4 research outputs found
Heterogeneous plaque-lumen geometry is associated with major adverse cardiovascular events.
AIMS
Prospective studies show that only a minority of plaques with higher risk features develop future major adverse cardiovascular events (MACE), indicating the need for more predictive markers. Biomechanical estimates such as plaque structural stress (PSS) improve risk prediction but require expert analysis. In contrast, complex and asymmetric coronary geometry is associated with both unstable presentation and high PSS, and can be estimated quickly from imaging. We examined whether plaque-lumen geometric heterogeneity evaluated from intravascular ultrasound affects MACE and incorporating geometric parameters enhances plaque risk stratification.
METHODS AND RESULTS
We examined plaque-lumen curvature, irregularity, lumen aspect ratio (LAR), roughness, PSS, and their heterogeneity indices (HIs) in 44 non-culprit lesions (NCLs) associated with MACE and 84 propensity-matched no-MACE-NCLs from the PROSPECT study. Plaque geometry HI were increased in MACE-NCLs vs. no-MACE-NCLs across whole plaque and peri-minimal luminal area (MLA) segments (HI curvature: adjusted P = 0.024; HI irregularity: adjusted P = 0.002; HI LAR: adjusted P = 0.002; HI roughness: adjusted P = 0.004). Peri-MLA HI roughness was an independent predictor of MACE (hazard ratio: 3.21, P < 0.001). Inclusion of HI roughness significantly improved the identification of MACE-NCLs in thin-cap fibroatheromas (TCFA, P < 0.001), or with MLA ≤ 4 mm2 (P < 0.001), or plaque burden (PB) ≥ 70% (P < 0.001), and further improved the ability of PSS to identify MACE-NCLs in TCFA (P = 0.008), or with MLA ≤ 4 mm2 (P = 0.047), and PB ≥ 70% (P = 0.003) lesions.
CONCLUSION
Plaque-lumen geometric heterogeneity is increased in MACE vs. no-MACE-NCLs, and inclusion of geometric heterogeneity improves the ability of imaging to predict MACE. Assessment of geometric parameters may provide a simple method of plaque risk stratification
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Heterogeneous plaque-lumen geometry is associated with major adverse cardiovascular events.
AIMS: Prospective studies show that only a minority of plaques with higher risk features develop future major adverse cardiovascular events (MACE), indicating the need for more predictive markers. Biomechanical estimates such as plaque structural stress (PSS) improve risk prediction but require expert analysis. In contrast, complex and asymmetric coronary geometry is associated with both unstable presentation and high PSS, and can be estimated quickly from imaging. We examined whether plaque-lumen geometric heterogeneity evaluated from intravascular ultrasound affects MACE and incorporating geometric parameters enhances plaque risk stratification. METHODS AND RESULTS: We examined plaque-lumen curvature, irregularity, lumen aspect ratio (LAR), roughness, PSS, and their heterogeneity indices (HIs) in 44 non-culprit lesions (NCLs) associated with MACE and 84 propensity-matched no-MACE-NCLs from the PROSPECT study. Plaque geometry HI were increased in MACE-NCLs vs. no-MACE-NCLs across whole plaque and peri-minimal luminal area (MLA) segments (HI curvature: adjusted P = 0.024; HI irregularity: adjusted P = 0.002; HI LAR: adjusted P = 0.002; HI roughness: adjusted P = 0.004). Peri-MLA HI roughness was an independent predictor of MACE (hazard ratio: 3.21, P < 0.001). Inclusion of HI roughness significantly improved the identification of MACE-NCLs in thin-cap fibroatheromas (TCFA, P < 0.001), or with MLA ≤ 4 mm2 (P < 0.001), or plaque burden (PB) ≥ 70% (P < 0.001), and further improved the ability of PSS to identify MACE-NCLs in TCFA (P = 0.008), or with MLA ≤ 4 mm2 (P = 0.047), and PB ≥ 70% (P = 0.003) lesions. CONCLUSION: Plaque-lumen geometric heterogeneity is increased in MACE vs. no-MACE-NCLs, and inclusion of geometric heterogeneity improves the ability of imaging to predict MACE. Assessment of geometric parameters may provide a simple method of plaque risk stratification
Recommended from our members
Heterogeneous plaque-lumen geometry is associated with major adverse cardiovascular events.
AIMS: Prospective studies show that only a minority of plaques with higher risk features develop future major adverse cardiovascular events (MACE), indicating the need for more predictive markers. Biomechanical estimates such as plaque structural stress (PSS) improve risk prediction but require expert analysis. In contrast, complex and asymmetric coronary geometry is associated with both unstable presentation and high PSS, and can be estimated quickly from imaging. We examined whether plaque-lumen geometric heterogeneity evaluated from intravascular ultrasound affects MACE and incorporating geometric parameters enhances plaque risk stratification. METHODS AND RESULTS: We examined plaque-lumen curvature, irregularity, lumen aspect ratio (LAR), roughness, PSS, and their heterogeneity indices (HIs) in 44 non-culprit lesions (NCLs) associated with MACE and 84 propensity-matched no-MACE-NCLs from the PROSPECT study. Plaque geometry HI were increased in MACE-NCLs vs. no-MACE-NCLs across whole plaque and peri-minimal luminal area (MLA) segments (HI curvature: adjusted P = 0.024; HI irregularity: adjusted P = 0.002; HI LAR: adjusted P = 0.002; HI roughness: adjusted P = 0.004). Peri-MLA HI roughness was an independent predictor of MACE (hazard ratio: 3.21, P < 0.001). Inclusion of HI roughness significantly improved the identification of MACE-NCLs in thin-cap fibroatheromas (TCFA, P < 0.001), or with MLA ≤ 4 mm2 (P < 0.001), or plaque burden (PB) ≥ 70% (P < 0.001), and further improved the ability of PSS to identify MACE-NCLs in TCFA (P = 0.008), or with MLA ≤ 4 mm2 (P = 0.047), and PB ≥ 70% (P = 0.003) lesions. CONCLUSION: Plaque-lumen geometric heterogeneity is increased in MACE vs. no-MACE-NCLs, and inclusion of geometric heterogeneity improves the ability of imaging to predict MACE. Assessment of geometric parameters may provide a simple method of plaque risk stratification
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Correcting common OCT artifacts enhances plaque classification and identification of higher-risk plaque features.
BACKGROUND: Optical coherence tomography (OCT) is used widely to guide stent placement, identify higher-risk plaques, and assess mechanisms of drug efficacy. However, a range of common artifacts can prevent accurate plaque classification and measurements, and limit usable frames in research studies. We determined whether pre-processing OCT images corrects artifacts and improves plaque classification. METHODS: We examined both ex-vivo and clinical trial OCT pullbacks for artifacts that prevented accurate tissue identification and/or plaque measurements. We developed Fourier transform-based software that reconstructed images free of common OCT artifacts, and compared corrected and uncorrected images. RESULTS: 48Â % of OCT frames contained image artifacts, with 62Â % of artifacts over or within lesions, preventing accurate measurement in 12Â % frames. Pre-processing corrected >70Â % of all artifacts, including thrombus, macrophage shadows, inadequate flushing, and gas bubbles. True tissue reconstruction was achieved in 63Â % frames that would otherwise prevent accurate clinical measurements. Artifact correction was non-destructive and retained anatomical lumen and plaque parameters. Correction improved accuracy of plaque classification compared against histology and retained accurate assessment of higher-risk features. Correction also changed plaque classification and prevented artifact-related measurement errors in a clinical study, and reduced unmeasurable frames to <5Â % ex-vivo and ~1Â % in-vivo. CONCLUSIONS: Fourier transform-based pre-processing corrects a wide range of common OCT artifacts, improving identification of higher-risk features and plaque classification, and allowing more of the whole dataset to be used for clinical decision-making and in research. Pre-processing can augment OCT image analysis systems both for stent optimization and in natural history or drug studies