Prognostic significance of pan-immune-inflammatory value in adverse cardiovascular and cerebrovascular events post-percutaneous coronary intervention in diabetic patients with coronary heart disease - Report - MDSpire
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Prognostic significance of pan-immune-inflammatory value in adverse cardiovascular and cerebrovascular events post-percutaneous coronary intervention in diabetic patients with coronary heart disease
Clinical Report: Prognostic Role of Pan-Immune-Inflammatory Value in PCI
Overview
The pan-immune-inflammation value (PIV) is a significant predictor of major adverse cardiovascular and cerebrovascular events (MACCE) in diabetic patients following percutaneous coronary intervention (PCI).
Background
Coronary heart disease (CHD) is a leading cause of morbidity and mortality, particularly in patients with diabetes mellitus (DM), who face higher risks of adverse outcomes post-PCI. The role of systemic inflammation in the pathogenesis of cardiovascular complications highlights the need for effective risk stratification tools. PIV, a novel biomarker, integrates various immune cell counts to reflect systemic inflammatory status, potentially enhancing prognostic capabilities in this high-risk population.
Data Highlights
Parameter
High PIV Group
Low PIV Group
p-value
Incidence of MACCE
37.7%
11.5%
< 0.001
MACCE-free survival
Worse
Better
< 0.001
Adjusted HR for high PIV
2.87
-
0.001
AUC for PIV
0.74
-
-
AUC for SII
0.69
-
-
AUC for NLR
0.66
-
-
AUC for PLR
0.64
-
-
Key Findings
PIV is a robust independent predictor of MACCE post-PCI in diabetic patients.
High PIV correlates with a significantly higher incidence of MACCE (37.7% vs. 11.5%, p < 0.001).
Patients with high PIV exhibited worse MACCE-free survival (log-rank p < 0.001).
PIV outperformed other inflammatory markers such as SII, NLR, and PLR in prognostic accuracy.
PIV offers a cost-effective method for risk stratification in diabetic patients undergoing PCI.
Clinical Implications
PIV can be utilized as a biomarker for identifying diabetic patients at increased risk of adverse cardiovascular events following PCI.
Conclusion
PIV serves as a prognostic tool for predicting adverse outcomes in diabetic patients post-PCI.
An interpretable machine-learning model classified angiographic coronary artery disease in patients referred for coronary angiography, but high disease prevalence and unclear inflammatory signals limited clinical interpretation.
