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Evaluating the Efficacy of AI-Assisted Quantitative Coronary Angiography in Reducing Procedural Time for Primary PCI
Joon Ho Ahn1, Seongho Park1, Seung Hun Lee1
1Division of Cardiology, Department of Internal Medicine, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea.
Insights
AI-assisted quantitative coronary angiography (QCA) significantly reduced procedure time for ST-elevation myocardial infarction (STEMI) primary percutaneous coronary intervention (PCI). This AI tool streamlines stent sizing and optimization, potentially improving reperfusion times without increasing contrast use.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Rapid reperfusion is critical for ST-elevation myocardial infarction (STEMI) outcomes.
- Primary percutaneous coronary intervention (PCI) is a key reperfusion strategy.
- Optimizing PCI procedure time is essential for patient care.
Purpose of the Study:
- To evaluate the impact of AI-assisted quantitative coronary angiography (QCA) on primary PCI procedure time in STEMI patients.
- To compare the efficiency and safety of AI-QCA-assisted PCI versus standard angiography-guided PCI.
Main Methods:
- Single-center, retrospective study of 1,041 STEMI patients undergoing primary PCI.
- Comparison between AI-QCA-assisted PCI (MPXA software) and angiography-guided PCI.
- Propensity score matching (1:3 ratio) to minimize bias; primary endpoint: time from angiography to PCI completion.
Main Results:
- AI-QCA-assisted PCI group showed a significant reduction in procedure time (34.9 vs. 39.5 minutes, P < 0.001) after matching.
- No significant differences in contrast media volume, in-hospital mortality, or cardiac mortality were observed between groups.
- AI-QCA primarily streamlined stent sizing and optimization processes.
Conclusions:
- AI-assisted QCA significantly shortens primary PCI procedure time for STEMI.
- Time savings may facilitate faster reperfusion, potentially improving clinical outcomes.
- Larger studies are warranted to confirm the clinical benefits of AI-QCA in STEMI treatment.
Background:
Rapid reperfusion is crucial in ST-elevation myocardial infarction (STEMI). We investigated whether AI-assisted quantitative coronary angiography (QCA) can reduce the procedure time for primary percutaneous coronary intervention (PCI) in patients with STEMI.
Methods:
This single-center, retrospective study compared primary PCI assisted by MPXA, an AI-based QCA software, with angiography-guided PCI in 1,041 patients with STEMI. To minimize potential bias, a 1:3 propensity score matching was performed between the treatment and control groups. The primary endpoint was the time from coronary angiography to PCI completion. The secondary endpoints included total contrast media volume during the procedure, in-hospital mortality, and in-hospital cardiac death.
Results:
After propensity score matching, 164 and 492 patients in the AI-QCA-assisted and angiography-guided groups, respectively, were analyzed. The AI-QCA-assisted PCI group showed significant reduction in procedure time than the angiography-guided PCI group (34.9 ± 13.9 vs. 39.5 ± 17.1 minutes, P < 0.001). There was no significant difference in contrast media volume between the two groups (132.0 ± 42.2 vs. 135.8 ± 48.0 mL, P = 0.354). Furthermore, no significant differences were observed in in-hospital mortality (2.4% vs. 3.0%, P = 0.692) or cardiac mortality (2.4% vs. 2.8%, P = 0.786) between the AI-QCA-assisted PCI and control groups.
Conclusion:
AI-assisted QCA significantly reduced the overall procedure time of primary PCI for STEMI, primarily by streamlining stent sizing and optimization processes without decreasing contrast use. Given that STEMI procedures typically begin with small-diameter balloon inflation to maintain blood flow and minimize complications, the time saved by optimal stent deployment could be clinically beneficial by allowing faster complete reperfusion. Larger studies are needed to confirm the potential clinical benefit of reducing the procedure time using AI-QCA in STEMI treatment.
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