Coronary angiography-derived microcirculatory resistance predicts adverse cardiovascular events in elderly with

Kangming Li1,2, Lele Wang3, Panqing Liao1

  • 1Department of Emergency, Guiling People's Hospital, Guilin, China.

Frontiers in Aging
|June 24, 2026
PubMed

Insights

Coronary microvascular dysfunction (CMD) assessment using coronary angiography-derived index of microcirculatory resistance (caIMR) effectively predicts major adverse cardiovascular events (MACE) in elderly patients with unstable angina after percutaneous coronary intervention.

Area of Science:

  • Cardiovascular Medicine
  • Interventional Cardiology
  • Geriatric Cardiology

Background:

  • Coronary microvascular dysfunction (CMD) is a key factor in adverse cardiovascular outcomes.
  • Coronary angiography-derived index of microcirculatory resistance (caIMR) is a promising noninvasive tool for CMD assessment.
  • Prognostic value of caIMR in elderly unstable angina (UA) patients post-percutaneous coronary intervention (PCI) needs further exploration.

Purpose of the Study:

  • To evaluate the prognostic value of post-PCI caIMR in elderly UA patients.
  • To determine if caIMR can stratify risk for major adverse cardiovascular events (MACE).

Main Methods:

  • Retrospective cohort study of 348 elderly UA patients (age ≥65) undergoing PCI.
  • Patients stratified by post-PCI caIMR (cutoff 2.50 mmHg s/cm).
  • Primary endpoint: MACE (myocardial infarction, stroke, heart failure, angina rehospitalization, cardiovascular death). Multivariable Cox regression and subgroup analyses were performed.

Main Results:

  • Elevated caIMR (≥2.50 mmHg s/cm) was associated with significantly higher MACE rates.
  • caIMR independently predicted MACE, with diabetes mellitus also showing predictive value.
  • caIMR demonstrated consistent prognostic value across subgroups, including those with and without hypertension or diabetes.

Conclusions:

  • Noninvasive caIMR assessment is effective in stratifying post-PCI risk in elderly UA patients.
  • caIMR serves as a valuable tool for identifying high-risk individuals for targeted interventions.
Abstract

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