Risk factors and assessment system for cardiogenic death and stroke in patients with premature acute myocardial
Xinyu Hou1, Jiatong Liu1, Jinling Zhang1
1Department of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Insights
This study developed a new scoring scale and prediction model to assess risks of death and stroke in younger Acute Myocardial Infarction (AMI) patients, outperforming existing methods for better clinical decisions.
Area of Science:
- Cardiology
- Clinical Risk Prediction
Background:
- Acute Myocardial Infarction (AMI) incidence is increasing in younger populations.
- Current treatment protocols show limited improvements in morbidity and mortality for premature AMI.
Purpose of the Study:
- Identify risk factors for 1-year cardiogenic death and stroke in patients <=55 years.
- Develop a prognostic risk prediction model and scoring scale for comprehensive risk assessment.
Main Methods:
- Utilized clinical study data (NCT03297164) and follow-up center database (3630 participants).
- Employed Cox regression and LASSO for predictive factor identification.
- Developed a scoring scale using multivariable Cox regression coefficients.
Main Results:
- Identified seven key predictors for risk assessment.
- The developed scoring scale demonstrated superior performance (AUC 0.75-0.77) compared to the GRACE score (AUC 0.61-0.50).
- Optimized binning strategies yielded a model with AUC 0.83 in the external set; a web application was created.
Conclusions:
- A practical scoring scale and prediction model for premature AMI patients were developed and validated.
- The model offers comprehensive risk assessment, aiding clinical decision-making.
- Optimized binning strategies enhance predictive accuracy for cardiovascular events.
Background:
The incidence of Acute Myocardial Infarction (AMI) is rising among younger populations. Despite advancements in treatment protocols, improvements in morbidity and mortality remain limited.
Objective:
To identify risk factors for cardiogenic death and stroke within one year in prematureAMI patients (≤55 years) and to develop a prognostic risk prediction model and scoring scale for comprehensive risk assessment.
Methods:
Utilizing clinical study NCT03297164 and the follow-up center database, we included 3630 participants enrolled from January 2017 to August 2022 to create training and testing sets. An external set (n = 472) was then selected. Cox proportional hazards and LASSO regression were employed to identify predictive factors, and β coefficients from multivariable Cox regression were utilized to develop the scoring scale.
Results:
Seven predictors were selected. The scoring scale achieved an AUC of 0.75 (0.66-0.84) in the test set and 0.77 (0.63-0.91) in the external set, outperforming the GRACE score (0.61 and 0.50, respectively). Based on event rate distributions, patients were stratified into three risk groups, with significant differences in event rates observed across subsets (log-rank test, P < 0.05). Further optimization of binning strategies, guided by the correlation between predictors and outcomes, resulted in a model with an AUC of 0.83 (0.72-0.93) in the external set. A corresponding web application was developed for supplementary risk assessment.
Conclusions:
This study developed and validated a practical scoring scale and a prediction model based on optimized binning strategies for premature AMI patients, offering a comprehensive risk assessment to support clinical decision-making.
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