Artery-specific prediction of percutaneous coronary intervention from coronary calcium volume on non- gated chest computed tomography
Year: 2026
Type: preprint
Abstract: Background The clinical significance of incidental coronary calcification on non-gated chest computed tomography (CT) in asymptomatic individuals is unclear. We developed artery-specific prediction models for percutaneous coronary intervention (PCI) using three-dimensional calcium volume, with invasive angiography as the reference standard. Methods Calcium volume was quantified for the left anterior descending artery, left circumflex artery, and right coronary artery using 3D Slicer. Artery-specific multivariable logistic regression models were evaluated by area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis, with internal validation using 1000 bootstrap resamples. Results Of 354 patients, 129 (36.4%) underwent PCI. Calcium volume independently predicted PCI in the left anterior descending and left circumflex arteries but not the right coronary artery; between-artery differences were borderline and primarily driven by the right coronary artery. Ejection fraction independently predicted PCI in all models (odds ratio 0.847–0.871, all P < 0.01). AUCs were 0.828 (left anterior descending), 0.798 (left circumflex), and 0.792 (right coronary) arteries. Calibration was adequate for left circumflex and right coronary models; the left anterior descending model showed residual miscalibration on the Hosmer-Lemeshow test ( P = 0.008) but acceptable agreement on bootstrap-corrected calibration. Decision curve analysis showed net benefit across a wide range of thresholds. Conclusions These artery-specific models may support quantitative triage of incidental coronary calcification, potentially avoiding unnecessary downstream evaluation in low-risk patients while ensuring timely revascularization in high-risk patients. Between-artery differences require confirmation in larger cohorts.
Source: Research Square
Institutions: Chinese PLA General Hospital, Hainan General Hospital
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225656049