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A new prediction model for sustained ventricular tachycardia in arrhythmogenic cardiomyopathy
Baowei Zhang1, Xin Xie1, Jinbo Yu1
1Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Insights
A new nomogram accurately identifies patients with arrhythmogenic cardiomyopathy (ACM) at high risk for sustained ventricular tachycardia (sVT). This tool aids in crucial management decisions for ACM patients, improving clinical outcomes.
Area of Science:
- Cardiology
- Genetics
- Medical Diagnostics
Background:
- Arrhythmogenic cardiomyopathy (ACM) is an inherited condition linked to a high risk of sudden cardiac death.
- Identifying ACM patients prone to sustained ventricular tachycardia (sVT) is critical for effective management.
Purpose of the Study:
- To develop and validate a predictive model for identifying ACM patients at high risk of sVT.
- To improve risk stratification and clinical decision-making in ACM management.
Main Methods:
- A retrospective study of 147 ACM patients utilized LASSO regression to identify sVT predictors.
- A nomogram was constructed using multivariable logistic regression and validated internally.
- Model performance was assessed via ROC curve analysis, calibration curves, and decision curve analysis.
Main Results:
- A nomogram incorporating age, male sex, syncope, heart failure, T wave inversion, LVEF, and SDNN level was developed.
- The nomogram demonstrated strong predictive performance with an AUC of 0.867 in the training group and 0.815 in the validation group.
- The model showed good calibration and superior clinical utility compared to existing methods for predicting sVT.
Conclusions:
- A novel, validated prediction model for sVT in ACM patients has been established.
- This nomogram serves as a valuable clinical tool for accurate risk identification in ACM.
- The model supports timely and targeted interventions for high-risk ACM individuals.
Background:
Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiomyopathy characterized by high risks of sustained ventricular tachycardia (sVT) and sudden cardiac death. Identifying patients with high risk of sVT is crucial for the management of ACM.
Methods:
A total of 147 ACM patients were retrospectively enrolled in the observational study and divided into training and validation groups. The least absolute shrinkage and selection operator (LASSO) regression model was employed to identify factors associated with sVT. Subsequently, a nomogram was constructed based on multivariable logistic regression analysis. The performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis was conducted to assess the clinical utility of the nomogram.
Results:
Seven parameters were incorporated into the nomogram: age, male sex, syncope, heart failure, T wave inversion in precordial leads, left ventricular ejection fraction (LVEF), SDNN level. The AUC of the nomogram to predict the probability of sVT was 0.867 (95% CI, 0.797-0.938) in the training group and 0.815 (95% CI, 0.673-0.958) in the validation group. The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. Decision curve analysis indicated that the nomogram had higher overall net benefits in predicting sVT.
Conclusion:
We have developed and internally validated a new prediction model for sVT in ACM. This model could serve as a valuable tool to accurately identify ACM patients with high risk of sVT.
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