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Risk factors assessment and a Bayesian network model for predicting ischemic stroke in patients with cardiac myxoma
Lin Ma1, Bin Cai1, Man-Li Qiao2
1Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
Bayesian networks identify cardiac symptoms, emboli, platelet count, and mobile tumors as key predictors of ischemic stroke in cardiac myxoma patients. This approach aids in early detection and prevention.
Area of Science:
- Cardiology
- Neurology
- Medical Informatics
Background:
- Cardiac myxoma (CM) poses a risk for ischemic stroke (IS).
- Predictive modeling is crucial for managing IS risk in CM patients.
- Bayesian networks (BN) offer a robust framework for analyzing complex medical data and identifying risk factors.
Purpose of the Study:
- To identify risk factors for IS in CM patients.
- To analyze interactions between IS-related variables.
- To develop a predictive model for IS using the BN approach.
Main Methods:
- Retrospective data collection from 416 CM patients (2002-2022).
- Bayesian network model construction using tabu search and maximum likelihood estimation.
- Comparison of BN model with logistic regression using ROC curves, AUC, sensitivity, and specificity.
Main Results:
- Cardiac symptoms, systemic embolic symptoms, platelet count, and tumor mobility were identified as direct IS predictors in CM.
- The BN model demonstrated superior or non-inferior performance compared to logistic regression (AUC: 0.706 vs. 0.697).
- BN achieved higher sensitivity (99.44% vs. 98.87%) but lower specificity (6.56% vs. 11.48%) and accuracy (85.82% vs. 86.06%).
Conclusions:
- Cardiac symptoms, embolic events, platelet count, and tumor characteristics are significant IS predictors in CM.
- The BN model shows potential for early IS detection, diagnosis, and prevention in clinical practice.
- BN offers a valuable alternative to traditional logistic regression for predicting IS in CM patients.
Objective:
This study aims to identify relevant risk factors, assess the interactions between variables, and establish a predictive model for ischemic stroke (IS) in patients with cardiac myxoma (CM) using the Bayesian network (BN) approach.
Methods:
Data of patients with CM were collected from three tertiary comprehensive hospitals in Beijing from January 2002 to January 2022. Age, sex, medical history, and information related to CM were extracted from the electronic medical record system. The BN model was constructed using the tabu search algorithm, and the conditional probability of each node was calculated using the maximum likelihood estimation method. The probability of each node of the network and the interrelationship between IS and its related factors were qualitatively and quantitatively analyzed. A receiver operating characteristic (ROC) curve was also plotted. Sensitivity, specificity, and area under the curve (AUC) values were calculated and compared between the BN and logistic regression models to evaluate the efficiency of the predictive model.
Results:
A total of 416 patients with CM were enrolled in this study, including 61 with and 355 without IS. The BN model found that cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility were directly associated with the occurrence of IS in patients with CM. The BN model for predicting CM-IS achieved higher scores on AUC {0.706 [95% confidence interval (CI), 0.639-0.773]} vs. [0.697 (95% CI, 0.629-0.766)] and sensitivity (99.44% vs. 98.87%), but lower scores on accuracies (85.82% vs. 86.06%) and specificity (6.56% vs. 11.48%) than the logistic regression model.
Conclusion:
Cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility are candidate predictors of IS in patients with CM. The BN model was superior or at least non-inferior to the traditional logistic regression model, and hence is potentially useful for early IS detection, diagnosis, and prevention in clinical practice.
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