Contemporary Predictors of Major Adverse Cardiovascular Events Following Percutaneous Coronary Intervention: A

Benjamin D Horne1,2,3, Nipun Atreja4, John Venditto4

  • 1Intermountain Medical Center Heart Institute, Salt Lake City, UT 84107, USA.

PubMed

Insights

Predictors of major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) were identified. A new model helps assess patient risk and improve post-PCI care outcomes.

Area of Science:

  • Cardiovascular Medicine
  • Interventional Cardiology
  • Health Outcomes Research

Background:

  • Patient outcomes following percutaneous coronary intervention (PCI) have significantly improved.
  • Contemporary predictors of major adverse cardiovascular events (MACE) post-PCI require updated evaluation.
  • Risk stratification is crucial for optimizing post-PCI patient care.

Purpose of the Study:

  • To identify contemporary predictors of MACE after PCI.
  • To develop a parsimonious risk prediction model for post-PCI MACE.
  • To evaluate the effectiveness of current treatments in mitigating MACE.

Main Methods:

  • Utilized the Cardiovascular Patient-Level Analytical Platform (CLiPPeR) dataset, integrating CathPCI Registry and national claims data.
  • Analyzed longitudinal outcomes (2-6 years) for 1,450,787 patients undergoing PCI between 2012-2015.
  • Employed Cox regression, landmarked 28 days post-PCI, to assess clinical and procedural predictors of MACE.

Main Results:

  • 12.4% of patients experienced MACE. Key predictors included cardiogenic shock, cardiac arrest, four-vessel disease, and chronic kidney disease (HR ≥ 1.50).
  • Other significant predictors: in-hospital stroke, three-vessel disease, anemia, heart failure, and STEMI presentation.
  • Discharge medications (aspirin, P2Y12 inhibitor, lipid-lowering) and revascularization were protective (HR ≤ 0.67).

Conclusions:

  • Identified key clinical and procedural predictors of longitudinal MACE risk in a national US post-PCI population.
  • Developed a parsimonious risk model that efficiently encapsulates these predictors.
  • Findings can inform care process assessment to further enhance post-PCI outcomes.