Prediction of persistent hemodynamic depression after carotid angioplasty and stenting using artificial neural
Jin Pyeong Jeon1, Chulho Kim2, Byoung-Doo Oh3
1Department of Neurosurgery, Hallym University College of Medicine, Chuncheon-si, Korea; Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon-si, Korea.
Insights
Artificial neural networks (ANN) demonstrated superior prediction of persistent hemodynamic depression (PHD) after carotid artery angioplasty and stenting (CAS) compared to other models. Further validation is recommended for these advanced predictive capabilities.
Area of Science:
- Cardiovascular Interventions
- Medical Artificial Intelligence
- Predictive Analytics in Medicine
Background:
- Persistent hemodynamic depression (PHD) is a potential complication following carotid artery angioplasty and stenting (CAS).
- Accurate prediction of PHD is crucial for patient management and improving outcomes after CAS procedures.
Purpose of the Study:
- To compare the predictive performance of artificial neural network (ANN) models against multiple logistic regression (MLR) and support vector machines (SVM) for PHD after CAS.
- To identify key predictive factors for PHD using advanced computational models.
Main Methods:
- A retrospective cohort of 76 patients undergoing CAS was used for training an ANN model.
- The ANN model was prospectively validated on a separate cohort of 33 patients.
- Performance was evaluated using accuracy and receiver operating characteristics (ROC) curve analysis, comparing ANN with MLR and SVM.
Main Results:
- The ANN model achieved high accuracy (98.7% training, 97.0% testing) and superior area under the ROC curve (AUROC) values (0.961 training, 0.950 testing).
- ANN significantly outperformed MLR (AUROC: 0.796) and SVM (AUROC: 0.885) in predicting PHD.
- MLR and SVM models showed lower accuracy rates (75.8%).
Conclusions:
- Artificial neural network models exhibit superior predictive power for persistent hemodynamic depression after CAS compared to traditional MLR and SVM models.
- The findings suggest ANN as a promising tool for risk stratification in patients undergoing CAS.
- External validation in a larger cohort is necessary to confirm these predictive capabilities.
Objectives:
To assess and compare predictive factors for persistent hemodynamic depression (PHD) after carotid artery angioplasty and stenting (CAS) using artificial neural network (ANN) and multiple logistic regression (MLR) or support vector machines (SVM) models.
Patients And Methods:
A retrospective data set of patients (n=76) who underwent CAS from 2007 to 2014 was used as input (training cohort) to a back-propagation ANN using TensorFlow platform. PHD was defined when systolic blood pressure was less than 90mmHg or heart rate was less 50 beats/min that lasted for more than one hour. The resulting ANN was prospectively tested in 33 patients (test cohort) and compared with MLR or SVM models according to accuracy and receiver operating characteristics (ROC) curve analysis.
Results:
No significant difference in baseline characteristics between the training cohort and the test cohort was observed. PHD was observed in 21 (27.6%) patients in the training cohort and 10 (30.3%) patients in the test cohort. In the training cohort, the accuracy of ANN for the prediction of PHD was 98.7% and the area under the ROC curve (AUROC) was 0.961. In the test cohort, the number of correctly classified instances was 32 (97.0%) using the ANN model. In contrast, the accuracy rate of MLR or SVM model was both 75.8%. ANN (AUROC: 0.950; 95% CI [confidence interval]: 0.813-0.996) showed superior predictive performance compared to MLR model (AUROC: 0.796; 95% CI: 0.620-0.915, p<0.001) or SVM model (AUROC: 0.885; 95% CI: 0.725-0.969, p<0.001).
Conclusions:
The ANN model seems to have more powerful prediction capabilities than MLR or SVM model for persistent hemodynamic depression after CAS. External validation with a large cohort is needed to confirm our results.
