Roadmap analysis for coronary artery stenosis detection and percutaneous coronary intervention prediction in cardiac CT for transcatheter aortic valve replacement.
Authors
Affiliations (8)
Affiliations (8)
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiology, Nihon University Itabashi Hospital, Tokyo, Japan.
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiovascular Medicine, Department of Internal Medicine, Faculty of Medicine, Toho University, Tokyo, Japan.
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Division of Cardiology, Department of Internal Medicine, Inje Univeristy Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea.
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiology, Healthpoint Hospital, Abu Dhabi, United Arab Emirates.
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
- Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, United States; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States. Electronic address: [email protected].
Abstract
The new artificial intelligence-based software, Roadmap (HeartFlow), may assist in evaluating coronary artery stenosis during cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). Consecutive TAVR candidates who underwent both cardiac CT angiography (CTA) and invasive coronary angiography were enrolled. We evaluated the ability of three methods to predict obstructive coronary artery disease (CAD), defined as ≥50 % stenosis on quantitative coronary angiography (QCA), and the need for percutaneous coronary intervention (PCI) within one year: Roadmap, clinician CT specialists with Roadmap, and CT specialists alone. The area under the curve (AUC) for predicting QCA ≥50 % stenosis was similar for CT specialists with or without Roadmap (0.93 [0.85-0.97] vs. 0.94 [0.88-0.98], p = 0.82), both significantly higher than Roadmap alone (all p < 0.05). For PCI prediction, no significant differences were found between QCA and CT specialists, with or without Roadmap, while Roadmap's AUC was lower (all p < 0.05). The negative predictive value (NPV) of CT specialists with Roadmap for ≥50 % stenosis was 97 %, and for PCI prediction, the NPV was comparable to QCA (p = 1.00). In contrast, the positive predictive value (PPV) of Roadmap alone for ≥50 % stenosis was 49 %, the lowest among all approaches, with a similar trend observed for PCI prediction. While Roadmap alone is insufficient for clinical decision-making due to low PPV, Roadmap may serve as a "second observer", providing a supportive tool for CT specialists by flagging lesions for careful review, thereby enhancing workflow efficiency and maintaining high diagnostic accuracy with excellent NPV.