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A nomogram model based on HALP score and sST2 for predicting 1-year MACE risk after PCI in acute myocardial
Chen-Yan Li1,2, Hai-Bo Wu2, Ya-Wei Duan2
1Hebei North University, Zhangjiakou, Hebei, China.
Insights
A new nomogram model integrating the HALP score and sST2 effectively predicts major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in acute myocardial infarction (AMI) patients. This tool aids in personalized risk stratification for better patient outcomes.
Area of Science:
- Cardiology
- Biomarkers
- Predictive Modeling
Background:
- Acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention (PCI) face significant risks of major adverse cardiovascular events (MACE).
- Accurate risk stratification is crucial for optimizing post-PCI management and improving patient outcomes.
- Existing predictive models may not fully capture the complex interplay of factors influencing MACE risk in this population.
Purpose of the Study:
- To develop and validate a nomogram model for predicting 1-year MACE risk in AMI patients post-PCI.
- To integrate the HALP score (hemoglobin, albumin, lymphocytes, platelets) and soluble ST2 (sST2) with clinical factors into a comprehensive predictive tool.
- To enhance individualized risk assessment for patients undergoing PCI for AMI.
Main Methods:
- Retrospective analysis of 236 AMI patients who underwent emergency PCI.
- Identification of independent predictors of MACE using multivariate logistic regression.
- Construction and validation of a nomogram model using receiver operating characteristic (ROC) curves and Bootstrap resampling.
Main Results:
- Killip class IV, high sST2, high LDL-C, high LVEDD, and low HALP score were identified as independent predictors of MACE.
- The combined nomogram model demonstrated strong predictive performance (AUC = 0.833) with good calibration.
- The model achieved a sensitivity of 87.3% and specificity of 68.7% for MACE prediction.
Conclusions:
- The developed nomogram, integrating the HALP score and sST2 with clinical variables, provides an effective tool for predicting MACE risk after PCI in AMI patients.
- This visual tool facilitates individualized risk stratification, potentially leading to tailored therapeutic strategies.
- The model's ability to incorporate inflammatory-nutritional status (HALP) and myocardial fibrosis markers (sST2) offers a more holistic approach to risk assessment.
Objective:
To develop a nomogram model integrating the HALP score (a composite score of hemoglobin, albumin, lymphocytes, and platelets) and sST2 for predicting the risk of major adverse cardiovascular events (MACE) within 1 year after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI).
Methods:
This retrospective analysis included 236 AMI patients undergoing emergency PCI (2019-2024), categorized into MACE (n = 102) and non-MACE (n = 134) groups. Independent predictors were identified through multivariate logistic regression analysis, and a nomogram model was constructed. Model performance was validated using receiver operating characteristic (ROC) curves and the Bootstrap method (N = 1,000).
Results:
Multivariate analysis revealed that Killip class IV (OR = 3.758, P = 0.009), high sST2 levels (OR = 1.008, P = 0.009), high LDL-C (OR = 1.533, P = 0.041), high LVEDD (OR = 1.106, P = 0.009), and low HALP score (OR = 0.958, P = 0.023) were independent predictors of MACE. The combined model exhibited significantly better predictive performance than single indicators (AUC = 0.833, 95% CI: 0.781-0.886), with a sensitivity of 87.3% and specificity of 68.7%. The nomogram demonstrated good calibration after Bootstrap validation (Hosmer-Lemeshow test P = 0.157).
Conclusion:
The nomogram model developed in this study, which integrates the HALP score (reflecting inflammatory-nutritional status) and sST2 (a marker of myocardial fibrosis) along with clinical indicators, can effectively predict the risk of MACE after PCI and provides a visual tool for individualized risk stratification.
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