Predicting left ventricular remodeling post-MI through coronary physiological measurements based on computational fluid dynamics
Wen Zheng,
Qian Guo,
Ruifeng Guo,
Yingying Guo,
Hui Wang,
Lei Xu,
Yunlong Huo,
Hui Ai,
Bin Que,
Xiao Wang,
Shaoping Nie
Affiliations
Wen Zheng
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Qian Guo
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Ruifeng Guo
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Yingying Guo
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Hui Wang
Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Lei Xu
Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Yunlong Huo
Institute of Mechanobiology & Medical Engineering, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, China
Hui Ai
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Bin Que
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China
Xiao Wang
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China; Corresponding author
Shaoping Nie
Center for Coronary Artery Disease, Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing, China; Corresponding author
Summary: Early detection of left ventricular remodeling (LVR) is crucial. While cardiac magnetic resonance (CMR) provides valuable information, it has limitations. Coronary angiography-derived fractional flow reserve (caFFR) and index of microcirculatory resistance (caIMR) offer viable alternatives. 157 patients with ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention were prospectively included. 23.6% of patients showed LVR. Machine learning algorithms constructed three LVR prediction models: Model 1 incorporated clinical and procedural parameters, Model 2 added CMR parameters, and Model 3 included echocardiographic and functional parameters (caFFR and caIMR) with Model 1. The random forest algorithm showed robust performance, achieving AUC of 0.77, 0.84, and 0.85 for Models 1, 2, and 3. SHAP analysis identified top features in Model 2 (infarct size, microvascular obstruction, admission hemoglobin) and Model 3 (current smoking, caFFR, admission hemoglobin). Findings indicate coronary physiology and echocardiographic parameters effectively predict LVR in patients with STEMI, suggesting their potential to replace CMR.
