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Risk Prediction Model Based on Biomarkers of Remodeling in Patients with Acute Anterior ST-Segment Elevation
Zeyan Liu1,2, Lijun Liu1, Jinglin Cheng2
1Department of Emergency Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China (mainland).
Insights
A new risk model accurately predicts left ventricular remodeling (LVR) in acute anterior ST-segment elevation myocardial infarction (STEMI) patients. This model, using key clinical factors, offers superior diagnostic accuracy for identifying patients at risk of LVR post-MI.
Area of Science:
- Cardiology
- Medical Diagnostics
- Biomedical Engineering
Background:
- Acute anterior ST-segment elevation myocardial infarction (STEMI) poses significant risks for patients.
- Left ventricular remodeling (LVR) is a critical complication following STEMI, impacting long-term prognosis.
- Effective risk prediction models are needed to identify STEMI patients prone to LVR.
Purpose of the Study:
- To develop and validate a novel risk prediction model for LVR in patients with acute anterior STEMI.
- To identify key clinical, echocardiographic, and biochemical factors associated with LVR development post-STEMI.
- To assess the diagnostic performance of the developed model using receiver operating characteristic (ROC) curve analysis.
Main Methods:
- Retrospective analysis of clinical data from 333 patients with acute anterior STEMI.
- Comparison of clinical, echocardiographic, and angiographic data between patients with and without LVR at 6-month follow-up.
- Multivariate logistic regression analysis to identify independent predictors of LVR.
- Calculation of the area under the curve (AUC) for model performance evaluation.
Main Results:
- The study identified the number of stenosed coronary vessels, left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), transforming growth factor-beta (TGF-ß) at admission, and cardiac troponin I 3 days after admission (3-d cTnI) as significant predictors of LVR.
- A prediction model incorporating these factors demonstrated an AUC of 0.978, indicating excellent diagnostic accuracy.
- The model's performance significantly surpassed that of individual predictive factors.
Conclusions:
- The developed risk prediction model exhibits high diagnostic accuracy for identifying LVR in acute anterior STEMI patients.
- The model integrates readily available clinical and biochemical parameters, facilitating practical clinical application.
- This tool can aid in early risk stratification and timely intervention for STEMI patients at risk of LVR.
Abstract:
BACKGROUND The aim of the present study was to develop a risk prediction model in patients with acute anterior ST-segment elevation myocardial infarction (STEMI). MATERIAL AND METHODS Clinical data from 333 patients with acute anterior STEMI were retrospectively analyzed. Clinical echocardiographic and angiographic data from patients with left ventricular remodeling (LVR) and those without LVR were compared. Factors that influenced risk were identified using multivariate logistic regression analysis. The area under the curve (AUC) of the receiver operating characteristic curve was used to assess the diagnostic performance of the model. RESULTS After 6-month follow-up, 135 of the patients experienced LVR (LVR group), whereas 198 did not (non-LVR group). Results of multivariate analysis showed that the number of stenosed coronary vessels, left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), transforming growth factor-beta (TGF-ß) at admission, and cardiac troponin I 3 days after admission (3-d cTnI) were all factors predictive of LVR in patients with acute anterior STEMI (all P<0.05). The established prediction model was Y=-20.639+0.711×number of stenosed coronary vessels + 0.137×LVEDV-0.129×LVEF+0.026×TGF-ß at admission + 0.162×3-d cTnI. The estimated AUC of this model was 0.978 (95% confidence interval [CI] 0.955-0.991), significantly superior to the single-factor numbers for stenosed coronary vessel of 0.650 (95% CI 0.597-0.702), LVEDV of 0.876 (95% CI 0.836-0.910), LVEF of 0.684 (95% CI 0.631-0.734), TGF-ß at admission of 0.696 (95% CI 0.644-0.745), cTnI at admission of 0.913 (95% CI 0.877-0.941), and 3-d cTnI of 0.945 (95% CI 0.914-0.967). CONCLUSIONS The established model had excellent diagnostic accuracy for predicting LVR in patients with acute anterior STEMI.
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