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Updated: Feb 7, 2026

Interventional Diagnostic Procedure: A Practical Guide for the Assessment of Coronary Vascular Function
Published on: March 15, 2022
[Long-term Outcomes of Elective Percutaneous Coronary Interventions]
1Federal State Budgetary Science Institution "Tomsk Research Institute of Cardiology of the Siberian Branch of the Russian Academy of Science".
Insights
A new model predicts long-term adverse cardiovascular events after percutaneous coronary intervention (PCI). Key predictors include family history, statin use, blood glucose, and contrast-induced nephropathy risk, aiding risk stratification for better patient management.
Area of Science:
- Cardiology
- Interventional Cardiology
- Public Health
Background:
- Elective percutaneous coronary interventions (PCI) are common for chronic ischemic heart disease.
- Long-term adverse cardiovascular events (MACE) remain a concern post-PCI.
- Predicting these events is crucial for optimizing patient care.
Purpose of the Study:
- To analyze long-term outcomes following elective PCI.
- To develop a predictive model for major adverse cardiovascular events (MACE).
- To identify key risk factors for adverse events post-PCI.
Main Methods:
- A cohort of 148 patients undergoing elective PCI for chronic ischemic heart disease was studied.
- Outcomes were assessed over 6 years, including cardiovascular death, acute coronary syndrome (ACS), and stroke.
- Stepwise logistic regression was used to develop a predictive model for MACE.
Main Results:
- Overall MACE occurred in 40.4% of patients over 6 years.
- Significant predictors identified include family history of cardiovascular disease, statin treatment, initial postprandial glucose, and high risk of contrast-induced nephropathy (CIN).
- The developed model demonstrated strong predictive performance (AUC 0.852).
Conclusions:
- A validated model can effectively stratify long-term cardiovascular risk after PCI.
- Identifying high-risk patients allows for tailored, proactive follow-up strategies.
- Simple clinical characteristics are sufficient for risk assessment.
Objective:
to analyze long-term outcomes and to develop a model for determining the risk of long-term adverse сardiovascular events after elective percutaneous coronary interventions (PCI).
Materials And Methods:
We included in this study 148 patients, sent from 2009 to 2011 for routine endovascular intervention for chronic ischemic heart disease on the background of stenotic coronary artery atherosclerosis. Outcomes of interventions were assessed over 6 years after the index PCI by analyzing medical records and telephone interviews. The primary composite endpoint of the study was major adverse cardiovascular event (MACE), including cardiovascular death, acute coronary syndrome (ACS), acute cerebrovascular accident (CVA).
Results:
Cardiovascular death was registered in 10.6 %, acute coronary syndrome occurred in 34.4 %, stroke - in 6.6 % of patients. Overall MAСE occurred in 40.4 % of patients. Patients with MACE were initially significantly more likely to have chronic obstructive pulmonary disease (16.4 vs. 4.4 %, p=0.02), multifocal atherosclerosis (32.8 vs. 17.8 %, p=0.034). They were initially more often diagnosed with atrial fibrillation (AF) (23 vs. 7.8 %, p=0.016) and were more likely to have family history of cardiovascular disease (50.8 vs. 24.4 %, p=0.0009). They had significantly higher levels of CRP before PCI (6 (5-11.5) vs. 5 (4.7) mg/L, p=0.034) and restenosis of previously installed stent (8.2 vs. 1.1 %, p=0.04). Most significant predictors of MACE identified using stepwise logistic regression and included in the developed model were: family history of cardiovascular disease, treatment with statins at time of PCI, initial level of postprandial blood glucose, high risk of contrast induced nephropathy (CIN) (11-15 points on Mehran CIN risk score). AUC values for the found model was 0.852 [95 % CI 0.749-0.956].
Conclusion:
The use of our model in patients with the upcoming PCI with the aim of stratifying the risk of long-term adverse cardiovascular events allows to identify groups of patients, who require the timely administration of more active follow-up strategies, through the use of simple clinical characteristics.
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