Latent Class Analysis Identifies Distinct Patient Phenotypes Associated With Mistaken Treatment Decisions and Adverse Outcomes in Coronary Artery Disease.

Journal: Angiology
Published Date: Jul 19, 2025

Abstract

This study aimed to identify patient characteristics linked to mistaken treatments and major adverse cardiovascular events (MACE) in percutaneous coronary intervention (PCI) for coronary artery disease (CAD) using deep learning-based fractional flow reserve (DEEPVESSEL-FFR, DVFFR). A retrospective cohort of 3,840 PCI patients was analyzed using latent class analysis (LCA) based on eight factors. Mistaken treatment was defined as negative DVFFR patients undergoing revascularization or positive DVFFR patients not receiving it. MACE included all-cause mortality, rehospitalization for unstable angina, and non-fatal myocardial infarction. Patients were classified into comorbidities (Class 1), smoking-drinking (Class 2), and relatively healthy (Class 3) groups. Mistaken treatment was highest in Class 2 (15.4% vs. 6.7%,  < .001), while MACE was highest in Class 1 (7.0% vs. 4.8%,  < .001). Adjusted analyses showed increased mistaken treatment risk in Class 1 (OR 1.96; 95% CI 1.49-2.57) and Class 2 (OR 1.69; 95% CI 1.28-2.25) compared with Class 3. Class 1 also had higher MACE risk (HR 1.53; 95% CI 1.10-2.12). In conclusion, comorbidities and smoking-drinking classes had higher mistaken treatment and MACE risks compared with the relatively healthy class.

Authors

  • Jing Qi
    China Meat Research Center, Beijing 100068, China.
  • ZhiQiang Wang
    The Affiliated Mental Health Center of Jiangnan University, Wuxi Mental Health Center, Wuxi 214151, Jiangsu, China.
  • Xiaoteng Ma
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical Center for Coronary Heart Disease, Beijing Institute of Heart Lung and Blood Vessel Disease, Capital Medical University, Beijing, China.
  • Zhijian Wang
    Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California 92093, United States.
  • Yueping Li
    School of Computer Engineering, Shenzhen Polytechnic, Shenzhen 518055, China. liyueping@szpt.edu.cn.
  • Lixia Yang
    Department of Gynaecology and Obstetrics, The Affiliated Yixing Hospital of Jiangsu University (Yixing People's Hospital), Yixing, Jiangsu, China.
  • Dongmei Shi
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical Center for Coronary Heart Disease, Beijing Institute of Heart Lung and Blood Vessel Disease, Capital Medical University, Beijing, China.
  • Yujie Zhou
    School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China.

Keywords

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