The Prognostic Value of the Advanced Lung Cancer Inflammation Index for Major Cardiovascular and Cerebrovascular

Mehmet Karaca1, Muhsin Kalyoncuoğlu2, Ahmet Zengin3

  • 1Cardiology Department, Atasehir Memorial Hospital, Uskudar University, Istanbul 34662, Turkey.

PubMed

Insights

The advanced lung cancer inflammation index (ALI) can predict major adverse cardiovascular and cerebrovascular events (MACCEs) in non-ST elevation myocardial infarction (NSTEMI) patients. Lower ALI values indicate a higher risk of MACCEs after percutaneous coronary intervention (PCI).

Area of Science:

  • Cardiology
  • Biomarkers
  • Inflammation

Background:

  • Non-ST elevation myocardial infarction (NSTEMI) patients undergoing percutaneous coronary intervention (PCI) face risks of major adverse cardiovascular and cerebrovascular events (MACCEs).
  • Inflammation plays a crucial role in cardiovascular disease progression.
  • Novel biomarkers are needed for accurate risk stratification in NSTEMI patients.

Purpose of the Study:

  • To investigate the prognostic value of the admission advanced lung cancer inflammation index (ALI) for one-year MACCEs in NSTEMI patients undergoing PCI.
  • To compare the predictive accuracy of ALI with other inflammation-based biomarkers.

Main Methods:

  • A cohort of 1173 NSTEMI patients undergoing PCI was studied.
  • ALI was calculated using body mass index (BMI), serum albumin, and neutrophil-to-lymphocyte ratio (NLR).
  • ROC analysis was used to compare the discriminatory power of ALI against NLR, CAR, and UAR for predicting one-year MACCEs.

Main Results:

  • 13.5% of patients experienced MACCEs within one year.
  • Lower admission ALI values were significantly associated with increased MACCEs (p < 0.001).
  • ALI demonstrated superior predictive accuracy for MACCEs compared to albumin, NLR, CAR, and UAR (AUC = 0.658).

Conclusions:

  • Admission ALI is a valuable prognostic biomarker for predicting one-year MACCEs in NSTEMI patients undergoing PCI.
  • An ALI value below 43.9 identifies patients at higher risk.
  • Incorporating ALI into risk assessment may enhance prognostication and guide clinical management.

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