Background: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics.
Objectives: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics.
Methods: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality.
Results: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively).
Conclusions: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.
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http://dx.doi.org/10.1016/j.jcmg.2023.05.020 | DOI Listing |
Alzheimers Dement
December 2024
University of Virginia, Charlottesville, VA, USA
Background: Seizures maybe associated with worse neuropathology findings in people with dementia. However, the role of seizure control and how it may impact post‐mortem histopathology findings in people with dementia remains unexplored.
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Alzheimers Dement
December 2024
Department of Neurology, Mayo Clinic, Rochester, MN, USA
Background: There is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Southern California, Los Angeles, CA, USA
Background: TDP‐43 (TAR DNA‐binding protein 43) is one of the most frequently observed co‐pathologies in Alzheimer's disease (AD). Recognizing the diversity of pathological features in individuals with AD, including the presence of TDP‐43, may lead to more personalized and effective treatment approaches. We investigate ante‐mortem cortical microstructural changes in MRI with subsequent autopsy confirmation of Alzheimer’s disease neuropathological changes (ADNC) with and without TDP‐43 comorbidity.
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December 2024
ALZpath, Inc., Carlsbad, CA, USA
Background: Tau phosphorylated at position 217 (pTau217) is considered to have the highest accuracy in identifying Alzheimer’s disease (AD) pathology using blood. We describe a multi‐cohort evaluation of the Simoa ALZpath pTau217 assay for the prediction of amyloid status in combination with additional blood‐based AD biomarkers (GFAP, pTau181, etc.), as well as comparisons between histopathological and PET based amyloid measurements.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
Background: While magnetic resonance imaging (MRI) markers of neurodegeneration are nonspecific to Alzheimer’s disease (AD) pathology, they have been correlated with cognitive dysfunction, and therefore, provide important information pertaining to disease staging. Neurodegeneration in AD is commonly assessed with macrostructural measures of brain atrophy, such as hippocampal volume. However, recent investigations have shown that markers of neural microstructure derived from diffusion MRI (DWI) may provide supplementary insight into the progression of AD pathophysiology.
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