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Updated: Jan 7, 2026
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Predictive Value of a Nomogram Model Constructed on the Basis of Residual Cholesterol in Predicting Major
Xiaoyan Yin1, Yuanzhuo Zhang1, Lei Ren1
1Fuyang Hospital of Bengbu Medical University.
Insights
Residual cholesterol (RC) can predict major adverse cardiovascular events (MACE) risk in acute myocardial infarction (AMI) patients post-PCI. An RC-based nomogram model offers a quick tool for identifying high-risk individuals, improving patient outcomes.
Area of Science:
- Cardiology
- Biomarkers
- Predictive Modeling
Background:
- Acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention (PCI) face risks of major adverse cardiovascular events (MACE).
- Identifying high-risk patients early is crucial for optimizing treatment strategies and improving outcomes.
Purpose of the Study:
- To develop and validate a residual cholesterol (RC)-based nomogram prediction model for MACE risk in AMI patients post-emergency PCI.
- To assess the clinical utility of the RC nomogram as a predictive tool.
Main Methods:
- Retrospective analysis of AMI patients who underwent emergency PCI.
- Logistic regression analysis to identify MACE risk factors and construct a nomogram model.
- Internal validation using bootstrap resampling and evaluation via ROC curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA).
Main Results:
- Residual cholesterol, symptom onset to first medical contact >90 minutes, number of involved coronary vessels, Killip scale II-IV, and hemoglobin concentration were significant MACE predictors (P < 0.05).
- The nomogram demonstrated good predictive performance with an ROC-AUC of 0.780 (0.721-0.839).
- The model showed moderate discrimination and calibration, with DCA indicating net clinical benefit.
Conclusions:
- Residual cholesterol is a valuable biomarker for stratifying MACE risk in AMI patients post-PCI.
- The developed RC-based nomogram serves as an accessible and effective tool for identifying high-risk patients, aiding clinical decision-making.
Abstract:
The aim of this study was to construct a residual cholesterol (RC)-based nomogram prediction model and assess its value in predicting the risk of major adverse cardiovascular events (MACE) after emergency percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI).Retrospective analysis of patients from Fuyang People's Hospital who underwent emergency PCI for AMI at our hospital between January 2022 and December 2023 was performed, and univariate logistic regression was used to screen the risk factors for the first occurrence of MACE in the patients, while multivariable logistic regression analysis was used to construct a prediction model. Internal validation was performed using 1,000 bootstrap resampling. The predictive effect of the nomogram model was evaluated using the receiver operating characteristic curve (ROC), Hosmer-Lemeshow deviance test, and decision curve analysis (DCA).Logistic regression analysis showed that residual cholesterol, greater than 90 minutes from symptom onset to first medical contact (SO-to-FMC > 90 minutes), number of involved coronary vessels, Killip scale II-IV, and hemoglobin concentration were factors influencing the occurrence of MACE after PCI in these AMI patients (P < 0.05). The area under the curve (ROC-AUC) of the nomogram model for predicting the risk of developing postoperative MACE was 0.780 (0.721-0.839); the result of the Hosmer-Lemeshow test of deviance, χ2 = 4.758 (P = 0.783), suggests that the model shows a moderately discriminatory and calibrated decision analysis curve; DCA shows a net clinical benefit with the nomogram model.RC is a promising biomarker for identifying AMI patients at high risk of postoperative MACE, and multivariate models based on RC can be used as quick and easy tools to identify these patients.
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