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International Angiology 2021 April;40(2):150-64

DOI: 10.23736/S0392-9590.20.04538-1


language: English

Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review

Ankush D. JAMTHIKAR 1, Anudeep PUVVULA 2, Deep GUPTA 1, Amer M. JOHRI 3, Vijay NAMBI 4, Narendra N. KHANNA 5, Luca SABA 6, Sophie MAVROGENI 7, John R. LAIRD 8, Gyan PAREEK 9, Martin MINER 10, Petros P. SFIKAKIS 11, Athanasios PROTOGEROU 12, George D. KITAS 13, Andrew NICOLAIDES 14, Aditya M. SHARMA 15, Vijay VISWANATHAN 16, Vijay S. RATHORE 17, Raghu KOLLURI 18, Deepak L. BHATT 19, Jasjit S. SURI 20

1 Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, India; 2 Annu’s Hospitals for Skin and Diabetes, Nellore, India; 3 Division of Cardiology, Department of Medicine, Queen’s University, Kingston, ON, Canada; 4 Michael E. De Bakey Veterans Affairs Hospital and Baylor College of Medicine, Houston, TX, USA; 5 Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India; 6 Department of Radiology, University Hospital of Cagliari, Cagliari, Italy; 7 Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece; 8 Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA; 9 Minimally Invasive Urology Institute, Brown University, Providence, RI, USA; 10 Men’s Health Center, Miriam Hospital Providence, Providence, RI, USA; 11 Unit of Rheumatology, National Kapodistrian University of Athens, Greece; 12 Department of Cardiovascular Prevention, National Kapodistrian University of Athens, Athens, Greece; 13 Research and Development Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; 14 Vascular Screening and Diagnostic Center, University of Nicosia, Nicosia, Cyprus; 15 Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA; 16 Professor M. Viswanathan Diabetes Research Center, MV Hospital for Diabetes, Chennai, India; 17 Department of Nephrology, Kaiser Permanente, Sacramento, CA, USA; 18 Ohio Health Heart and Vascular and Syntropic Corelab, Columbus, OH, USA; 19 Brigham and Women’s Hospital Heart and Vascular Center, Harvard Medical School, Boston, MA, USA; 20 Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA, USA

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.

KEY WORDS: Renal insufficiency, chronic; Cardiovascular diseases; Risk assessment; Glomerular filtration rate; Artificial intelligence; Machine learning

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