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

Vasileios Kotsis; Ankush D Jamthikar; Tadashi Araki; Deep Gupta; John R Laird; Argiris A Giannopoulos; Luca Saba; Harman S Suri; Sophie Mavrogeni; George D Kitas; Klaudija Viskovic; Narendra N Khanna; Ajay Gupta; Andrew Nicolaides; Jasjit S Suri

doi:10.1016/j.diabres.2018.07.028

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

Diabetes Res Clin Pract. 2018 Sep:143:322-331. doi: 10.1016/j.diabres.2018.07.028. Epub 2018 Jul 27.

Authors

Affiliations

¹ Hypertension Center, Papageorgiou Hospital, Aristotle University of Thessaloniki, Greece.
² Department of Electronics and Communication Engineering, VNIT, Nagpur, Maharashtra, India.
³ Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan.
⁴ Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA.
⁵ Department of Medicine, Imperial College London, UK.
⁶ Department of Radiology, University of Cagliari, Italy.
⁷ Brown University, Providence, RI, USA.
⁸ Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece.
⁹ Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester, UK; Department of Rheumatology, Dudley Group NHS Foundation Trust, Dudley, UK.
¹⁰ Department of Radiology and Ultrasound University Hospital for Infectious Diseases, Croatia.
¹¹ Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India.
¹² Department of Radiology and Feil Family Brain and Mind Research Institute, Weill Cornell Medical Center, NY, USA.
¹³ Department of Vascular Surgery, Imperial College, London, UK; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus.
¹⁴ Stroke Monitoring and Diagnostic Division, AtheroPoint(TM), Roseville, CA, USA. Electronic address: jasjit.suri@atheropoint.com.

PMID: 30059757
DOI: 10.1016/j.diabres.2018.07.028

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 (GSM_conv).

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 GSM_conv, 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 GSM_conv. 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 GSM_conv.

Keywords: Age-adjusted grayscale median; Carotid atherosclerosis; Diabetes; Hemoglobin; Plaque echolucency; Ultrasound.

MeSH terms

Aged
Carotid Artery Diseases / diagnostic imaging*
Diabetes Mellitus / diagnostic imaging*
Female
Humans
Male
Phenotype
Risk Factors
Ultrasonography / methods*