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Published on: June 11, 2019
Construction and validation of a predictive model for intracardiac thrombus risk in patients with dilated
Xuetao Zhu1, Jun Li1, Yi Jiang1
1Department of Cardiology, The First Affiliated Hospital of Anhui Medical University, No. 218 Jixi Road, Hefei, Anhui, 230022, P.R. China.
Insights
This study developed a predictive model for intracardiac thrombus (ICT) in dilated cardiomyopathy (DCM) patients. The XGBoost model demonstrated high accuracy in identifying patients at risk of ICT, aiding clinical decision-making.
Area of Science:
- Cardiology
- Medical Informatics
- Predictive Analytics
Background:
- Systemic embolic events are a severe complication of dilated cardiomyopathy (DCM).
- Intracardiac thrombus (ICT) is a primary source of these embolic events.
- Accurate risk prediction for ICT in DCM patients is crucial for preventing catastrophic outcomes.
Purpose of the Study:
- To develop and validate a predictive model for identifying the risk of intracardiac thrombus (ICT) in patients with dilated cardiomyopathy (DCM).
- To compare the performance of different machine learning classifiers for ICT risk prediction.
Main Methods:
- Utilized data from 632 DCM patients, including clinical, vital sign, comorbidity, and biochemical data.
- Employed transthoracic echocardiography for ICT identification.
- Applied Least Absolute Shrinkage and Selection Operator (LASSO) regression for variable selection.
- Constructed and evaluated four models: Logistic Regression, Support Vector Machine (SVM), Random Forest, and eXtreme Gradient Boosting (XGBoost).
Main Results:
- Out of 632 patients, 13.92% had ICT.
- The XGBoost model achieved the highest Area Under the Curve (AUC) of 0.947 (95% CI: 0.924-0.969), outperforming other models.
- Key predictors identified by the XGBoost model included D-dimer levels, age, and atrial fibrillation.
Conclusions:
- The XGBoost model demonstrates strong predictive capability for ICT risk in DCM patients.
- This model can serve as a valuable tool for clinicians to identify high-risk individuals.
- Early identification of ICT risk can potentially mitigate embolic event complications.
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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