The use of mechanical circulatory support in elective high-risk percutaneous coronary interventions: a

Alexander Geppert1, Kambis Mashayekhi2,3, Kurt Huber1,4

  • 13rd Department of Medicine, Cardiology and Intensive Care Medicine, Clinic Ottakring, Montleartstrasse 37, A-1160 Vienna, Austria.

PubMed

Insights

Mechanical circulatory support (MCS) devices may aid high-risk percutaneous coronary interventions (HR-PCIs). While IABP use declined, V-A ECMO and Impella show potential, necessitating further trials and a new algorithm for patient selection.

Area of Science:

  • Cardiology
  • Interventional Cardiology
  • Medical Devices

Background:

  • Percutaneous coronary intervention (PCI) is increasingly performed in high-risk patients.
  • Mechanical circulatory support (MCS) devices are used to mitigate risks during high-risk PCI (HR-PCI).
  • Common MCS devices include intra-aortic balloon pump (IABP), veno-arterial extracorporeal membrane oxygenation (V-A ECMO), and Impella.

Purpose of the Study:

  • To review clinical evidence for MCS devices in HR-PCI.
  • To explore mechanisms of improved outcomes with MCS in HR-PCI.
  • To propose a novel algorithm for identifying suitable candidates for MCS-assisted HR-PCI.

Main Methods:

  • Review of contemporary medical literature on MCS devices in HR-PCI.
  • Analysis of clinical outcomes associated with IABP, V-A ECMO, and Impella.
  • Development of a predictive algorithm for MCS utilization in HR-PCI.

Main Results:

  • IABP use has decreased due to lack of demonstrated benefit in HR-PCI and cardiogenic shock.
  • V-A ECMO shows low major adverse cardiac and cerebrovascular events (MACCEs) but increased risks of acute kidney injury and transfusion.
  • Impella use has seen declining MACCE rates with improved operator experience, though mortality benefits require further investigation via randomized trials.

Conclusions:

  • Current evidence for IABP in HR-PCI is limited.
  • V-A ECMO and Impella present distinct risk-benefit profiles in HR-PCI.
  • A new algorithm combining anatomical, comorbidity, and clinical factors is proposed to optimize MCS selection for HR-PCI.