JACC Adv. 2026 Jun;5(6 Pt 2):102829. doi: 10.1016/j.jacadv.2026.102829.
ABSTRACT
BACKGROUND: The associations between coronary artery tortuosity and age, sex, and cardiovascular risk factors are not fully established. Prior studies enrolled fewer than 1,000 subjects and relied on heuristic metrics.
OBJECTIVES: The purpose of this study was to develop and validate an automated right coronary artery (RCA) tortuosity measure using machine learning (ML)-based vessel segmentation and examine its association with sex, age, coronary artery disease (CAD), and cardiovascular risk factors.
METHODS: We developed an ML-enabled pipeline to quantify RCA tortuosity as a continuous measure in 38,691 RCA angiograms from 22,334 patients and evaluated concordance with blinded interventional cardiologist review in a subset of 300 angiograms. Regression models were used to study the association of tortuosity with age, sex, hypertension, diabetes, hypercholesterolemia, smoking, and presence of CAD.
RESULTS: Tortuosity ranged from 0.007 to 0.289. Blinded interventional cardiologist classification of high tortuosity vs not had 85.6% precision. Tortuosity was higher in women (β per SD = 0.17; P < 0.001) and higher with hypertension (β per SD = 0.06; P = 0.002), but lower with diabetes (β per SD = -0.11, P < 0.001. After adjustment for risk factors, age was not independently associated with tortuosity. Hypercholesterolemia and smoking were not associated. Higher tortuosity was associated with CAD (OR per SD = 1.05; P < 0.001), severe CAD (OR per SD = 1.09; P < 0.001), and higher Gensini score (β per SD = 0.05; P < 0.001), even after adjustment.
CONCLUSIONS: We derived a scalable ML-enabled measure of RCA tortuosity from coronary angiography and found associations with sex, hypertension, diabetes, and presence and severity of CAD.
PMID:42312790 | DOI:10.1016/j.jacadv.2026.102829