ArticleBMC cardiovascular disorders2026
The Pan-Immune-Inflammation Value (PIV) predicts major adverse cardiovascular events in elderly patients undergoing percutaneous coronary intervention: a real-world study.
Article in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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7 authors.
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Abstract
purposeThe Pan-Immune-Inflammation Value (PIV), a novel inflammatory marker primarily studied in cancer, remains underinvestigated in elderly PCI patients. This study evaluates PIV’s prognostic value for risk stratification and personalized treatment in this population. PATIENTS AND
methodsIn this study, we enrolled elderly patients undergoing PCI who were aged 75 years or older between the years 2019 and 2023. Patients were divided into low- and high-PIV groups based on the median value of the cohort. The primary endpoint was the incidence of major adverse cardiovascular events (MACE), comprising cardiac death, recurrent myocardial infarction, and target vessel revascularization. Secondary endpoints included the individual components of MACE. Cox regression and ROC analyses were employed to evaluate the independent prognostic value of PIV.
resultsPatients in the high-PIV group presented with a higher burden of comorbidities and worse cardiac function at baseline. During a median follow-up of 362 days, the high-PIV group experienced a significantly higher incidence of MACE (10.8% vs. 5.1%, P < 0.001), cardiac death (7.1% vs. 2.8%, P < 0.001), and all-cause mortality (10.8% vs. 4.5%, P < 0.001). Multivariable Cox regression confirmed PIV as an independent predictor of MACE after adjusting for confounders (Model 3: HR 1.572, 95% CI 1.040,2.377, P = 0.032). ROC analysis showed that PIV had superior predictive ability for MACE (AUC = 0.641) compared to models combining PIV with age ≥ 80 years.
conclusionPIV serves as a simple, potent, and independent prognostic biomarker for MACE in elderly patients following PCI. Its integration into clinical risk stratification could help identify high-risk patients who may benefit from more intensive management.
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