Key result
The XGBoost prediction model accurately predicted intracardiac thrombus risk in patients with dilated cardiomyopathy, achieving an AUC of 0.947 compared to 0.854 for logistic regression.
Why the study?
Systemic embolic events from intracardiac thrombus are a catastrophic complication of dilated cardiomyopathy, prompting the need for a predictive model to evaluate this risk.
Observational (n=632)
No
Effect estimate: AUC 0.947 (95% CI 0.924-0.969)
Absolute Event Rate: 0.947% vs 0.854%
p-value: p=<0.05
An XGBoost machine learning model using clinical variables including D-dimer, age, and atrial fibrillation can accurately predict intracardiac thrombus risk in patients with dilated cardiomyopathy.
May support ML-based ICT risk stratification in DCM; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: Systemic embolic events due to exfoliation of intracardiac thrombus (ICT) are one of the catastrophic complications of dilated cardiomyopathy (DCM). This study intended to develop a prediction model to predict the risk of ICT in patients with DCM. METHODS: Data from 632 patients with DCM from a hospital was collected. ICT was identified based on the results of transthoracic echocardiography. Basic information, vital signs, comorbidities, and biochemical data were measured and collected from each patient. The least absolute shrinkage and selection operator (LASSO) regression was used for the final model variable screening. Four classifiers including Logistic Regression, support vector machine (SVM), Random Forest, and eXtreme Gradient Boosting (XGBoost) were used for model construction respectively. The area under of the curve (AUC) with 95% confidence interval (CI), sensitivity, specificity, and accuracy of the models were calculated to assess the predictive ability of the models. RESULTS: Of these 632 DCM patients, 88 (13.92%) had ICT and 544 (86.08%) did not. Eleven clinical variables were selected for the construction of predictive models. The AUC of the Logistic Regression model to predict ICT probability was 0.854 (95%CI: 0.811-0.896), the SVM model was 0.769 (95%CI: 0.715-0.824), the Random Forest model was 0.917 (95%CI: 0.887-0.947), and the XGBoost model was 0.947 (95%CI: 0.924-0.969). The Delong test demonstrated that the XGBoost model had the highest AUC for predicting the ICT probability compared to other models (P < 0.05). Moreover, D-dimer, age, and atrial fibrillation contributed the most to the XGBoost model among these 11 variables. CONCLUSION: The XGBoost model has a good predictive ability in predicting ICT risk in patients with DCM and may assist clinicians in identifying ICT risk.
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Zhu et al. (2025) conducted an observational in Dilated cardiomyopathy (n=632). XGBoost prediction model vs. Logistic Regression model was evaluated on Area under the curve (AUC) for predicting intracardiac thrombus probability (AUC 0.947, 95% CI 0.924-0.969, p=<0.05). The XGBoost prediction model accurately predicted intracardiac thrombus risk in patients with dilated cardiomyopathy, achieving an AUC of 0.947 compared to 0.854 for logistic regression.
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