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Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy
Ze-Xin Fan1, Chao-Bin Wang2, Li-Bo Fang3
1Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
This study identified key risk factors for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM). A Bayesian network (BN) model proved superior to logistic regression for predicting IS in this population.
Area of Science:
- Cardiology
- Neurology
- Medical Informatics
Background:
- Dilated cardiomyopathy (DCM) is a significant risk factor for ischemic stroke (IS).
- Accurate prediction of IS in DCM patients is crucial for timely intervention and improved outcomes.
- Traditional risk assessment models may not fully capture the complex interplay of factors contributing to IS in DCM.
Purpose of the Study:
- To identify risk factors for IS in patients with DCM.
- To develop and validate a predictive model for IS using the Bayesian network (BN) approach.
- To compare the performance of the BN model against traditional logistic regression for IS prediction in DCM.
Main Methods:
- A cohort of 634 DCM patients was analyzed, with data collected between 2016-2021.
- Patients were randomly divided into training (70%) and testing (30%) sets.
- A BN model was constructed using the Tabu search algorithm and validated against a logistic regression model using AUC, accuracy, sensitivity, and specificity.
Main Results:
- Key predictors of IS identified included hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis.
- The BN model demonstrated superior or equivalent performance compared to logistic regression.
- The BN model achieved accuracies of 83.7% (training) and 85.5% (test), with AUCs of 0.763 and 0.822, respectively.
Conclusions:
- Hypertension, hyperlipidemia, atrial fibrillation/flutter, low eGFR, and intracardiac thrombosis are significant predictors of IS in DCM patients.
- The BN model offers a more suitable approach for early IS detection and diagnosis in DCM.
- This BN model can aid in preventing IS occurrence and recurrence within the DCM patient cohort.
Objective:
This study aimed to identify risk factors and create a predictive model for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM) using the Bayesian network (BN) approach.
Materials And Methods:
We collected clinical data of 634 patients with DCM treated at three referral management centers in Beijing between 2016 and 2021, including 127 with and 507 without IS. The patients were randomly divided into training (441 cases) and test (193 cases) sets at a ratio of 7:3. A BN model was established using the Tabu search algorithm with the training set data and verified with the test set data. The BN and logistic regression models were compared using the area under the receiver operating characteristic curve (AUC).
Results:
Multivariate logistic regression analysis showed that hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis were associated with IS. The BN model found that hyperlipidemia, atrial fibrillation (AF) or atrial flutter, eGFR, and intracardiac thrombosis were closely associated with IS. Compared to the logistic regression model, the BN model for IS performed better or equally well in the training and test sets, with respective accuracies of 83.7 and 85.5%, AUC of 0.763 [95% confidence interval (CI), 0.708-0.818] and 0.822 (95% CI, 0.748-0.896), sensitivities of 20.2 and 44.2%, and specificities of 98.3 and 97.3%.
Conclusion:
Hypertension, hyperlipidemia, AF or atrial flutter, low eGFR, and intracardiac thrombosis were good predictors of IS in patients with DCM. The BN model was superior to the traditional logistic regression model in predicting IS in patients with DCM and is, therefore, more suitable for early IS detection and diagnosis, and could help prevent the occurrence and recurrence of IS in this patient cohort.
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