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The objectives of this study are to (1) examine the "10-year cardiovascular risk" in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against "conventional cardiovascular risk calculators." These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian–Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% (P <.0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P =.0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P =.06). [ABSTRACT FROM AUTHOR]

We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima–media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females −0.25 (P <.001) and −0.35 (P <.001), respectively; (2) for T2DM and non-T2DM −0.26 (P <.001) and −0.49 (P =.02), respectively, and (3) for left and right CCA −0.25 (P <.001) and −0.23 (P <.001), respectively. Automated TPA is an equally reliable biomarker compared with cIMTave for patients with CKD (with or without T2DM) with subclinical atherosclerosis. [ABSTRACT FROM AUTHOR]

Motivation: This study presents a novel nonlinear model which can predict 10‐year carotid ultrasound image‐based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low‐density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima‐media thickness (IMT), wall variability, and total plaque area). Methodology: Two‐step process was adapted: First, five baseline carotid image‐based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™, CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10‐year predictions of carotid phenotypes. Results: Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10‐year carotid phenotypes. Mean correlation coefficient (CC) between 10‐year carotid image‐based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10‐year measurements of five phenotypes IMTave10yr, IMTmax10yr, IMTmin10yr, IMTV10yr, and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter‐operator variability between two operators showed significant CC (P < 0.0001). Conclusions: A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image‐based phenotypes for predicting the 10‐year carotid ultrasound image‐based phenotypes which may be used risk assessment. [ABSTRACT FROM AUTHOR]