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Latent Class Analysis Identifies Distinct Patient Phenotypes Associated With Mistaken Treatment Decisions and Adverse

Jing Qi1, Zhiqiang Wang1, Xiaoteng Ma1

  • 1Department 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.

Angiology
|July 19, 2025
PubMed

Insights

Patients with comorbidities or smoking/drinking habits undergoing percutaneous coronary intervention (PCI) face higher risks of mistaken treatment and major adverse cardiovascular events (MACE). These risks are significantly greater compared to healthier patient groups.

Area of Science:

  • Cardiology
  • Medical Technology
  • Data Science

Background:

  • Percutaneous coronary intervention (PCI) is a common procedure for coronary artery disease (CAD).
  • Accurate assessment of lesion severity is crucial for optimal PCI decision-making.
  • Deep learning-based fractional flow reserve (DEEPVESSEL-FFR, DVFFR) offers a novel approach to FFR assessment.

Purpose of the Study:

  • To identify patient characteristics associated with suboptimal treatment decisions in PCI.
  • To investigate the link between patient profiles and major adverse cardiovascular events (MACE) post-PCI.
  • To evaluate the utility of DVFFR in stratifying PCI patient risk.

Main Methods:

  • Retrospective analysis of 3,840 PCI patients.
  • Latent class analysis (LCA) to categorize patients into distinct groups based on eight factors.
  • Definition of mistaken treatment: revascularization despite negative DVFFR or no revascularization despite positive DVFFR.

Main Results:

  • Three patient classes identified: comorbidities (Class 1), smoking-drinking (Class 2), and relatively healthy (Class 3).
  • Mistaken treatment rates were highest in the smoking-drinking group (15.4%) compared to the others.
  • Major adverse cardiovascular events (MACE) were most frequent in the comorbidities group (7.0%).
  • Adjusted analyses confirmed higher risks of mistaken treatment in Class 1 (OR 1.96) and Class 2 (OR 1.69).
  • Class 1 patients exhibited a significantly higher risk of MACE (HR 1.53).

Conclusions:

  • Patient characteristics, particularly comorbidities and smoking/drinking habits, are significantly associated with increased risks of suboptimal PCI treatment.
  • These patient groups also face a higher likelihood of experiencing major adverse cardiovascular events (MACE).
  • DVFFR-based risk stratification can help identify patients who may benefit from closer monitoring or tailored treatment strategies.

Keywords:
DEEPVESSEL-FFRcomputed tomography angiographycoronary artery diseasefractional flow reservelatent class analysismistaken treatment

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