Mahmoudi, Michael, Nicholas, Zoe, Jabbour, Richard J., Shambrook, James, Abbas, Ausami, Browne, Tevin, Hinton, Jonathan, Antoniades, Charalambos, Mamas, Mamas, Leipsic, Jonathon, Rogers, Campbell, Koo, Bon-kwon, Al-Lamee, Rasha, Kontopantelis, Evangelos and Curzen, Nick (2025) Anatomical, physiological and inflammatory characterization of non-culprit vessels in patients undergoing primary PCI for ST-elevation myocardial infarction in the presence of multivessel disease: rationale and design of the PICNIC study. American Heart Journal, 292, [107298]. (doi:10.1016/j.ahj.2025.107298).
Abstract
Background: up to 50% of patients presenting with ST-elevation myocardial infarction (STEMI) have multivessel coronary artery disease (CAD). Randomized trials suggest that complete revascularization improves outcomes, but the mechanism and identification of patients who benefit remain unclear. This study aims to assess the association between blood and coronary imaging biomarkers and clinical events, to identify patient-, vessel-, and lesion-specific risk in STEMI patients with bystander disease.
Method: PICNIC is a multicenter, international, prospective, observational study enrolling 320 patients with STEMI and multivessel CAD undergoing primary PCI of the culprit vessel without complete revascularization. Participants will undergo blood sampling for inflammatory markers and coronary CT angiography (CTCA) to assess: (1) plaque burden and morphology, (2) artificial intelligence-enabled fractional flow reserve derived from CTCA (FFRCT) analysis of plaque and hemodynamic features, and (3) fat attenuation index (FAI) to evaluate perivascular inflammation.
The primary analysis will evaluate the association between a composite 24-month clinical endpoint (including all-cause mortality, myocardial infarction, ischemia-driven revascularization as first layer and cardiac arrest, heart failure, stroke, and ventricular tachyarrhythmia (second layer)) and: (1) serum inflammatory markers, and (2) anatomical and physiological characteristics of non–infarct-related arteries (NIRA) assessed by CTCA, FFRCT, and FAI. Statistical and machine learning methods will be applied to determine which combinations of clinical, imaging, and biomarker data best predict patient-, vessel-, and lesion-specific risk.
Conclusion: PICNIC will characterize the anatomical, physiological, and inflammatory features of NIRA lesions in STEMI patients treated with culprit-only PCI in order to develop an AI-based risk prediction model. If such a model is successful it could be used to inform personalized revascularization strategies.
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