein complexes that are produced by activated neutrophils, monocytes, and keratinocytes in psoriasis. Lipid-rich necrotic core (LRNC) is a high-risk coronary plaque feature previously found to be associated with cardiovascular risk factors and psoriasis severity. LRNC can decrease with biologic therapy, but how this occurs remains unknown. We investigated the relationship between S100 proteins, LRNC, and biologic therapy in psoriasis. S100A8/A9 associated with LRNC in fully adjusted models (ß = 0.27, P = 0.009; n = 125 patients with psoriasis with available coronary computed tomography angiography scans; LRNC analyses; and serum S100A7, S100A8, S100A9, S100A12, and S100A8/A9 levels). At 1 year, in patients receiving biologic therapy (36 of 73 patients had 1-year coronary computed tomography angiography scans available), a 79% reduction in S100A8/A9 levels (‒172 [‒291.7 to 26.4] vs. ‒29.9 [‒137.9 to 50.5]; P = 0.04) and a 0.6 mm2 reduction in average LRNC area (0.04 [‒0.48 to 0.77] vs. ‒0.56 [‒1.8 to 0.13]; P = 0.02) were noted. These results highlight the potential role of S100A8/A9 in the development of high-risk coronary plaque in psoriasis.

Estimating the Precision of Quantitative Imaging Biomarkers without Test-Retest Studies.

RATIONALE AND OBJECTIVES: A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study. MATERIALS AND METHODS: We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method. RESULTS: The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method. CONCLUSION: Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available.

Computed tomography angiographic biomarkers help identify vulnerable carotid artery plaque.

OBJECTIVE: The current risk assessment for patients with carotid atherosclerosis relies primarily on measuring the degree of stenosis. More reliable risk stratification could improve patient selection for targeted treatment. We have developed and validated a model to predict for major adverse neurologic events (MANE; stroke, transient ischemic attack, amaurosis fugax) that incorporates a combination of plaque morphology, patient demographics, and patient clinical information. METHODS: We enrolled 221 patients with asymptomatic carotid stenosis of any severity who had undergone computed tomography angiography at baseline and ≥6 months later. The images were analyzed for carotid plaque morphology (plaque geometry and tissue composition). The data were partitioned into training and validation cohorts. Of the 221 patients, 190 had complete records available and were included in the present analysis. The training cohort was used to develop the best model for predicting MANE, incorporating the patient and plaque features. First, single-variable correlation and unsupervised clustering were performed. Next, several multivariable models were implemented for the response variable of MANE. The best model was selected by optimizing the area under the receiver operating characteristic curve (AUC) and Cohen's kappa statistic. The model was validated using the sequestered data to demonstrate generalizability. RESULTS: A total of 62 patients had experienced a MANE during follow-up. Unsupervised clustering of the patient and plaque features identified single-variable predictors of MANE. Multivariable predictive modeling showed that a combination of the plaque features at baseline (matrix, intraplaque hemorrhage [IPH], wall thickness, plaque burden) with the clinical features (age, body mass index, lipid levels) best predicted for MANE (AUC, 0.79), In contrast, the percent diameter stenosis performed the worst (AUC, 0.55). The strongest single variable for discriminating between patients with and without MANE was IPH, and the most predictive model was produced when IPH was considered with wall remodeling. The selected model also performed well for the validation dataset (AUC, 0.64) and maintained superiority compared with percent diameter stenosis (AUC, 0.49). CONCLUSIONS: A composite of plaque geometry, plaque tissue composition, patient demographics, and clinical information predicted for MANE better than did the traditionally used degree of stenosis alone for those with carotid atherosclerosis. Implementing this predictive model in the clinical setting could help identify patients at high risk of MANE.

Carotid Plaque Phenotyping by Correlating Plaque Morphology from Computed Tomography Angiography with Transcriptional Profiling.

OBJECTIVE: Ischaemic strokes can be caused by unstable carotid atherosclerosis, but methods for identification of high risk lesions are lacking. Carotid plaque morphology imaging using software for visualisation of plaque components in computed tomography angiography (CTA) may improve assessment of plaque phenotype and stroke risk, but it is unknown if such analyses also reflect the biological processes related to lesion stability. Here, we investigated how carotid plaque morphology by image analysis of CTA is associated with biological processes assessed by transcriptomic analyses of corresponding carotid endarterectomies (CEAs). METHODS: Carotid plaque morphology was assessed in patients undergoing CEA for symptomatic or asymptomatic carotid stenosis consecutively enrolled between 2006 and 2015. Computer based analyses of pre-operative CTA was performed to define calcification, lipid rich necrotic core (LRNC), intraplaque haemorrhage (IPH), matrix (MATX), and plaque burden. Plaque morphology was correlated with molecular profiles obtained from microarrays of corresponding CEAs and models were built to assess the ability of plaque morphology to predict symptomatology. RESULTS: Carotid plaques (n = 93) from symptomatic patients (n = 61) had significantly higher plaque burden and LRNC compared with plaques from asymptomatic patients (n = 32). Lesions selected from the transcriptomic cohort (n = 40) with high LRNC, IPH, MATX, or plaque burden were characterised by molecular signatures coupled with inflammation and extracellular matrix degradation, typically linked with instability. In contrast, highly calcified plaques had a molecular signature signifying stability with enrichment of profibrotic pathways and repressed inflammation. In a cross validated prediction model for symptoms, plaque morphology by CTA alone was superior to the degree of stenosis. CONCLUSION: The study demonstrates that CTA image analysis for evaluation of carotid plaque morphology, also reflects prevalent biological processes relevant for assessment of plaque phenotype. The results support the use of CTA image analysis of plaque morphology for risk stratification and management of patients with carotid stenosis.

Proteoglycan 4 Modulates Osteogenic Smooth Muscle Cell Differentiation during Vascular Remodeling and Intimal Calcification.

Calcification is a prominent feature of late-stage atherosclerosis, but the mechanisms driving this process are unclear. Using a biobank of carotid endarterectomies, we recently showed that Proteoglycan 4 (PRG4) is a key molecular signature of calcified plaques, expressed in smooth muscle cell (SMC) rich regions. Here, we aimed to unravel the PRG4 role in vascular remodeling and intimal calcification. PRG4 expression in human carotid endarterectomies correlated with calcification assessed by preoperative computed tomographies. PRG4 localized to SMCs in early intimal thickening, while in advanced lesions it was found in the extracellular matrix, surrounding macro-calcifications. In experimental models, Prg4 was upregulated in SMCs from partially ligated ApoE-/- mice and rat carotid intimal hyperplasia, correlating with osteogenic markers and TGFb1. Furthermore, PRG4 was enriched in cells positive for chondrogenic marker SOX9 and around plaque calcifications in ApoE-/- mice on warfarin. In vitro, PRG4 was induced in SMCs by IFNg, TGFb1 and calcifying medium, while SMC markers were repressed under calcifying conditions. Silencing experiments showed that PRG4 expression was driven by transcription factors SMAD3 and SOX9. Functionally, the addition of recombinant human PRG4 increased ectopic SMC calcification, while arresting cell migration and proliferation. Mechanistically, it suppressed endogenous PRG4, SMAD3 and SOX9, and restored SMC markers' expression. PRG4 modulates SMC function and osteogenic phenotype during intimal remodeling and macro-calcification in response to TGFb1 signaling, SMAD3 and SOX9 activation. The effects of PRG4 on SMC phenotype and calcification suggest its role in atherosclerotic plaque stability, warranting further investigations.

Virtual Transcriptomics: Noninvasive Phenotyping of Atherosclerosis by Decoding Plaque Biology From Computed Tomography Angiography Imaging.

[Figure: see text].

Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve.

BACKGROUND: To evaluate the feasibility of non-invasive fractional flow reserve (FFR) estimation using histologically-validated assessment of plaque morphology on coronary CTA (CCTA) as inputs to a predictive model further validated against invasive FFR. METHODS: Patients (n = 113, 59 ± 8.9 years, 77% male) with suspected coronary artery disease (CAD) who had undergone CCTA and invasive FFR between August 2013 and May 2018 were included. Commercially available software was used to extract quantitative plaque morphology inclusive of both vessel structure and composition. The extracted plaque morphology was then fed as inputs to an optimized artificial neural network to predict lesion-specific ischemia/hemodynamically significant CAD with performance validated by invasive FFR. RESULTS: A total of 122 lesions were considered, 59 (48%) had low FFR values. Plaque morphology-based FFR assessment achieved an area under the curve, sensitivity and specificity of 0.94, 0.90 and 0.81, respectively, versus 0.71, 0.71, and 0.50, respectively, for an optimized threshold applied to degree of stenosis. The optimized ridge regression model for continuous value estimation of FFR achieved a cross-correlation coefficient of 0.56 and regression slope of 0.59 using cross validation, versus 0.18 and 0.10 for an optimized threshold applied to degree of stenosis. CONCLUSIONS: Our results show that non-invasive plaque morphology-based FFR assessment may be used to predict lesion-specific ischemia resulting in hemodynamically significant CAD. This substantially outperforms degree of stenosis interpretation and has a comparable level of sensitivity and specificity relative to publicly reported results from computational fluid dynamics-based approaches.

Treatment of Psoriasis With Biologic Therapy Is Associated With Improvement of Coronary Artery Plaque Lipid-Rich Necrotic Core: Results From a Prospective, Observational Study.

BACKGROUND: Lipid-rich necrotic core (LRNC), a high-risk coronary plaque feature assessed by coronary computed tomography angiography, is associated with increased risk of future cardiovascular events in patients with subclinical, nonobstructive coronary artery disease. Psoriasis is a chronic inflammatory condition that is associated with increased prevalence of high-risk coronary plaque and risk of cardiovascular events. This study characterized LRNC in psoriasis and how LRNC modulates in response to biologic therapy. METHODS: Consecutive biologic naïve psoriasis patients (n=209) underwent coronary computed tomography angiography at baseline and 1-year to assess changes in LRNC using a novel histopathologically validated software (vascuCAP Elucid Bioimaging, Boston, MA) before and after biologic therapy over 1 year. RESULTS: Study participants were middle-aged, predominantly male with similar cardiometabolic and psoriasis status between treatment groups. In all participants at baseline, LRNC was associated with Framingham risk score (ß [standardized ß]=0.12 [95% CI, 0.00-0.15]; P=0.045), and psoriasis severity (ß=0.13 [95% CI, 0.01-0.26]; P=0.029). At 1-year, participants receiving biologic therapy had a reduction in LRNC (mm2; 3.12 [1.99-4.66] versus 2.97 [1.84-4.35]; P=0.028), while those who did not receive biologic therapy over 1 year demonstrated no significant change with nominally higher LRNC (3.12 [1.82-4.60] versus 3.34 [2.04-4.74]; P=0.06). The change in LRNC was significant compared with that of the nonbiologic treated group (ΔLRNC, -0.22 mm2 versus 0.14 mm2, P=0.004) and remained significant after adjusting for cardiovascular risk factors and psoriasis severity (ß=-0.09 [95% CI, -0.01 to -0.18]; P=0.033). CONCLUSIONS: LRNC was associated with psoriasis severity and cardiovascular risk factors in psoriasis. Additionally, there was favorable modification of LRNC in those on biologic therapy. This study provides evidence of potential reduction in LRNC with treatment of systemic inflammation. Larger, longer follow-up prospective studies should be conducted to understand how changes in LRNC may translate into a reduction in future cardiovascular events in psoriasis.