It has been hypothesized that mechanical risk factors may be used to predict future atherosclerotic plaque rupture. Truly predictive methods for plaque rupture and methods to identify the best predictor(s) from all the candidates are lacking in the literature. A novel combination of computational and statistical models based on serial magnetic resonance imaging (MRI) was introduced to quantify sensitivity and specificity of mechanical predictors to identify the best candidate for plaque rupture site prediction. Serial in vivo MRI data of carotid plaque from one patient was acquired with follow-up scan showing ulceration. 3D computational fluid-structure interaction (FSI) models using both baseline and follow-up data were constructed and plaque wall stress (PWS) and strain (PWSn) and flow maximum shear stress (FSS) were extracted from all 600 matched nodal points (100 points per matched slice, baseline matching follow-up) on the lumen surface for analysis. Each of the 600 points was marked “ulcer” or “nonulcer” using follow-up scan. Predictive statistical models for each of the seven combinations of PWS, PWSn, and FSS were trained using the follow-up data and applied to the baseline data to assess their sensitivity and specificity using the 600 data points for ulcer predictions. Sensitivity of prediction is defined as the proportion of the true positive outcomes that are predicted to be positive. Specificity of prediction is defined as the proportion of the true negative outcomes that are correctly predicted to be negative. Using probability 0.3 as a threshold to infer ulcer occurrence at the prediction stage, the combination of PWS and PWSn provided the best predictive accuracy with (sensitivity, specificity) = (0.97, 0.958). Sensitivity and specificity given by PWS, PWSn, and FSS individually were (0.788, 0.968), (0.515, 0.968), and (0.758, 0.928), respectively. The proposed computational-statistical process provides a novel method and a framework to assess the sensitivity and specificity of various risk indicators and offers the potential to identify the optimized predictor for plaque rupture using serial MRI with follow-up scan showing ulceration as the gold standard for method validation. While serial MRI data with actual rupture are hard to acquire, this single-case study suggests that combination of multiple predictors may provide potential improvement to existing plaque assessment schemes. With large-scale patient studies, this predictive modeling process may provide more solid ground for rupture predictor selection strategies and methods for image-based plaque vulnerability assessment.
Cardiovascular diseases are closely linked to atherosclerotic plaque development and rupture. Assessment of plaque vulnerability is of fundamental significance to cardiovascular research and disease diagnosis, prevention, tre... | Find, read and cite all the research you need on Tech Science Press
Plaque progression and vulnerability are influenced by many risk factors. Our goal is to find simple methods to combine multiple risk factors for better plaque development predictions. A sample size of 374 intravascular ultrasound (IVUS) slices with matched follow-up was obtained from 9 patients (Mean age 59, 7 m) with informed consent obtained. 3D fluid-structure interaction models were constructed to obtain plaque stress/strain conditions. Four morphological and biomechanical factors (plaque burden (PB), cap thickness (CT), lipid percent (LP) and average plaque wall stress (PWS)) were chosen to predict plaque burden increase defined as PBI = (PB at follow-up) - (PB at baseline). For a given slice Si, the ground truth Y PBI is define as Y PBI (Si)=1 if PBI(Si)>0; Y PBI =0 if PBI(Si)≤0. For a single predictor W, a threshold value Wc was used to assign the binary prediction outcome: Y W (Si)=1 if W>Wc; Y W (Si)=0 if W≤Wc. Wc was chosen to get optimal agreement between Y W and Y PBI . To use multiple predictors (say, W1, W2, W3) to PBI, a new predictor Combo(W1,W2,W3) was created with its values defined as Combo(W1,W2,W3)=Y W1 +Y W2 +Y W3 , where Y W1 , Y W2 and Y W3 were evaluated the same way as before. Combo was then treated as a single predictor and a threshold value was determined to achieve best agreement with Y PBI . Table 1 summarizes the optimal thresholds and agreement rates for all 15 strategies. Agreement rate using PB alone was 57.5%. PWS was the best single predictor for PBI with agreement rate 62.6%. Combining CT and PWS achieved 66.5% agreement rate, 9% better over PB, which was also obtained by combining 4 risk factors. The method presented here could be used to combine predictors from different sources (stenosis, cap, lipid, inflammation, macrophage, hemorrhage, stress, strain, flow shear stress, FFR, smoking, diabetes, cholesterol, alcohol, hypertension, pro-rupture genes, etc.) to improve prediction accuracy and help decision-making in clinical practice.
It is hypothesized that artery stiffness may be associated with plaque progression. However, in vivo vessel material stiffness follow-up data is lacking in the literature. In vivo 3D multi-contrast and Cine magnetic resonance imaging (MRI) carotid plaque data were acquired from 8 patients with follow-up (18 months) with written informed consent obtained. Cine MRI and 3D thin-layer models were used to determine parameter values of the Mooney-Rivlin models for the 81slices from 16 plaques (2 scans/patient) using our established iterative procedures. Effective Young’s Modulus (YM) values for stretch ratio [1.0,1.3] were calculated for each slice for analysis. Stress-stretch ratio curves from Mooney-Rivlin models for the 16 plaques and 81 slices are given in Fig. 1. Average YM value of the 81 slices was 411kPa. Slice YM values varied from 70 kPa (softest) to 1284 kPa (stiffest), a 1734% difference. Average slice YM values by vessel varied from 109 kPa (softest) to 922 kPa (stiffest), a 746% difference. Location-wise, the maximum slice YM variation rate within a vessel was 306% (139 kPa vs. 564 kPa). Average slice YM variation rate within a vessel for the 16 vessels was 134%. Average variation of YM values from baseline (T1) to follow up (T2) for all patients was 61.0%. The range of the variation of YM values was [-28.4%, 215%]. For progression study, YM increase (YMI=YM T2 -TM T1 ) showed negative correlation with plaque progression measured by wall thickness increase (WTI), (r= -0.6802, p=0.0634). YM T2 showed strong negative correlation with WTI (r= -0.7764, p=0.0235). Correlation between YM T1 and WTI was not significant (r= -0.4353, p= 0.2811). Conclusion In vivo carotid vessel material properties have large variations from patient to patient, along the vessel segment within a patient, and from baseline to follow up. Use of patient-specific, location specific and time-specific material properties could potentially improve the accuracy of model stress/strain calculations.
The SNP-set analysis is a powerful tool for dissecting the genetics of complex human diseases. There are three fundamental genetic association approaches to SNR-set analysis: the marginal model fitting approach, the joint model fitting approach, and the decorrelation approach. A problem of primary interest is how these approaches compare with each other. To address this problem, we develop a theoretical platform to compare the signal-to-noise ratio (SNR) of these approaches under the generalized linear model. We elaborate how causal genetic effects give rise to statistically detectable association signals and show that, when causal effects spread over blocks of strong linkage disequilibrium (LD), the SNR of the marginal model fitting is usually higher than that of the decorrelation approach which, in turn, is higher than that of the unbiased joint model fitting approach. We also scrutinize dense effects and LDs by a bivariate model and extensive simulations using the 1000 Genome Project data. Last, we compare the statistical power of two generic types of SNP-set tests (summation-based and supremum-based) by simulations and an osteoporosis study using large data from UK Biobank. Our results help develop powerful tools for SNP-set analysis and understand the signal detection problem in the presence of colored noise.
The $p$-value combination approach is an important statistical strategy for testing global hypotheses with broad applications in signal detection, meta-analysis, data integration, etc. In this paper we extend the classic Fisher's combination method to a unified family of statistics, called TFisher, which allows a general truncation-and-weighting scheme of input $p$-values. TFisher can significantly improve statistical power over the Fisher and related truncation-only methods for detecting both rare and dense "signals." To address wide applications, analytical calculations for TFisher's size and power are deduced under any two continuous distributions in the null and the alternative hypotheses. The corresponding omnibus test (oTFisher) and its size calculation are also provided for data-adaptive analysis. We study the asymptotic optimal parameters of truncation and weighting based on Bahadur efficiency (BE). A new asymptotic measure, called the asymptotic power efficiency (APE), is also proposed for better reflecting the statistics' performance in real data analysis. Interestingly, under the Gaussian mixture model in the signal detection problem, both BE and APE indicate that the soft-thresholding scheme is the best, the truncation and weighting parameters should be equal. By simulations of various signal patterns, we systematically compare the power of statistics within TFisher family as well as some rare-signal-optimal tests. We illustrate the use of TFisher in an exome-sequencing analysis for detecting novel genes of amyotrophic lateral sclerosis. Relevant computation has been implemented into an R package TFisher published on the Comprehensive R Archive Network to cater for applications.
ABSTRACT The analysis of whole‐genome sequence (WGS) data using longitudinal phenotypes offers a potentially rich resource for the examination of the genetic variants and their covariates that affect complex phenotypes over time. We summarize eight contributions to the Genetic Analysis Workshop 18, which applied a diverse array of statistical genetic methods to analyze WGS data in combination with data from genome‐wide association studies (GWAS) from up to four different time points on blood pressure phenotypes. The common goal of these analyses was to develop and apply appropriate methods that utilize longitudinal repeated measures to potentially increase the analytic efficiency of WGS and GWAS data. These diverse methods can be grouped into two categories, based on the way they model dependence structures: (1) linear mixed‐effects (LME) models, where the random effect terms in the linear models are used to capture the dependence structures; and (2) variance‐components models, where the dependence structures are constructed directly based on multiple components of variance‐covariance matrices for the multivariate Gaussian responses. Despite the heterogeneous nature of these analytical methods, the group came to the following conclusions: (1) the use of repeat measurements can gain power to identify variants associated with the phenotype; (2) the inclusion of family data may correct genotyping errors and allow for more accurate detection of rare variants than using unrelated individuals only; and (3) fitting mixed‐effects and variance‐components models for longitudinal data presents computational challenges. The challenges and computational burden demanded by WGS data were addressed in the eight contributions.
Abstract Although there is strong evidence that certain activities can increase bone density and structure in some individuals, it is unclear what specific mechanical factors govern the response. This is important because understanding the effect of mechanical signals on bone could contribute to more effective osteoporosis prevention methods and efficient clinical trial design. The degree to which strain rate and magnitude govern bone adaptation in humans has never been prospectively tested. Here, we studied the effects of a voluntary upper extremity compressive loading task in healthy adult women during a twelve month prospective period. One hundred and two women age 21-40 participated in one of two experiments. (1): low (n=21) and high (n=24) strain magnitude. (2): low (n=21) and high (n=20) strain rate. Control: (n=16): no intervention. Strains were assigned using subject-specific finite element models. Load cycles were recorded digitally. The primary outcome was change in ultradistal integral bone mineral content (iBMC), assessed with QCT. Interim timepoints and secondary outcomes were assessed with high resolution pQCT (HRpQCT). Sixty-six subjects completed the intervention, and interim data were analyzed for 77 subjects. Both the low and high strain rate groups had significant 12-month increases to ultradistal iBMC (change in control: -1.3±2.7%, low strain rate: 2.7±2.1%, high strain rate: 3.4±2.2%), total iBMC, and other measures. “Loading dose” was positively related to 12-month change in ultradistal iBMC, and interim changes to total BMD, cortical thickness and inner trabecular BMD. Subjects who gained the most bone completed, on average, 130 loading bouts of (mean strain) 550 με at 1805 με/s. Those with the greatest gains had the highest loading dose. We conclude that signals related to strain magnitude, rate, and number of loading bouts contribute to bone adaptation in healthy adult women, but only explain a small amount of variance in bone changes.
Increasing evidence suggests that mechanisms governing advanced plaque progression may be different from those for early progression and require further investigation. Serial MRI data and 3D fluid–structure interaction (FSI) models were employed to identify possible correlations between mechanical stresses and advanced plaque progression measured by vessel wall thickness increase (WTI). Long-term patient follow up was used to gather data and investigate if the correlations identified above were reproducible. In vivo MRI data were acquired from 16 patients in a follow-up study with 2 to 4 scans for each patient (scan interval: average 18 months and standard deviation 6.8 months). A total of 38 scan pairs (baseline and follow-up) were formed for analysis using the carotid bifurcation as the registration point. 3D FSI models were constructed to obtain plaque wall stress (PWS) and flow shear stress (FSS) to quantify their correlations with plaque progression. The Linear Mixed-Effects models were used to study possible correlations between WTI and baseline PWS and FSS with nodal dependence taken into consideration. Of the 38 scan pairs, 22 pairs showed positive correlation between baseline PWS and WTI, 1 pair showed negative correlation, and 15 pairs showed no correlation. Thirteen patients changed their correlation sign (81.25%). Between baseline FSS and WTI, 16 pairs showed negative correlation, 1 pair showed positive correlation. Twelve patients changed correlation sign (75%). Our results showed that advanced plaque progression had an overall positive correlation with plaque wall stress and a negative correlation with flow shear stress at baseline. However, long-term follow up showed that correlations between plaque progress and mechanical stresses (FSS and PWS) identified for one time period were not re-producible for most cases (>80%). Further investigations are needed to identify the reasons causing the correlation sign changes.
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