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Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
Published on: January 28, 2020
Association of inflammatory markers based on routine blood with prognosis in patients underwent percutaneous coronary
Li Kun Huo1,2, Kang Yin Chen1, Gary Tse1,3,4
1Department of Cardiology, Tianjin Key Laboratory of Ions and Molecular Function of Cardiovascular Diseases, The Second Hospital of Tianjin Medical University, Tianjin Institute of Cardiology, Tianjin, China.
Insights
Inflammation markers, including neutrophil-to-lymphocyte ratio (NLR) and red cell distribution width (RDW), predict 1-year mortality after percutaneous coronary intervention (PCI). Higher NLR and RDW levels are associated with increased risk, improving patient risk stratification.
Area of Science:
- Cardiology
- Inflammation Research
- Biomarker Analysis
Background:
- Inflammation is a key factor in coronary artery disease (CAD) progression.
- Percutaneous coronary intervention (PCI) is a common treatment for CAD.
- Predicting mortality after PCI is crucial for patient management.
Purpose of the Study:
- To investigate the association between inflammatory biomarkers and 1-year all-cause mortality in patients undergoing PCI.
- To evaluate the predictive value of neutrophil-to-lymphocyte ratio (NLR) and red cell distribution width (RDW) for mortality after PCI.
Main Methods:
- Retrospective cohort study of 4651 patients who underwent PCI.
- Analysis of baseline demographic, clinical, and laboratory parameters.
- Cox regression, restricted cubic spline, and ROC analyses were used to assess the association between biomarkers and mortality.
Main Results:
- 198 (4.26%) patients died within 1 year post-PCI.
- NLR and RDW were significant independent predictors of 1-year all-cause mortality after adjusting for confounders.
- Multivariable models incorporating NLR and RDW improved the area under the curve to 0.83 for mortality prediction.
Conclusions:
- NLR and RDW are valuable inflammatory biomarkers for predicting 1-year mortality in patients after PCI.
- These biomarkers can enhance risk stratification and inform clinical decision-making.
- A nomogram was developed for predicting individual mortality risk.
Abstract:
Inflammation contributes to the pathophysiological processes of coronary artery disease. We evaluated the association between inflammatory biomarkers, neutrophil-to-lymphocyte ratio (NLR), red cell distribution width (RDW), systemic inflammatory index, platelet-lymphocyte ratio, and 1-year all-cause mortality in patients underwent percutaneous coronary intervention (PCI). In this retrospective cohort, we consecutively enrolled 4651 patients who underwent PCI. Baseline demographic details, clinical data, and laboratory parameters on admission were analyzed. The primary outcome was 1-year all-cause mortality after PCI. We performed Cox regression and restricted cubic spline analysis to assessed the association between the inflammatory biomarkers and the clinical outcome. The area under the curve from receiver operating characteristic analysis was determined for the ability to classify mortality outcomes. A total of 4651 patients were included. Of these, 198 (4.26%) died on follow-up. Univariate Cox regression showed that NLR (heart rate [HR]: 1.070, 95% confidence interval [CI]: 1.060-1.082, P < .001), RDW (HR: 1.441, 95% CI 1.368-1.518, P < .001), systemic inflammatory index (HR: 1.000, 95% CI 1.000-3.180, P < .001), platelet-lymphocyte ratio (HR: 3.812, 95% CI 1.901-3.364, P < .001) were significant predictors of 1-year all-cause mortality. After adjusting for other confounders in multivariate analysis, NLR (HR: 01.038, 95% CI 1.022-1.054, P < .001) and RDW (HR: 1.437, 95% CI 1.346-1.535, P < .001) remained significant predictors. Restricted cubic spline analysis showed the relationship between RDW, NLR, and 1-year all-cause mortality was linear after adjusting for the covariables (P for non-linearity < 0.001). The multivariable adjusted model led to improvement in the area under the curve to 0.83 (P < .05). Nomogram was created to predict the probability of 1 year mortality. Among the laboratory indices, RDW and NLR showed the best performance for mortality risk prediction. Multivariate predictive models significantly improved risk stratification.
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