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An interpretable machine learning approach to predict left ventricular aneurysm formation following primary PCI in
Wensi Zhao1, Guoran Ruan2, Hongbin Wang3
1Department of Oncology, Renmin Hospital of Wuhan University, 238 Jiefang Road, Wuchang District, Wuhan 430060, China.
Insights
A new interpretable model predicts left ventricular aneurysm (LVA) after ST-segment elevation myocardial infarction (STEMI) treatment. This tool uses routine clinical data to assess patient risk for LVA, aiding personalized follow-up planning.
Area of Science:
- Cardiology
- Medical Informatics
- Predictive Analytics
Background:
- Left ventricular aneurysm (LVA) is a significant complication post-percutaneous coronary intervention (pPCI) for ST-segment elevation myocardial infarction (STEMI).
- Accurate prediction of LVA is crucial for patient management and risk stratification.
Purpose of the Study:
- To develop and externally validate an interpretable prediction model for LVA after pPCI in STEMI patients.
- To identify key clinical predictors of LVA using machine learning and statistical methods.
Main Methods:
- Retrospective analysis of 1507 (development) and 535 (validation) STEMI patients.
- Feature selection using LASSO regression and Boruta algorithm, identifying 8 routine predictors.
- Model development using 8 machine learning algorithms, with logistic regression selected for its balance of performance and interpretability.
Main Results:
- The logistic regression model demonstrated high predictive accuracy with an AUC of 0.948 (internal) and 0.950 (external) validation.
- Key predictors identified by SHAP analysis include left ventricular ejection fraction (LVEF), N-terminal pro-B-type natriuretic peptide (NT-proBNP), Killip class ≥2, and C-reactive protein (CRP).
- The model showed acceptable calibration and clinical net benefit via decision curve analysis.
Conclusions:
- An interpretable, externally validated model for LVA prediction in STEMI patients was successfully developed.
- The model, utilizing routine clinical variables, can support individualized risk assessment and optimize follow-up strategies for post-pPCI STEMI patients.
Background:
Left ventricular aneurysm (LVA) remains a clinically important structural complication after primary percutaneous coronary intervention (pPCI) in patients with ST-segment elevation myocardial infarction (STEMI). This study aimed to develop and externally validate an interpretable model for predicting LVA after pPCI.
Methods:
We retrospectively included 1507 patients from the development center and 535 patients from an external validation center. Least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm were used for feature selection. Eight routinely available predictors were retained: albumin (ALB), N-terminal pro-B-type natriuretic peptide (NT-proBNP), C-reactive protein (CRP), Killip class ≥2, left ventricular ejection fraction (LVEF), Gensini score, lactate dehydrogenase (LDH), and infarct-related artery involving the left anterior descending artery (IRA-LAD). These variables were incorporated into eight machine learning algorithms. Model performance was evaluated using discrimination, calibration, decision curve analysis (DCA), and classification metrics. SHapley Additive exPlanations (SHAP) was used for model interpretation, and a web-based calculator was developed.
Results:
Logistic regression showed the most favorable balance between performance and interpretability. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.948 (95% confidence interval [CI], 0.897-0.997) in internal validation and 0.950 (95% CI, 0.908-0.992) in external validation. Calibration and DCA showed acceptable agreement and clinical net benefit. SHAP analysis identified LVEF, NT-proBNP, Killip class ≥2, and CRP as major predictors.
Conclusions:
An interpretable model using routine clinical variables was developed and externally validated for predicting LVA after pPCI in STEMI patients. This model may support individualized risk assessment and follow-up planning.
