Aim: The study investigated the association of carotid ultrasound echolucent plaque-based biomarker with HbA1c, measured as age-adjusted grayscale median (AAGSM) as a function of chronological age, total plaque area, and conventional grayscale median (GSMconv). Methods: Two stages were developed: (a) automated measurement of carotid parameters such as total plaque area (TPA); (b) computing the AAGSM as a function of GSMconv, age, and TPA. Intra-operator (novice and experienced) analysis was conducted. Results: IRB approved, 204 patients’ left/right CCA (408 images) ultrasound scans were collected: mean age: 69 ± 11 years; mean HbA1c: 6.12 ± 1.47%. A moderate inverse correlation was observed between AAGSM and HbA1c (CC of −0.13, P = 0.01), compared to GSM (CC of −0.06, P = 0.24). The RCCA and LCCA showed CC of −0.18, P < 0.01 and −0.08; P < 0.24. Female and males showed CC of −0.29, P < 0.01 and −0.10, P = 0.09. Using the threshold for AAGSM and HbA1c as: low-risk (AAGSM > 100; HbA1c < 5.7%), moderate-risk (40 < AAGSM < 100; 5.7% < HbA1c < 6.5%) and high-risk (AAGSM < 40; HbA1c > 6.5%), the area under the curve showed a better performance of AAGSM over GSMconv. A paired t-test between operators and expert (P < 0.0001); inter-operator CC of 0.85 (P < 0.0001). Conclusions: Echolucent plaque in patients with diabetes can be more accurately characterized for risk stratification using AAGSM compared to GSMconv.

Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients

Saba, Luca;
2018-01-01

Abstract

Aim: The study investigated the association of carotid ultrasound echolucent plaque-based biomarker with HbA1c, measured as age-adjusted grayscale median (AAGSM) as a function of chronological age, total plaque area, and conventional grayscale median (GSMconv). Methods: Two stages were developed: (a) automated measurement of carotid parameters such as total plaque area (TPA); (b) computing the AAGSM as a function of GSMconv, age, and TPA. Intra-operator (novice and experienced) analysis was conducted. Results: IRB approved, 204 patients’ left/right CCA (408 images) ultrasound scans were collected: mean age: 69 ± 11 years; mean HbA1c: 6.12 ± 1.47%. A moderate inverse correlation was observed between AAGSM and HbA1c (CC of −0.13, P = 0.01), compared to GSM (CC of −0.06, P = 0.24). The RCCA and LCCA showed CC of −0.18, P < 0.01 and −0.08; P < 0.24. Female and males showed CC of −0.29, P < 0.01 and −0.10, P = 0.09. Using the threshold for AAGSM and HbA1c as: low-risk (AAGSM > 100; HbA1c < 5.7%), moderate-risk (40 < AAGSM < 100; 5.7% < HbA1c < 6.5%) and high-risk (AAGSM < 40; HbA1c > 6.5%), the area under the curve showed a better performance of AAGSM over GSMconv. A paired t-test between operators and expert (P < 0.0001); inter-operator CC of 0.85 (P < 0.0001). Conclusions: Echolucent plaque in patients with diabetes can be more accurately characterized for risk stratification using AAGSM compared to GSMconv.
2018
Age-adjusted grayscale median; Carotid atherosclerosis; Diabetes; Hemoglobin; Plaque echolucency; Ultrasound
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/249970
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