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Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
Published on: January 28, 2020
An inflammatory prognostic scoring system to predict the risk for adults with acute coronary syndrome undergoing
Ge Song1, Ying Zhang1,2,3, Xinchen Wang1
1Department of Cardiology, The Affiliated Hospital of Chengde Medical University, No.36, Nanyingzi Street, Shuangqiao District, 067000, Chengde, Hebei Province, China.
Insights
The inflammatory prognostic score (IPS) effectively predicts major adverse cardiovascular events (MACEs) in acute coronary syndrome (ACS) patients after percutaneous coronary intervention (PCI). A higher IPS indicates increased risk, aiding long-term prognosis assessment.
Area of Science:
- Cardiology
- Biomarkers
- Risk Stratification
Background:
- Acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) face significant cardiovascular event risks.
- Accurate risk stratification is crucial for optimizing patient management and long-term outcomes.
Purpose of the Study:
- To evaluate the prognostic value of the inflammatory prognostic score (IPS) for predicting major adverse cardiovascular events (MACEs) in ACS patients post-PCI.
- To assess the IPS's ability to predict both all-cause and cardiac-related MACEs.
Main Methods:
- Analysis of 1384 ACS patients who underwent PCI, collecting demographic and 11 inflammatory marker data.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO)-COX regression to identify key inflammatory markers (albumin and monocyte-to-lymphocyte ratio) for IPS construction.
- Applied Kaplan-Meier analysis, Cox regression, and receiver operator characteristic (ROC) curves to validate the IPS's predictive power.
Main Results:
- The IPS, constructed from albumin and monocyte-to-lymphocyte ratio, significantly correlated with worse long-term prognosis (log-rank p < 0.001).
- The IPS was identified as an independent risk factor for MACEs in ACS patients undergoing PCI.
- Time-dependent ROC analysis confirmed the IPS's satisfactory predictive value for various MACEs, with a linear association observed via restricted cubic spline analysis.
Conclusions:
- Elevated IPS levels are associated with increased risk in ACS patients undergoing PCI.
- The IPS serves as a valuable tool for risk stratification, enhancing the assessment of long-term prognosis in ACS patients.
Background:
This study aimed to investigate the value of the inflammatory prognostic score (IPS) system for predicting the risk of all-cause major adverse cardiovascular events (MACEs) and cardiac-related MACEs in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI).
Methods:
Overall, 1384 patients with ACS who underwent PCI between January 2016 and December 2018 were consecutively enrolled. Demographic characteristics and related laboratory results for 11 inflammatory markers were collected. Least absolute shrinkage and selection operator (LASSO)-COX regression, Kaplan- Meier, restricted cubic spline (RCS), receiver operator characteristic curve (ROC), time-dependent ROC, and Cox hazard proportional regression were applied to explore the values of individual and IPS parameters.
Results:
Based on the LASSO analysis, albumin (ALB) and monocyte-to-lymphocyte ratio (MLR) were included in the construction of the IPS system. A higher IPS was associated with a significantly worse long-term prognosis in the Kaplan-Meier analysis (log-rank p < 0.001). The Cox proportional hazards model demonstrated that the IPS was an independent risk factor for patients with ACS undergoing PCI. In addition, the IPS remained an independent prognosticator compared to the lowest tertiles. The time-dependent ROC showed satisfactory values for the long-term prognosis of different MACEs. Additionally, RCS showed a linear association with IPS, all-cause MACEs, and cardiac-related MACEs.
Conclusions:
A higher IPS level was associated with an increased risk in patients with ACS undergoing PCI, suggesting that the IPS may be a useful method for risk stratification in the assessment of the long-term prognosis of ACS.
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