Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated cardiomyopathy: a retrospective study.

Abstract

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.